Artificial Intelligence

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Recent submissions

Any replacements are listed farther down

[508] viXra:1806.0302 [pdf] submitted on 2018-06-21 07:02:32

New Artificial Neural Networks Method

Authors: George Rajna
Comments: 47 Pages.

An international team of scientists from Eindhoven University of Technology, University of Texas at Austin, and University of Derby, has developed a revolutionary method that quadratically accelerates artificial intelligence (AI) training algorithms. [27] Predictions for an AI-dominated future are increasingly common, but Antoine Blondeau has experience in reading, and arguably manipulating, the runes—he helped develop technology that evolved into predictive texting and Apple's Siri. [26] Artificial intelligence can improve health care by analyzing data from apps, smartphones and wearable technology. [25] Now, researchers at Google’s DeepMind have developed a simple algorithm to handle such reasoning—and it has already beaten humans at a complex image comprehension test. [24] A marimba-playing robot with four arms and eight sticks is writing and playing its own compositions in a lab at the Georgia Institute of Technology. The pieces are generated using artificial intelligence and deep learning. [23] Now, a team of researchers at MIT and elsewhere has developed a new approach to such computations, using light instead of electricity, which they say could vastly improve the speed and efficiency of certain deep learning computations. [22]
Category: Artificial Intelligence

[507] viXra:1806.0286 [pdf] submitted on 2018-06-21 03:50:18

An End-to-end Model of Predicting Diverse Ranking OnHeterogeneous Feeds

Authors: Zizhe Gao, Zheng Gao, Heng Huang, Zhuoren Jiang, Yuliang Yan
Comments: 6 Pages.

As an external assistance for online shopping, multimedia content (feed) plays an important role in e-Commerce eld. Feeds in formats of post, item list and video bring in richer auxiliary information and more authentic assessments of commodities (items). In Alibaba, the largest Chinese online retailer, besides traditional item search engine (ISE), a content search engine (CSE) is utilized for feeds recommendation as well. However, the diversity of feed types raises a challenge for the CSE to rank heterogeneous feeds. In this paper, a two-step end-to-end model including Heterogeneous Type Sorting and Homogeneous Feed Ranking is proposed to address this problem. In the first step, an independent Multi-Armed bandit (iMAB) model is proposed first, and an improved personalized Markov Deep Neural Network (pMDNN) model is developed later on. In the second step, an existing Deep Structured Semantic Model (DSSM) is utilized for homogeneous feed ranking. A/B test on Alibaba product environment shows that, by considering user preference and feed type dependency, pMDNN model significantly outperforms than iMAB model to solve heterogeneous feed ranking problem.
Category: Artificial Intelligence

[506] viXra:1806.0284 [pdf] submitted on 2018-06-21 05:08:23

Deep Learning Nuclear Events

Authors: George Rajna
Comments: 30 Pages.

A deep neural network running on an ordinary desktop computer is interpreting highly technical data related to national security as well as—and sometimes better than— today's best automated methods or even human experts. [19] Scientists at the National Center for Supercomputing Applications (NCSA), located at the University of Illinois at Urbana-Champaign, have pioneered the use of GPU-accelerated deep learning for rapid detection and characterization of gravitational waves. [18] Researchers from Queen Mary University of London have developed a mathematical model for the emergence of innovations. [17] Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning. [16] Neural networks learn how to carry out certain tasks by analyzing large amounts of data displayed to them. [15] Who is the better experimentalist, a human or a robot? When it comes to exploring synthetic and crystallization conditions for inorganic gigantic molecules, actively learning machines are clearly ahead, as demonstrated by British Scientists in an experiment with polyoxometalates published in the journal Angewandte Chemie. [14] Machine learning algorithms are designed to improve as they encounter more data, making them a versatile technology for understanding large sets of photos such as those accessible from Google Images. Elizabeth Holm, professor of materials science and engineering at Carnegie Mellon University, is leveraging this technology to better understand the enormous number of research images accumulated in the field of materials science. [13] With the help of artificial intelligence, chemists from the University of Basel in Switzerland have computed the characteristics of about two million crystals made up of four chemical elements. The researchers were able to identify 90 previously unknown thermodynamically stable crystals that can be regarded as new materials. [12] The artificial intelligence system's ability to set itself up quickly every morning and compensate for any overnight fluctuations would make this fragile technology much more useful for field measurements, said co-lead researcher Dr Michael Hush from UNSW ADFA. [11]
Category: Artificial Intelligence

[505] viXra:1806.0263 [pdf] submitted on 2018-06-15 21:10:42

Synthetic Human and Genius

Authors: Salvatore Gerard Micheal
Comments: 1 Page.

why just today i gave up on AI-SA, artificial intelligence and synthetic awareness
Category: Artificial Intelligence

[504] viXra:1806.0202 [pdf] submitted on 2018-06-14 08:13:55

AI Needs Hardware Accelerators

Authors: George Rajna
Comments: 44 Pages.

In a recent paper published in Nature, our IBM Research AI team demonstrated deep neural network (DNN) training with large arrays of analog memory devices at the same accuracy as a Graphical Processing Unit (GPU)-based system. [25] Physicists in the US have used machine learning to determine the phase diagram of a system of 12 idealized quantum particles to a higher precision than ever before. [24] The research group took advantage of a system at SLAC's Stanford Synchrotron Radiation Lightsource (SSRL) that combines machine learning—a form of artificial intelligence where computer algorithms glean knowledge from enormous amounts of data—with experiments that quickly make and screen hundreds of sample materials at a time. [23] Researchers at the UCLA Samueli School of Engineering have demonstrated that deep learning, a powerful form of artificial intelligence, can discern and enhance microscopic details in photos taken by smartphones. [22] Such are the big questions behind one of the new projects underway at the MIT-IBM Watson AI Laboratory, a collaboration for research on the frontiers of artificial intelligence. [21] The possibility of cognitive nuclear-spin processing came to Fisher in part through studies performed in the 1980s that reported a remarkable lithium isotope dependence on the behavior of mother rats. [20] And as will be presented today at the 25th annual meeting of the Cognitive Neuroscience Society (CNS), cognitive neuroscientists increasingly are using those emerging artificial networks to enhance their understanding of one of the most elusive intelligence systems, the human brain. [19] U.S. Army Research Laboratory scientists have discovered a way to leverage emerging brain-like computer architectures for an age-old number-theoretic problem known as integer factorization. [18] have come up with a novel machine learning method that enables scientists to derive insights from systems of previously intractable complexity in record time. [17]
Category: Artificial Intelligence

[503] viXra:1806.0161 [pdf] submitted on 2018-06-13 03:36:25

Machine Learning Quantum Phases

Authors: George Rajna
Comments: 42 Pages.

Physicists in the US have used machine learning to determine the phase diagram of a system of 12 idealized quantum particles to a higher precision than ever before. [24] The research group took advantage of a system at SLAC's Stanford Synchrotron Radiation Lightsource (SSRL) that combines machine learning—a form of artificial intelligence where computer algorithms glean knowledge from enormous amounts of data—with experiments that quickly make and screen hundreds of sample materials at a time. [23] Researchers at the UCLA Samueli School of Engineering have demonstrated that deep learning, a powerful form of artificial intelligence, can discern and enhance microscopic details in photos taken by smartphones. [22] Such are the big questions behind one of the new projects underway at the MIT-IBM Watson AI Laboratory, a collaboration for research on the frontiers of artificial intelligence. [21] The possibility of cognitive nuclear-spin processing came to Fisher in part through studies performed in the 1980s that reported a remarkable lithium isotope dependence on the behavior of mother rats. [20] And as will be presented today at the 25th annual meeting of the Cognitive Neuroscience Society (CNS), cognitive neuroscientists increasingly are using those emerging artificial networks to enhance their understanding of one of the most elusive intelligence systems, the human brain. [19] U.S. Army Research Laboratory scientists have discovered a way to leverage emerging brain-like computer architectures for an age-old number-theoretic problem known as integer factorization. [18] have come up with a novel machine learning method that enables scientists to derive insights from systems of previously intractable complexity in record time. [17] Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning. [16] Neural networks learn how to carry out certain tasks by analyzing large amounts of data displayed to them. [15]
Category: Artificial Intelligence

[502] viXra:1806.0134 [pdf] submitted on 2018-06-10 17:03:06

What I Would Ask a True ai When We Develop it

Authors: Salvatore Gerard Micheal
Comments: 5 Pages.

an essay about artificial intelligence, synthetic awareness, and why we need both
Category: Artificial Intelligence

[501] viXra:1806.0075 [pdf] submitted on 2018-06-06 12:28:50

Schur Group Theory Software Interfacing with Ruby Language in the Context of Ruby Based Machine Learning - An Interesting Insight into the Informatics World of Group Theory and its Nano-Bio Applications.

Authors: Nirmal Tej kumar
Comments: 3 Pages. Simple Technical Notes/Short Communication on SchurGroupTheory Software

We are very much inspired by “Lie Algebra” and its interesting applications in the realms of Science & Technology domains involving multi-disciplinary R&D these days in the context of nanotechnology. It is therefore inspiring to present a simple technical note involving the above mentioned TITLE for the READERS.Schur Group theory Software written in C language could be easily interfaced with Ruby language.Therefore,we could explore the many useful features of Ruby language in the context of Machine Learning/IoT/Cloud Applications etc.
Category: Artificial Intelligence

[500] viXra:1806.0072 [pdf] submitted on 2018-06-06 13:39:03

Artificial Intelligence Analyze Causation

Authors: George Rajna
Comments: 52 Pages.

Now, researchers have tested the first artificial intelligence model to identify and rank many causes in real-world problems without time-sequenced data, using a multi-nodal causal structure and Directed Acyclic Graphs. [29] A country that thinks its adversaries have or will get AI weapons will want to get them too. Wide use of AI-powered cyberattacks may still be some time away. [28] Following the old saying that "knowledge is power", companies are seeking to infer increasingly intimate properties about their customers as a way to gain an edge over their competitors. [27] Researchers from Human Longevity, Inc. (HLI) have published a study in which individual faces and other physical traits were predicted using whole genome sequencing data and machine learning. [26] Artificial intelligence can improve health care by analyzing data from apps, smartphones and wearable technology. [25] Now, researchers at Google's DeepMind have developed a simple algorithm to handle such reasoning—and it has already beaten humans at a complex image comprehension test. [24] A marimba-playing robot with four arms and eight sticks is writing and playing its own compositions in a lab at the Georgia Institute of Technology. The pieces are generated using artificial intelligence and deep learning. [23] Now, a team of researchers at MIT and elsewhere has developed a new approach to such computations, using light instead of electricity, which they say could vastly improve the speed and efficiency of certain deep learning computations. [22] Physicists have found that the structure of certain types of quantum learning algorithms is very similar to their classical counterparts—a finding that will help scientists further develop the quantum versions. [21] We should remain optimistic that quantum computing and AI will continue to improve our lives, but we also should continue to hold companies, organizations, and governments accountable for how our private data is used, as well as the technology's impact on the environment. [20] It's man vs machine this week as Google's artificial intelligence programme AlphaGo faces the world's top-ranked Go player in a contest expected to end in another victory for rapid advances in AI. [19]
Category: Artificial Intelligence

[499] viXra:1806.0044 [pdf] submitted on 2018-06-04 06:31:48

L’apprentissage Profond Sur Mimic-III :Prédiction de la Mortalité Sous 24 H

Authors: Ayoub ABRAICH
Comments: 97 Pages.

Ce projet décrit la fouille de données sur la base MIMIC-III . L’objectif est de prédire le décès à l’hôpital sur la base MIMIC III. On va suivre dans ce projet le processus Knowledge Discovery in Databases (KDD) qui est : 1. Sélection et extraction d’un ensemble de données de séries chronologiques multiva- riées à partir d’une base de données de rangées de millons en écrivant des requêtes SQL. 2. Prétraiter et nettoyer la série chronologique en un ensemble de données bien rangé en explorant les données, en gérant les données manquantes (taux de données man- quantes> 50%) et en supprimant le bruit / les valeurs aberrantes. 3. Développement d’un modèle prédictif permettant d’associer aux séries chronolo- giques biomédicales un indicateur de gravité ( probabilité de mortalité ) en mettant en œuvre plusieurs algorithmes tels que l’arbre de décision gradient boost et le k-NN (k-nearest neighbors) avec l’algorithme DTW (Dynamic time warping). 4. Résultat de 30% d’augmentation du score F1 (mesure de la précision d’un test) par rapport à l’indice de notation médical (SAPS II).
Category: Artificial Intelligence

[498] viXra:1806.0007 [pdf] submitted on 2018-06-02 04:37:40

Apple Cleared Path for App Update

Authors: George Rajna
Comments: 41 Pages.

A team of researchers including U of A engineering and physics faculty has developed a new method of detecting single photons, or light particles, using quantum dots. [27] Recent research from Kumamoto University in Japan has revealed that polyoxometalates (POMs), typically used for catalysis, electrochemistry, and photochemistry, may also be used in a technique for analyzing quantum dot (QD) photoluminescence (PL) emission mechanisms. [26] Researchers have designed a new type of laser called a quantum dot ring laser that emits red, orange, and green light. [25] The world of nanosensors may be physically small, but the demand is large and growing, with little sign of slowing. [24] In a joint research project, scientists from the Max Born Institute for Nonlinear Optics and Short Pulse Spectroscopy (MBI), the Technische Universität Berlin (TU) and the University of Rostock have managed for the first time to image free nanoparticles in a laboratory experiment using a highintensity laser source. [23] For the first time, researchers have built a nanolaser that uses only a single molecular layer, placed on a thin silicon beam, which operates at room temperature. [22] A team of engineers at Caltech has discovered how to use computer-chip manufacturing technologies to create the kind of reflective materials that make safety vests, running shoes, and road signs appear shiny in the dark. [21] In the September 23th issue of the Physical Review Letters, Prof. Julien Laurat and his team at Pierre and Marie Curie University in Paris (Laboratoire Kastler Brossel-LKB) report that they have realized an efficient mirror consisting of only 2000 atoms. [20] Physicists at MIT have now cooled a gas of potassium atoms to several nanokelvins—just a hair above absolute zero—and trapped the atoms within a two-dimensional sheet of an optical lattice created by crisscrossing lasers. Using a high-resolution microscope, the researchers took images of the cooled atoms residing in the lattice. [19]
Category: Artificial Intelligence

[497] viXra:1805.0546 [pdf] submitted on 2018-05-31 09:44:38

Deep Learning Hologram Reconstruction

Authors: George Rajna
Comments: 47 Pages.

Deep learning, which uses multi-layered artificial neural networks, is a form of machine learning that has demonstrated significant advances in many fields, including natural language processing, image/video labeling and captioning. [26]
Category: Artificial Intelligence

[496] viXra:1805.0545 [pdf] submitted on 2018-05-31 13:02:19

Face to Phase Recognition

Authors: George Rajna
Comments: 49 Pages.

Frenkel and his collaborators have now developed such a "phase-recognition" tool—or more precisely, a way to extract "hidden" signatures of an unknown structure from measurements made by existing tools. [27] Deep learning, which uses multi-layered artificial neural networks, is a form of machine learning that has demonstrated significant advances in many fields, including natural language processing, image/video labeling and captioning. [26]
Category: Artificial Intelligence

[495] viXra:1805.0539 [pdf] submitted on 2018-05-30 10:24:13

Machine Learning Accelerate Bioengineering

Authors: George Rajna
Comments: 45 Pages.

Scientists from the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) have developed a way to use machine learning to dramatically accelerate the design of microbes that produce biofuel. [25] AI combined with stem cells promises a faster approach to disease prevention. Andrew Masterson reports. According to product chief Trystan Upstill, the news app "uses the best of artificial intelligence to find the best of human intelligence—the great reporting done by journalists around the globe." [23] Artificial intelligence is astonishing in its potential. It will be more transformative than the PC and the Internet. Already it is poised to solve some of our biggest challenges. [22] In the search for extraterrestrial intelligence (SETI), we've often looked for signs of intelligence, technology and communication that are similar to our own. [21] Call it an a-MAZE-ing development: A U.K.-based team of researchers has developed an artificial intelligence program that can learn to take shortcuts through a labyrinth to reach its goal. In the process, the program developed structures akin to those in the human brain. [20] And as will be presented today at the 25th annual meeting of the Cognitive Neuroscience networks to enhance their understanding of one of the most elusive intelligence systems, the human brain. [19] U.S. Army Research Laboratory scientists have discovered a way to leverage emerging brain-like computer architectures for an age-old number-theoretic problem known as integer factorization. [18] Now researchers at the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) and UC Berkeley have come up with a novel machine learning method that enables scientists to derive insights from systems of previously intractable complexity in record time. [17] Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning. [16]
Category: Artificial Intelligence

[494] viXra:1805.0520 [pdf] submitted on 2018-05-30 03:30:54

An English-Hindi Code-Mixed Corpus: Stance Annotation and Baseline System

Authors: Sahil Swami, Ankush Khandelwal, Vinay Singh, Syed Sarfaraz Akhtar, Manish Shrivastava
Comments: 9 Pages. CICLing 2018

Social media has become one of the main channels for peo- ple to communicate and share their views with the society. We can often detect from these views whether the person is in favor, against or neu- tral towards a given topic. These opinions from social media are very useful for various companies. We present a new dataset that consists of 3545 English-Hindi code-mixed tweets with opinion towards Demoneti- sation that was implemented in India in 2016 which was followed by a large countrywide debate. We present a baseline supervised classification system for stance detection developed using the same dataset that uses various machine learning techniques to achieve an accuracy of 58.7% on 10-fold cross validation.
Category: Artificial Intelligence

[493] viXra:1805.0519 [pdf] submitted on 2018-05-30 03:34:17

A Corpus of English-Hindi Code-Mixed Tweets for Sarcasm Detection

Authors: Sahil Swami, Ankush Khandelwal, Vinay Singh, Syed Sarfaraz Akhtar, Manish Shrivastava
Comments: 9 Pages. CICLing 2018

Social media platforms like twitter and facebook have be- come two of the largest mediums used by people to express their views to- wards different topics. Generation of such large user data has made NLP tasks like sentiment analysis and opinion mining much more important. Using sarcasm in texts on social media has become a popular trend lately. Using sarcasm reverses the meaning and polarity of what is implied by the text which poses challenge for many NLP tasks. The task of sarcasm detection in text is gaining more and more importance for both commer- cial and security services. We present the first English-Hindi code-mixed dataset of tweets marked for presence of sarcasm and irony where each token is also annotated with a language tag. We present a baseline su- pervised classification system developed using the same dataset which achieves an average F-score of 78.4 after using random forest classifier and performing 10-fold cross validation.
Category: Artificial Intelligence

[492] viXra:1805.0509 [pdf] submitted on 2018-05-28 10:26:25

AI for Solar Cells

Authors: George Rajna
Comments: 47 Pages.

Solar cells will play a key role in shifting to a renewable economy. Organic photovoltaics (OPVs) are a promising class of solar cells, based on a light-absorbing organic molecule combined with a semiconducting polymer. [26] Today IBM Research is introducing IBM Crypto Anchor Verifier, a new technology that brings innovations in AI and optical imaging together to help prove the identity and authenticity of objects. [25] AI combined with stem cells promises a faster approach to disease prevention. Andrew Masterson reports. [24] According to product chief Trystan Upstill, the news app "uses the best of artificial intelligence to find the best of human intelligence—the great reporting done by journalists around the globe." [23] Artificial intelligence is astonishing in its potential. It will be more transformative than the PC and the Internet. Already it is poised to solve some of our biggest challenges. [22] In the search for extraterrestrial intelligence (SETI), we've often looked for signs of intelligence, technology and communication that are similar to our own. [21] Call it an aMAZE -ing development: A U.K.-based team of researchers has developed an artificial intelligence program that can learn to take shortcuts through a labyrinth to reach its goal. In the process, the program developed structures akin to those in the human brain. [20] And as will be presented today at the 25th annual meeting of the Cognitive Neuroscience networks to enhance their understanding of one of the most elusive intelligence systems, the human brain. [19] U.S. Army Research Laboratory scientists have discovered a way to leverage emerging brain-like computer architectures for an age-old number-theoretic problem known as integer factorization. [18] have come up with a novel machine learning method that enables scientists to derive insights from systems of previously intractable complexity in record time. [17]
Category: Artificial Intelligence

[491] viXra:1805.0504 [pdf] submitted on 2018-05-28 15:39:24

An Insight into the World of Hidden Markov Models Based on Higher Order Logic (HOL)/Scala/Haskell/JVM/IoT in the Context of NLP & Medical Image Processing Applications.

Authors: Nirmal Tej kumar
Comments: 3 Pages. Technical Notes on HMM/HOL/NLP to probe Medical Images

As explained in the TITLE mentioned above - it was proposed to design,develop,implement,test and probe the interesting aspects of Medical Imaging domains using HOL/NLP/HMM Concepts.
Category: Artificial Intelligence

[490] viXra:1805.0472 [pdf] submitted on 2018-05-26 09:08:15

AI Can't Solve Everything

Authors: George Rajna
Comments: 44 Pages.

While it is undeniable that AI has opened up a wealth of promising opportunities, it has also led to the emergence of a mindset that can be best described as "AI solutionism". [24] Intel's Gadi Singer believes his most important challenge is his latest: using artificial intelligence (AI) to reshape scientific exploration. [23] Artificial intelligence is astonishing in its potential. It will be more transformative than the PC and the Internet. Already it is poised to solve some of our biggest challenges. [22] In the search for extraterrestrial intelligence (SETI), we've often looked for signs of intelligence, technology and communication that are similar to our own. [21] Call it an aMAZE -ing development: A U.K.-based team of researchers has developed an artificial intelligence program that can learn to take shortcuts through a labyrinth to reach its goal. In the process, the program developed structures akin to those in the human brain. [20] And as will be presented today at the 25th annual meeting of the Cognitive Neuroscience networks to enhance their understanding of one of the most elusive intelligence systems, the human brain. [19] U.S. Army Research Laboratory scientists have discovered a way to leverage emerging brain-like computer architectures for an age-old number-theoretic problem known as integer factorization. [18] have come up with a novel machine learning method that enables scientists to derive insights from systems of previously intractable complexity in record time. [17] Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning. [16] Neural networks learn how to carry out certain tasks by analyzing large amounts of data displayed to them. [15] Who is the better experimentalist, a human or a robot? When it comes to exploring synthetic and crystallization conditions for inorganic gigantic molecules, actively learning machines are clearly ahead, as demonstrated by British Scientists in an experiment with polyoxometalates published in the journal Angewandte Chemie. [14]
Category: Artificial Intelligence

[489] viXra:1805.0459 [pdf] submitted on 2018-05-25 09:09:21

AI Changing Science

Authors: George Rajna
Comments: 42 Pages.

Intel's Gadi Singer believes his most important challenge is his latest: using artificial intelligence (AI) to reshape scientific exploration. [23] Artificial intelligence is astonishing in its potential. It will be more transformative than the PC and the Internet. Already it is poised to solve some of our biggest challenges. [22] In the search for extraterrestrial intelligence (SETI), we've often looked for signs of intelligence, technology and communication that are similar to our own. [21] Call it an aMAZE -ing development: A U.K.-based team of researchers has developed an artificial intelligence program that can learn to take shortcuts through a labyrinth to reach its goal. In the process, the program developed structures akin to those in the human brain. [20] And as will be presented today at the 25th annual meeting of the Cognitive Neuroscience networks to enhance their understanding of one of the most elusive intelligence systems, the human brain. [19] U.S. Army Research Laboratory scientists have discovered a way to leverage emerging brain-like computer architectures for an age-old number-theoretic problem known as integer factorization. [18] have come up with a novel machine learning method that enables scientists to derive insights from systems of previously intractable complexity in record time. [17] Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning. [16] Neural networks learn how to carry out certain tasks by analyzing large amounts of data displayed to them. [15] Who is the better experimentalist, a human or a robot? When it comes to exploring synthetic and crystallization conditions for inorganic gigantic molecules, actively learning machines are clearly ahead, as demonstrated by British Scientists in an experiment with polyoxometalates published in the journal Angewandte Chemie. [14] Machine learning algorithms are designed to improve as they encounter more data, making them a versatile technology for understanding large sets of photos such as those accessible from Google Images.
Category: Artificial Intelligence

[488] viXra:1805.0436 [pdf] submitted on 2018-05-23 12:57:08

AI with Optical Scanning

Authors: George Rajna
Comments: 45 Pages.

Today IBM Research is introducing IBM Crypto Anchor Verifier, a new technology that brings innovations in AI and optical imaging together to help prove the identity and authenticity of objects. [25] AI combined with stem cells promises a faster approach to disease prevention. Andrew Masterson reports. [24] According to product chief Trystan Upstill, the news app "uses the best of artificial intelligence to find the best of human intelligence—the great reporting done by journalists around the globe." [23] Artificial intelligence is astonishing in its potential. It will be more transformative than the PC and the Internet. Already it is poised to solve some of our biggest challenges. [22] In the search for extraterrestrial intelligence (SETI), we've often looked for signs of intelligence, technology and communication that are similar to our own. [21] Call it an a-MAZE-ing development: A U.K.-based team of researchers has developed an artificial intelligence program that can learn to take shortcuts through a labyrinth to reach its goal. In the process, the program developed structures akin to those in the human brain. [20]
Category: Artificial Intelligence

[487] viXra:1805.0380 [pdf] submitted on 2018-05-22 15:08:49

Bringing Deep Learning to IoT Devices Using Higher Order Logic(HOL)/Scala/Haskell/JVM as an Informatics Platform – A Novel Suggestion in the Context of Hardware/Software/Firmware Co-Design Approaches.

Authors: Nirmal Tej kumar
Comments: 3 Pages. Short Communication

As explained in the TITLE mentioned above,it is very much inspiring to probe the frontiers of IoT & its application domains in the context of science & technology using HOL/Scala/Haskell/JVM To the best of our knowledge,this is one of the pioneering efforts in this promising,challenging & inspiring aspects of DEEP LEARNING.
Category: Artificial Intelligence

[486] viXra:1805.0365 [pdf] submitted on 2018-05-21 05:36:58

AI Combined with Stem Cells

Authors: George Rajna
Comments: 42 Pages.

AI combined with stem cells promises a faster approach to disease prevention. Andrew Masterson reports. According to product chief Trystan Upstill, the news app "uses the best of artificial intelligence to find the best of human intelligence—the great reporting done by journalists around the globe." [23] Artificial intelligence is astonishing in its potential. It will be more transformative than the PC and the Internet. Already it is poised to solve some of our biggest challenges. [22] In the search for extraterrestrial intelligence (SETI), we've often looked for signs of intelligence, technology and communication that are similar to our own. [21] Call it an aMAZE -ing development: A U.K.-based team of researchers has developed an artificial intelligence program that can learn to take shortcuts through a labyrinth to reach its goal. In the process, the program developed structures akin to those in the human brain. [20] And as will be presented today at the 25th annual meeting of the Cognitive Neuroscience networks to enhance their understanding of one of the most elusive intelligence systems, the human brain. [19] U.S. Army Research Laboratory scientists have discovered a way to leverage emerging brain-like computer architectures for an age-old number-theoretic problem known as integer factorization. [18] have come up with a novel machine learning method that enables scientists to derive insights from systems of previously intractable complexity in record time. [17] Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning. [16] Neural networks learn how to carry out certain tasks by analyzing large amounts of data displayed to them. [15] Who is the better experimentalist, a human or a robot? When it comes to exploring synthetic and crystallization conditions for inorganic gigantic molecules, actively learning machines are clearly ahead, as demonstrated by British Scientists in an experiment with polyoxometalates published in the journal Angewandte Chemie. [14]
Category: Artificial Intelligence

[485] viXra:1805.0354 [pdf] submitted on 2018-05-20 06:35:52

Google Pushes Artificial Intelligence

Authors: George Rajna
Comments: 40 Pages.

According to product chief Trystan Upstill, the news app "uses the best of artificial intelligence to find the best of human intelligence—the great reporting done by journalists around the globe." [23] Artificial intelligence is astonishing in its potential. It will be more transformative than the PC and the Internet. Already it is poised to solve some of our biggest challenges. [22] In the search for extraterrestrial intelligence (SETI), we've often looked for signs of intelligence, technology and communication that are similar to our own. [21] Call it an aMAZE -ing development: A U.K.-based team of researchers has developed an artificial intelligence program that can learn to take shortcuts through a labyrinth to reach its goal. In the process, the program developed structures akin to those in the human brain. [20] And as will be presented today at the 25th annual meeting of the Cognitive Neuroscience networks to enhance their understanding of one of the most elusive intelligence systems, the human brain. [19] U.S. Army Research Laboratory scientists have discovered a way to leverage emerging brain-like computer architectures for an age-old number-theoretic problem known as integer factorization. [18] have come up with a novel machine learning method that enables scientists to derive insights from systems of previously intractable complexity in record time. [17] Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning. [16] Neural networks learn how to carry out certain tasks by analyzing large amounts of data displayed to them. [15] Who is the better experimentalist, a human or a robot? When it comes to exploring synthetic and crystallization conditions for inorganic gigantic molecules, actively learning machines are clearly ahead, as demonstrated by British Scientists in an experiment with polyoxometalates published in the journal Angewandte Chemie. [14]
Category: Artificial Intelligence

[484] viXra:1805.0311 [pdf] submitted on 2018-05-15 11:07:14

Modeling and Simulation of Servo Feed System of CNC Machine Tool Based on Matlab/simulink

Authors: Subom YUN, Onjoeng SIM
Comments: 5 Pages. fig 9, equation 5, reference 9

In the industry, CNC machine tools play an irreplaceable role. It not only realizes the rapid industrial production, but also saves manpower and material resources. It is the symbol of modernization. As an important part of CNC machine tools, feed system plays a very important role on the processing process; it refers to the product's quality problems. According to the principle of mechanical dynamics, I establish a mathematical model of machine tool feed drive system and use Simulink(dynamic simulation tool) in MATLAB to construct the simulation model of the feed system of lathe. We also designed the ANFIS-PID controller to cope with the mathematical model of the complex object and the model uncertainty that exists when there is external noise. These efforts offer effective foundation for the improvement of CNC machine tool.
Category: Artificial Intelligence

[483] viXra:1805.0295 [pdf] submitted on 2018-05-14 22:08:38

Modeling and Simulation of Feed System Design of CNC Machine Tool Based on Matlab/simulink

Authors: Yunsubom
Comments: 8page

In the industry, CNC machine tools plays an irreplaceable role. It not only realizes the rapid industrial production, but also saves manpower and material resources. It is the symbol of modernization. As an important part of CNC machine tools, feed system plays a very important role on the processing process; it refers to the product's quality problems. According to the principle of mechanical dynamics, I establish a mathematical model of machine tool feed drive system and use Simulink(dynamic simulation tool) in MATLAB to construct the simulation model of the feed system of lathe. We also designed the ANFIS-PID controller to cope with the mathematical model of the complex object and the model uncertainty that exists when there is external noise. These efforts offer effective foundation for the improvement of CNC machine tool.
Category: Artificial Intelligence

[482] viXra:1805.0279 [pdf] submitted on 2018-05-13 08:47:06

AI Find Alien Intelligence

Authors: George Rajna
Comments: 37 Pages.

In the search for extraterrestrial intelligence (SETI), we've often looked for signs of intelligence, technology and communication that are similar to our own. [21] Call it an aMAZE -ing development: A U.K.-based team of researchers has developed an artificial intelligence program that can learn to take shortcuts through a labyrinth to reach its goal. In the process, the program developed structures akin to those in the human brain. [20] And as will be presented today at the 25th annual meeting of the Cognitive Neuroscience networks to enhance their understanding of one of the most elusive intelligence systems, the human brain. [19] U.S. Army Research Laboratory scientists have discovered a way to leverage emerging brain-like computer architectures for an age-old number-theoretic problem known as integer factorization. [18] have come up with a novel machine learning method that enables scientists to derive insights from systems of previously intractable complexity in record time. [17] Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning. [16] Neural networks learn how to carry out certain tasks by analyzing large amounts of data displayed to them. [15] Who is the better experimentalist, a human or a robot? When it comes to exploring synthetic and crystallization conditions for inorganic gigantic molecules, actively learning machines are clearly ahead, as demonstrated by British Scientists in an experiment with polyoxometalates published in the journal Angewandte Chemie. [14] Machine learning algorithms are designed to improve as they encounter more data, making them a versatile technology for understanding large sets of photos such as those accessible from Google Images. Elizabeth Holm, professor of materials science and engineering at Carnegie Mellon University, is leveraging this technology to better understand the enormous number of research images accumulated in the field of materials science. [13]
Category: Artificial Intelligence

[481] viXra:1805.0277 [pdf] submitted on 2018-05-13 10:13:09

Strategy on Artificial Intelligence

Authors: George Rajna
Comments: 38 Pages.

Artificial intelligence is astonishing in its potential. It will be more transformative than the PC and the Internet. Already it is poised to solve some of our biggest challenges. [22] In the search for extraterrestrial intelligence (SETI), we've often looked for signs of intelligence, technology and communication that are similar to our own. [21] Call it an aMAZE -ing development: A U.K.-based team of researchers has developed an artificial intelligence program that can learn to take shortcuts through a labyrinth to reach its goal. In the process, the program developed structures akin to those in the human brain. [20] And as will be presented today at the 25th annual meeting of the Cognitive Neuroscience networks to enhance their understanding of one of the most elusive intelligence systems, the human brain. [19] U.S. Army Research Laboratory scientists have discovered a way to leverage emerging brain-like computer architectures for an age-old number-theoretic problem known as integer factorization. [18] have come up with a novel machine learning method that enables scientists to derive insights from systems of previously intractable complexity in record time. [17] Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning. [16] Neural networks learn how to carry out certain tasks by analyzing large amounts of data displayed to them. [15] Who is the better experimentalist, a human or a robot? When it comes to exploring synthetic and crystallization conditions for inorganic gigantic molecules, actively learning machines are clearly ahead, as demonstrated by British Scientists in an experiment with polyoxometalates published in the journal Angewandte Chemie. [14] Machine learning algorithms are designed to improve as they encounter more data, making them a versatile technology for understanding large sets of photos such as those accessible from Google Images. Elizabeth Holm, professor of materials science and engineering at Carnegie Mellon University, is leveraging this technology to better understand the enormous number of research images accumulated in the field of materials science. [13]
Category: Artificial Intelligence

[480] viXra:1805.0267 [pdf] submitted on 2018-05-13 15:24:24

An Improved Method of Generating Z-Number Based on Owa Weights and Maximum Entropy

Authors: Bingyi Kang
Comments: 24 Pages.

How to generate Z-number is an important and open issue in the uncertain information processing of Z-number. In [1], a method of generating Z-number using OWA weight and maximum entropy is investigated. However, the meaning of the method in [1] is not clear enough according to the definition of Z-number. Inspired by the methodology in [1], we improve the method of determining Z-number based on OWA weights and maximum entropy, which is more clear about the meaning of Z-number. Some numerical examples are used to illustrate the effectiveness of the proposed method.
Category: Artificial Intelligence

[479] viXra:1805.0240 [pdf] submitted on 2018-05-11 08:45:03

Probabilistic Computing for AI

Authors: George Rajna
Comments: 33 Pages.

Probabilistic computing will allow future systems to comprehend and compute with uncertainties inherent in natural data, which will enable us to build computers capable of understanding, predicting and decision-making. [20] For years, the people developing artificial intelligence drew inspiration from what was known about the human brain, and it has enjoyed a lot of success as a result. Now, AI is starting to return the favor. [19] Scientists at the National Center for Supercomputing Applications (NCSA), located at the University of Illinois at Urbana-Champaign, have pioneered the use of GPU-accelerated deep learning for rapid detection and characterization of gravitational waves. [18] Researchers from Queen Mary University of London have developed a mathematical model for the emergence of innovations. [17] Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning. [16] Neural networks learn how to carry out certain tasks by analyzing large amounts of data displayed to them. [15] Who is the better experimentalist, a human or a robot? When it comes to exploring synthetic and crystallization conditions for inorganic gigantic molecules, actively learning machines are clearly ahead, as demonstrated by British Scientists in an experiment with polyoxometalates published in the journal Angewandte Chemie. [14] Machine learning algorithms are designed to improve as they encounter more data, making them a versatile technology for understanding large sets of photos such as those accessible from Google Images. Elizabeth Holm, professor of materials science and engineering at Carnegie Mellon University, is leveraging this technology to better understand the enormous number of research images accumulated in the field of materials science. [13] With the help of artificial intelligence, chemists from the University of Basel in Switzerland have computed the characteristics of about two million crystals made up of four chemical elements. The researchers were able to identify 90 previously unknown thermodynamically stable crystals that can be regarded as new materials. [12] The artificial intelligence system's ability to set itself up quickly every morning and compensate for any overnight fluctuations would make this fragile technology much more useful for field measurements, said co-lead researcher Dr Michael Hush from UNSW ADFA. [11]
Category: Artificial Intelligence

[478] viXra:1805.0226 [pdf] submitted on 2018-05-12 00:44:53

A Memristor based Unsupervised Neuromorphic System Towards Fast and Energy-Efficient GAN

Authors: Fuqiang Liu, Chenchen Liu
Comments: 8 Pages.

Deep Learning has gained immense success in pushing today's artificial intelligence forward. To solve the challenge of limited labeled data in the supervised learning world, unsupervised learning has been proposed years ago while low accuracy hinters its realistic applications. Generative adversarial network (GAN) emerges as an unsupervised learning approach with promising accuracy and are under extensively study. However, the execution of GAN is extremely memory and computation intensive and results in ultra-low speed and high-power consumption. In this work, we proposed a holistic solution for fast and energy-efficient GAN computation through a memristor-based neuromorphic system. First, we exploited a hardware and software co-design approach to map the computation blocks in GAN efficiently. We also proposed an efficient data flow for optimal parallelism training and testing, depending on the computation correlations between different computing blocks. To compute the unique and complex loss of GAN, we developed a diff-block with optimized accuracy and performance. The experiment results on big data show that our design achieves 2.8x speedup and 6.1x energy-saving compared with the traditional GPU accelerator, as well as 5.5x speedup and 1.4x energy-saving compared with the previous FPGA-based accelerator.
Category: Artificial Intelligence

[477] viXra:1805.0222 [pdf] submitted on 2018-05-12 05:37:50

AI take Shortcuts

Authors: George Rajna
Comments: 32 Pages.

Call it an aMAZE -ing development: A U.K.-based team of researchers has developed an artificial intelligence program that can learn to take shortcuts through a labyrinth to reach its goal. In the process, the program developed structures akin to those in the human brain. [20] And as will be presented today at the 25th annual meeting of the Cognitive Neuroscience Society (CNS), cognitive neuroscientists increasingly are using those emerging artificial networks to enhance their understanding of one of the most elusive intelligence systems, the human brain. [19] U.S. Army Research Laboratory scientists have discovered a way to leverage emerging brain-like computer architectures for an age-old number-theoretic problem known as integer factorization. [18] have come up with a novel machine learning method that enables scientists to derive insights from systems of previously intractable complexity in record time. [17] Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning. [16] Neural networks learn how to carry out certain tasks by analyzing large amounts of data displayed to them. [15] Who is the better experimentalist, a human or a robot? When it comes to exploring synthetic and crystallization conditions for inorganic gigantic molecules, actively learning machines are clearly ahead, as demonstrated by British Scientists in an experiment with polyoxometalates published in the journal Angewandte Chemie. [14] Machine learning algorithms are designed to improve as they encounter more data, making them a versatile technology for understanding large sets of photos such as those accessible from Google Images. Elizabeth Holm, professor of materials science and engineering at Carnegie Mellon University, is leveraging this technology to better understand the enormous number of research images accumulated in the field of materials science. [13] With the help of artificial intelligence, chemists from the University of Basel in Switzerland have computed the characteristics of about two million crystals made up of four chemical elements. The researchers were able to identify 90 previously unknown thermodynamically stable crystals that can be regarded as new materials. [12]
Category: Artificial Intelligence

[476] viXra:1805.0214 [pdf] submitted on 2018-05-11 03:01:10

Ai Should not be an Open Source Project

Authors: Dimiter Dobrev
Comments: 10 Pages. Bulgarian language

Who should have the Artificial Intelligence technology? This technology should belong to everybody, but not the technology itself, but the fruits it will give us. Of course, we should not allow AI to fall into the hands of irresponsible people. Similarly, nuclear technology should benefit everyone, but these technologies must be kept in secret and should not be accessible to everyone.
Category: Artificial Intelligence

[475] viXra:1805.0195 [pdf] submitted on 2018-05-09 08:58:01

Collectives of Automata for Building of Active Systems of Artifical Intelligence

Authors: Aleksey A. Demidov
Comments: 37 Pages.

Basics of knowledge of AI in simple form
Category: Artificial Intelligence

[474] viXra:1805.0147 [pdf] submitted on 2018-05-07 09:36:53

Full Circle in Deep Learning

Authors: George Rajna
Comments: 30 Pages.

For years, the people developing artificial intelligence drew inspiration from what was known about the human brain, and it has enjoyed a lot of success as a result. Now, AI is starting to return the favor. [19] Scientists at the National Center for Supercomputing Applications (NCSA), located at the University of Illinois at Urbana-Champaign, have pioneered the use of GPU-accelerated deep learning for rapid detection and characterization of gravitational waves. [18] Researchers from Queen Mary University of London have developed a mathematical model for the emergence of innovations. [17] Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning. [16] Neural networks learn how to carry out certain tasks by analyzing large amounts of data displayed to them. [15] Who is the better experimentalist, a human or a robot? When it comes to exploring synthetic and crystallization conditions for inorganic gigantic molecules, actively learning machines are clearly ahead, as demonstrated by British Scientists in an experiment with polyoxometalates published in the journal Angewandte Chemie. [14]
Category: Artificial Intelligence

[473] viXra:1805.0089 [pdf] submitted on 2018-05-04 08:35:04

Group Sparse Recovery in Impulsive Noise Via Adm

Authors: Jianwen Huang, Feng Zhang, Jianjun Wang, Wendong Wang
Comments: 25 Pages.

In this paper, we consider the recovery of group sparse signals corrupted by impulsive noise. In some recent literature, researchers have utilized stable data fitting models, like $l_1$-norm, Huber penalty function and Lorentzian-norm, to substitute the $l_2$-norm data fidelity model to obtain more robust performance. In this paper, a stable model is developed, which exploits the generalized $l_p$-norm as the measure for the error for sparse reconstruction. In order to address this model, we propose an efficient alternative direction method, which includes the proximity operator of $l_p$-norm functions to the framework of Lagrangian methods. Besides, to guarantee the convergence of the algorithm in the case of $0\leq p<1$ (nonconvex case), we took advantage of a smoothing strategy. For both $0\leq p<1$ (nonconvex case) and $1\leq p\leq2$ (convex case), we have derived the conditions of the convergence for the proposed algorithm. Moreover, under the block restricted isometry property with constant $\delta_{\tau k_0}<\tau/(4-\tau)$ for $0<\tau<4/3$ and $\delta_{\tau k_0}<\sqrt{(\tau-1)/\tau}$ for $\tau\geq4/3$, a sharp sufficient condition for group sparse recovery in the presence of impulsive noise and its associated error upper bound estimation are established. Numerical results based on the synthetic block sparse signals and the real-world FECG signals demonstrate the effectiveness and robustness of new algorithm in highly impulsive noise.
Category: Artificial Intelligence

[472] viXra:1805.0053 [pdf] submitted on 2018-05-01 04:43:59

Magnetic Waves of Neuromorphic Computing

Authors: George Rajna
Comments: 41 Pages.

A team of physicists has uncovered properties of a category of magnetic waves relevant to the development of neuromorphic computing—an artificial intelligence system that seeks to mimic human-brain function. [24] The research group took advantage of a system at SLAC's Stanford Synchrotron Radiation Lightsource (SSRL) that combines machine learning—a form of artificial intelligence where computer algorithms glean knowledge from enormous amounts of data—with experiments that quickly make and screen hundreds of sample materials at a time. [23] Researchers at the UCLA Samueli School of Engineering have demonstrated that deep learning, a powerful form of artificial intelligence, can discern and enhance microscopic details in photos taken by smartphones. [22] Such are the big questions behind one of the new projects underway at the MIT-IBM Watson AI Laboratory, a collaboration for research on the frontiers of artificial intelligence. [21] The possibility of cognitive nuclear-spin processing came to Fisher in part through studies performed in the 1980s that reported a remarkable lithium isotope dependence on the behavior of mother rats. [20] And as will be presented today at the 25th annual meeting of the Cognitive Neuroscience Society (CNS), cognitive neuroscientists increasingly are using those emerging artificial networks to enhance their understanding of one of the most elusive intelligence systems, the human brain. [19] U.S. Army Research Laboratory scientists have discovered a way to leverage emerging brain-like computer architectures for an age-old number-theoretic problem known as integer factorization. [18] have come up with a novel machine learning method that enables scientists to derive insights from systems of previously intractable complexity in record time. [17] Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning. [16]
Category: Artificial Intelligence

[471] viXra:1805.0044 [pdf] submitted on 2018-05-01 13:09:45

AI Spots Gravitational Waves

Authors: George Rajna
Comments: 23 Pages.

A deep-learning system that can sift gravitational wave signals from background noise has been created by physicists in the UK. [8] Using data from the first-ever gravitational waves detected last year, along with a theoretical analysis, physicists have shown that gravitational waves may oscillate between two different forms called "g" and "f"-type gravitational waves. [7] Astronomy experiments could soon test an idea developed by Albert Einstein almost exactly a century ago, scientists say. [6] It's estimated that 27% of all the matter in the universe is invisible, while everything from PB&J sandwiches to quasars accounts for just 4.9%. But a new theory of gravity proposed by theoretical physicist Erik Verlinde of the University of Amsterdam found out a way to dispense with the pesky stuff. [5] The proposal by the trio though phrased in a way as to suggest it's a solution to the arrow of time problem, is not likely to be addressed as such by the physics community— it's more likely to be considered as yet another theory that works mathematically, yet still can't answer the basic question of what is time. [4] The Weak Interaction transforms an electric charge in the diffraction pattern from one side to the other side, causing an electric dipole momentum change, which violates the CP and Time reversal symmetry. The Neutrino Oscillation of the Weak Interaction shows that it is a General electric dipole change and it is possible to any other temperature dependent entropy and information changing diffraction pattern of atoms, molecules and even complicated biological living structures.
Category: Artificial Intelligence

[470] viXra:1804.0334 [pdf] submitted on 2018-04-24 04:00:45

Introduction of Reflex Based Neural Network

Authors: Liang Yi
Comments: 17 Pages.

This paper introduces a new neural network that works quite different from current neural network model. The RBNN model is based on the concept of conditioned reflex, which widely exists in real creatures. In RBNN, all learning procedure are executed by the neural network itself, which makes it not a complex mathematic model but simple enough to be implemented in real brain. In RBNN, information is organized in a clear way, which makes the whole network a white box rather than a black box, so we can teach the network knowledge easily and fast. This paper shows the power of conditioned reflex as a search tool which can be used as states transfer function in state machine. Using combinations of neurons as symbols which plays the role of letters in traditional state machine, a RBNN can be treated as a state machine with small number of memory unit but huge number of letters.
Category: Artificial Intelligence

[469] viXra:1804.0281 [pdf] submitted on 2018-04-19 09:47:02

Perceptual Significance of Kernel Methods for Natural Image Processing

Authors: Vikas Ramachandra, Truong Nguyen
Comments: 6 Pages.

We explore the unifying connection between kernel regression, Volterra series expansion and multiscale signal decomposition using recent results on function estimation for system identification. We show that using any of these techniques for (non-linear) image processing tasks is (approximately) equivalent. Further, we use the relation between wavelets and independent components of natural images. Kernel methods can be shown to be implicit Volterra series expansions, which are well approximated by wavelets. Wavelets are, in turn, well represented by independent components of natural images. Thus, it can be seen that kernel methods are also near optimal in terms of higher order statistical modeling and approximation of (natural) images. This explains the reason for good results often (perceptually) observed with the use of kernel methods for many image processing problems.
Category: Artificial Intelligence

[468] viXra:1804.0280 [pdf] submitted on 2018-04-19 09:53:13

Superresolution Using Perceptually Significant Side Information

Authors: Vikas Ramachandra, Truong Nguyen
Comments: 4 Pages.

We investigate the problem of super-resolution of images in the presence of side information. In some situations, when some information of the original image is available to the sender, it can be embedded into the low resolution images, either in the pixels themselves or in the headers. This information can be later used when required to reconstruct the superresolved image. For this, a novel multiresolution histogram matching based superresolution procedure is outlined. The proposed technique gives better results compared to contemporary resolution enhancement algorithms, and is especially useful for de-blurring text images captured from mobile phone cameras.
Category: Artificial Intelligence

[467] viXra:1804.0278 [pdf] submitted on 2018-04-19 09:59:42

A Distributed Compressive Sampling Approach for Scene Capture Using an Array of Single Pixel Cameras

Authors: Vikas Ramachandra
Comments: 4 Pages.

This paper presents a method of capturing 3D scene information using an array of single pixel cameras. Based on the recent results for distributed compressive sampling, it is shown here that there could be considerable savings in the measurements required to construct the whole scene, when the correlations between the images captured by the individual cameras in the array is exploited. A technique for doing so for an array of cameras separated by translations along one axis only is illustrated.
Category: Artificial Intelligence

[466] viXra:1804.0249 [pdf] submitted on 2018-04-18 03:52:47

Machine Learning Protein Dynamics Data

Authors: George Rajna
Comments: 32 Pages.

At the University of South Florida, researchers are integrating machine learning techniques into their work studying proteins. [21] Bioinformatics professors Anthony Gitter and Casey Greene set out in summer 2016 to write a paper about biomedical applications for deep learning, a hot new artificial intelligence field striving to mimic the neural networks of the human brain. [20] A team of researchers from the University of Muenster in Germany has now demonstrated that this combination is extremely well suited to planning chemical syntheses—so-called retrosyntheses—with unprecedented efficiency. [19] Two physicists at ETH Zurich and the Hebrew University of Jerusalem have developed a novel machine-learning algorithm that analyses large data sets describing a physical system and extract from them the essential information needed to understand the underlying physics. [18] have come up with a novel machine learning method that enables scientists to derive insights from systems of previously intractable complexity in record time. [17] Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning. [16] Neural networks learn how to carry out certain tasks by analyzing large amounts of data displayed to them. [15] Who is the better experimentalist, a human or a robot? When it comes to exploring synthetic and crystallization conditions for inorganic gigantic molecules, actively learning machines are clearly ahead, as demonstrated by British Scientists in an experiment with polyoxometalates published in the journal Angewandte Chemie. [14] Machine learning algorithms are designed to improve as they encounter more data, making them a versatile technology for understanding large sets of photos such as those accessible from Google Images. Elizabeth Holm, professor of materials science and engineering at Carnegie Mellon University, is leveraging this technology to better understand the enormous number of research images accumulated in the field of materials science. [13]
Category: Artificial Intelligence

[465] viXra:1804.0237 [pdf] submitted on 2018-04-18 11:04:11

Beyond Back Propagation

Authors: Tofara Moyo
Comments: 1 Page.

5 Protea Lane Newton west
Category: Artificial Intelligence

[464] viXra:1804.0197 [pdf] submitted on 2018-04-14 08:00:32

Artificial Intelligence Accelerates Discovery

Authors: George Rajna
Comments: 40 Pages.

The research group took advantage of a system at SLAC's Stanford Synchrotron Radiation Lightsource (SSRL) that combines machine learning—a form of artificial intelligence where computer algorithms glean knowledge from enormous amounts of data—with experiments that quickly make and screen hundreds of sample materials at a time. [23] Researchers at the UCLA Samueli School of Engineering have demonstrated that deep learning, a powerful form of artificial intelligence, can discern and enhance microscopic details in photos taken by smartphones. [22] Such are the big questions behind one of the new projects underway at the MIT-IBM Watson AI Laboratory, a collaboration for research on the frontiers of artificial intelligence. [21] The possibility of cognitive nuclear-spin processing came to Fisher in part through studies performed in the 1980s that reported a remarkable lithium isotope dependence on the behavior of mother rats. [20] And as will be presented today at the 25th annual meeting of the Cognitive Neuroscience Society (CNS), cognitive neuroscientists increasingly are using those emerging artificial networks to enhance their understanding of one of the most elusive intelligence systems, the human brain. [19] U.S. Army Research Laboratory scientists have discovered a way to leverage emerging brain-like computer architectures for an age-old number-theoretic problem known as integer factorization. [18] have come up with a novel machine learning method that enables scientists to derive insights from systems of previously intractable complexity in record time. [17] Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning. [16] Neural networks learn how to carry out certain tasks by analyzing large amounts of data displayed to them. [15]
Category: Artificial Intelligence

[463] viXra:1804.0155 [pdf] submitted on 2018-04-11 07:17:37

Deep Learning Smartphone Microscope

Authors: George Rajna
Comments: 36 Pages.

Researchers at the UCLA Samueli School of Engineering have demonstrated that deep learning, a powerful form of artificial intelligence, can discern and enhance microscopic details in photos taken by smartphones. [22] Such are the big questions behind one of the new projects underway at the MIT-IBM Watson AI Laboratory, a collaboration for research on the frontiers of artificial intelligence. [21] The possibility of cognitive nuclear-spin processing came to Fisher in part through studies performed in the 1980s that reported a remarkable lithium isotope dependence on the behavior of mother rats. [20] And as will be presented today at the 25th annual meeting of the Cognitive Neuroscience Society (CNS), cognitive neuroscientists increasingly are using those emerging artificial networks to enhance their understanding of one of the most elusive intelligence systems, the human brain. [19] U.S. Army Research Laboratory scientists have discovered a way to leverage emerging brain-like computer architectures for an age-old number-theoretic problem known as integer factorization. [18] have come up with a novel machine learning method that enables scientists to derive insights from systems of previously intractable complexity in record time. [17] Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning. [16] Neural networks learn how to carry out certain tasks by analyzing large amounts of data displayed to them. [15] Who is the better experimentalist, a human or a robot? When it comes to exploring synthetic and crystallization conditions for inorganic gigantic molecules, actively learning machines are clearly ahead, as demonstrated by British Scientists in an experiment with polyoxometalates published in the journal Angewandte Chemie. [14] Machine learning algorithms are designed to improve as they encounter more data, making them a versatile technology for understanding large sets of photos such as those accessible from Google Images.
Category: Artificial Intelligence

[462] viXra:1804.0149 [pdf] submitted on 2018-04-09 11:59:00

Machine Learning for Gravitational Waves

Authors: George Rajna
Comments: 21 Pages.

A trio of students from the University of Glasgow have developed a sophisticated artificial intelligence which could underpin the next phase of gravitational wave astronomy. [8] Using data from the first-ever gravitational waves detected last year, along with a theoretical analysis, physicists have shown that gravitational waves may oscillate between two different forms called "g" and "f"-type gravitational waves. [7] Astronomy experiments could soon test an idea developed by Albert Einstein almost exactly a century ago, scientists say. [6] It's estimated that 27% of all the matter in the universe is invisible, while everything from PB&J sandwiches to quasars accounts for just 4.9%. But a new theory of gravity proposed by theoretical physicist Erik Verlinde of the University of Amsterdam found out a way to dispense with the pesky stuff. [5] The proposal by the trio though phrased in a way as to suggest it's a solution to the arrow of time problem, is not likely to be addressed as such by the physics community— it's more likely to be considered as yet another theory that works mathematically, yet still can't answer the basic question of what is time. [4] The Weak Interaction transforms an electric charge in the diffraction pattern from one side to the other side, causing an electric dipole momentum change, which violates the CP and Time reversal symmetry. The Neutrino Oscillation of the Weak Interaction shows that it is a General electric dipole change and it is possible to any other temperature dependent entropy and information changing diffraction pattern of atoms, molecules and even complicated biological living structures.
Category: Artificial Intelligence

[461] viXra:1804.0114 [pdf] submitted on 2018-04-07 10:24:50

Rethinking BICA’s R&D Challenges: Grief Revelations of an Upset Revisionist

Authors: Emanuel Diamant
Comments: 6 Pages.

Biologically Inspired Cognitive Architectures (BICA) is a subfield of Artificial Intelligence aimed at creating machines that emulate human cognitive abilities. What distinguish BICA from other AI approaches is that it based on principles drawn from biology and neuroscience. There is a widespread conviction that nature has a solution for almost all problems we are faced with today. We have only to pick up the solution and replicate it in our design. However, Nature does not easily give up her secrets. Especially, when it is about human brain deciphering. For that reason, large Brain Research Initiatives have been launched around the world. They will provide us with knowledge about brain workflow activity in neuron assemblies and their interconnections. But what is being “flown” (conveyed) via the interconnections the research programme does not disclose. It is implied that what flows in the interconnections is information. But what is information? – that remains undefined. Having in mind BICA’s interest in the matters, the paper will try to clarify the issues.
Category: Artificial Intelligence

[460] viXra:1804.0113 [pdf] submitted on 2018-04-07 10:28:34

Artificial Neural Networks: a Bio-Inspired Revolution or a Long Lasting Misconception and Self-Delusion

Authors: Emanuel Diamant
Comments: 7 Pages. Rejected by the IJCNN 2018, Rio de Janeiro, July 08-13, 2018.

Ali Rahimi, best paper award recipient at NIPS 2017, labelled the current state of Deep Learning (DL) headway as “alchemy”. Yann LeCun, one of the prominent figures in the DL R&D, was insulted by this expression. However, in his response, LeCun did not claimed that DL designers know how and why their DL systems reach so surprising performances. The possible reason for this cautiousness is: No one knows how and in which way system input data is transformed into semantic information at the system’s output. And this, certainly, has its own reason: No one knows what information is! I dare to offer my humble clarification about this obscure and usually untouchable matter. I hope someone would be ready to line up with me.
Category: Artificial Intelligence

[459] viXra:1804.0112 [pdf] submitted on 2018-04-07 11:12:53

Recurrent Capsule Network for Image Generation

Authors: Srikumar Sastry
Comments: 9 Pages.

We have already seen state-of-the-art image generation techniques with Generative Adversarial Networks (Goodfellow et al. 2014), Variational Autoencoder and Recurrent Network for Image generation (K. Gregor et al. 2015). But all these architectures fail to learn object location and pose in images. In this paper, I propose Recurrent Capsule Network based on variational auto encoding framework which can not only preserve equivariance in images in the latent space but also can be used for image classification and generation. For image classification, it can recognise highly overlapping objects due to the use of capsules (Hinton et al. 2011), considerably better than convolutional networks. It can generate images which can be difficult to differentiate from the real data.
Category: Artificial Intelligence

[458] viXra:1804.0094 [pdf] submitted on 2018-04-06 04:33:26

Automated Classification of Hand-Grip Action on Objects Using Machine Learning

Authors: Anju Mishra, Amity University Uttar Pradesh SHANU SHARMA, Amity University Uttar Pradesh SANJAY KUMAR, Oxford Brooks University PRIYA RANJAN, Amity University Uttar Pradesh AMIT UJLAYAN, Gautam Buddha University
Comments: 10 Pages. This is a preprint of a paper under possible publication consideration.

Brain computer interface is the current area of research to provide assistance to disabled persons. To cope up with the growing needs of BCI applications, this paper presents an automated classification scheme for handgrip actions on objects by using Electroencephalography (EEG) data. The presented approach focuses on investigation of classifying correct and incorrect handgrip responses for objects by using EEG recorded patterns. The method starts with preprocessing of data, followed by extraction of relevant features from the epoch data in the form of discrete wavelet transform (DWT), and entropy measures. After computing feature vectors, artificial neural network classifiers used to classify the patterns into correct and incorrect handgrips on different objects. The proposed method was tested on real dataset, which contains EEG recordings from 14 persons. The results showed that the proposed approach is effective and may be useful to develop a variety of BCI based devices to control hand movements. KEYWORDS EEG, Brain computer interface, Machine learning, Hand action recognition
Category: Artificial Intelligence

[457] viXra:1804.0074 [pdf] submitted on 2018-04-04 07:28:11

Biomedical Applications for Deep Learning

Authors: George Rajna
Comments: 30 Pages.

Bioinformatics professors Anthony Gitter and Casey Greene set out in summer 2016 to write a paper about biomedical applications for deep learning, a hot new artificial intelligence field striving to mimic the neural networks of the human brain. [20] A team of researchers from the University of Muenster in Germany has now demonstrated that this combination is extremely well suited to planning chemical syntheses—so-called retrosyntheses—with unprecedented efficiency. [19] Two physicists at ETH Zurich and the Hebrew University of Jerusalem have developed a novel machine-learning algorithm that analyses large data sets describing a physical system and extract from them the essential information needed to understand the underlying physics. [18] Now researchers at the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) and UC Berkeley have come up with a novel machine learning method that enables scientists to derive insights from systems of previously intractable complexity in record time. [17] Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning. [16] Neural networks learn how to carry out certain tasks by analyzing large amounts of data displayed to them. [15] Who is the better experimentalist, a human or a robot? When it comes to exploring synthetic and crystallization conditions for inorganic gigantic molecules, actively learning machines are clearly ahead, as demonstrated by British Scientists in an experiment with polyoxometalates published in the journal Angewandte Chemie. [14]
Category: Artificial Intelligence

[456] viXra:1804.0056 [pdf] submitted on 2018-04-05 08:26:14

Computer Recognize Dynamic Events

Authors: George Rajna
Comments: 35 Pages.

Such are the big questions behind one of the new projects underway at the MIT-IBM Watson AI Laboratory, a collaboration for research on the frontiers of artificial intelligence. [21] The possibility of cognitive nuclear-spin processing came to Fisher in part through studies performed in the 1980s that reported a remarkable lithium isotope dependence on the behavior of mother rats. [20] And as will be presented today at the 25th annual meeting of the Cognitive Neuroscience Society (CNS), cognitive neuroscientists increasingly are using those emerging artificial networks to enhance their understanding of one of the most elusive intelligence systems, the human brain. [19] U.S. Army Research Laboratory scientists have discovered a way to leverage emerging brain-like computer architectures for an age-old number-theoretic problem known as integer factorization. [18] have come up with a novel machine learning method that enables scientists to derive insights from systems of previously intractable complexity in record time. [17] Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning. [16] Neural networks learn how to carry out certain tasks by analyzing large amounts of data displayed to them. [15] Who is the better experimentalist, a human or a robot? When it comes to exploring synthetic and crystallization conditions for inorganic gigantic molecules, actively learning machines are clearly ahead, as demonstrated by British Scientists in an experiment with polyoxometalates published in the journal Angewandte Chemie. [14] Machine learning algorithms are designed to improve as they encounter more data, making them a versatile technology for understanding large sets of photos such as those accessible from Google Images. Elizabeth Holm, professor of materials science and engineering at Carnegie Mellon University, is leveraging this technology to better understand the enormous number of research images accumulated in the field of materials science. [13]
Category: Artificial Intelligence

[455] viXra:1804.0048 [pdf] submitted on 2018-04-03 10:59:48

Machine Learning to Microbial Relationship

Authors: George Rajna
Comments: 30 Pages.

Marculescu, along with ECE Ph.D. student Chieh Lo, has developed a machine learning algorithm—called MPLasso—that uses data to infer associations and interactions between microbes in the GI microbiome. [20] A team of researchers from the University of Muenster in Germany has now demonstrated that this combination is extremely well suited to planning chemical syntheses—so-called retrosyntheses—with unprecedented efficiency. [19] Two physicists at ETH Zurich and the Hebrew University of Jerusalem have developed a novel machine-learning algorithm that analyses large data sets describing a physical system and extract from them the essential information needed to understand the underlying physics. [18] Now researchers at the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) and UC Berkeley have come up with a novel machine learning method that enables scientists to derive insights from systems of previously intractable complexity in record time. [17] Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning. [16] Neural networks learn how to carry out certain tasks by analyzing large amounts of data displayed to them. [15]
Category: Artificial Intelligence

[454] viXra:1804.0020 [pdf] submitted on 2018-04-02 03:54:11

A New Neural Network for Artificial General Intelligence

Authors: Haisong Liang
Comments: 44 Pages. Include both English and Chinese version.

Since artificial intelligence was first introduced several decades ago, neural network has achieved remarkable results as one of the most important research methods, and variety of neural network models have been proposed. Usually, for a specific task, we train the network with large amounts of data to develop a mathematical model, making the model produce the expected outputs according to inputs, which also results in the black box problem. In this case, if we study from the perspective of information meanings and their causal relations with the following measures: denote information by neurons; store their relations with links; give neurons a state indicating the strength of information, which can be updated by a state function or input signal; then we can store different information and their relations and control related information's expression with neurons' state. The neural network will become a dynamic system then. More importantly, we can denote different information and logic by designing the topology of neural network and the attributes of the links, and thus having the ability to design and explain every detail of the network precisely, turning neural network into a general information storage, expression, control and processing system, which is also commonly referred as "Strong AI".
Category: Artificial Intelligence

[453] viXra:1803.0751 [pdf] submitted on 2018-03-31 04:17:45

Galenism: A Methodology for the Key Unification of Von Neumann Machines and Hierarchical Databases

Authors: Pallabi Chakraborty, Bhargav Bhushanam
Comments: 7 Pages.

The implications of psychoacoustic methodologies have been far-reaching and pervasive. In this work, we disprove the simulation of active networks. We examine how fiber-optic cables can be applied to the evaluation of DNS.
Category: Artificial Intelligence

[452] viXra:1803.0728 [pdf] submitted on 2018-03-30 06:52:39

Artificial Intelligence in Chemical Synthesis

Authors: George Rajna
Comments: 29 Pages.

A team of researchers from the University of Muenster in Germany has now demonstrated that this combination is extremely well suited to planning chemical syntheses—so-called retrosyntheses—with unprecedented efficiency. [19] Two physicists at ETH Zurich and the Hebrew University of Jerusalem have developed a novel machine-learning algorithm that analyses large data sets describing a physical system and extract from them the essential information needed to understand the underlying physics. [18] Now researchers at the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) and UC Berkeley have come up with a novel machine learning method that enables scientists to derive insights from systems of previously intractable complexity in record time. [17] Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning. [16] Neural networks learn how to carry out certain tasks by analyzing large amounts of data displayed to them. [15]
Category: Artificial Intelligence

[451] viXra:1803.0699 [pdf] submitted on 2018-03-29 01:48:31

Universal Forecasting Scheme {Version 4}

Authors: Ramesh Chandra Bagadi
Comments: 3 Pages.

In this research investigation, the author has presented a Novel Method of Forecasting.
Category: Artificial Intelligence

[450] viXra:1803.0696 [pdf] submitted on 2018-03-29 06:19:03

Teaching Machine in Physical Systems

Authors: George Rajna
Comments: 27 Pages.

Two physicists at ETH Zurich and the Hebrew University of Jerusalem have developed a novel machine-learning algorithm that analyses large data sets describing a physical system and extract from them the essential information needed to understand the underlying physics. [18] Now researchers at the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) and UC Berkeley have come up with a novel machine learning method that enables scientists to derive insights from systems of previously intractable complexity in record time. [17] Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning. [16] Neural networks learn how to carry out certain tasks by analyzing large amounts of data displayed to them. [15] Who is the better experimentalist, a human or a robot? When it comes to exploring synthetic and crystallization conditions for inorganic gigantic molecules, actively learning machines are clearly ahead, as demonstrated by British Scientists in an experiment with polyoxometalates published in the journal Angewandte Chemie. [14] Machine learning algorithms are designed to improve as they encounter more data, making them a versatile technology for understanding large sets of photos such as those accessible from Google Images. Elizabeth Holm, professor of materials science and engineering at Carnegie Mellon University, is leveraging this technology to better understand the enormous number of research images accumulated in the field of materials science. [13] With the help of artificial intelligence, chemists from the University of Basel in Switzerland have computed the characteristics of about two million crystals made up of four chemical elements. The researchers were able to identify 90 previously unknown thermodynamically stable crystals that can be regarded as new materials. [12]
Category: Artificial Intelligence

[449] viXra:1803.0695 [pdf] submitted on 2018-03-28 04:26:14

Brain's Potential for Quantum Computation

Authors: George Rajna
Comments: 32 Pages.

The possibility of cognitive nuclear-spin processing came to Fisher in part through studies performed in the 1980s that reported a remarkable lithium isotope dependence on the behavior of mother rats. [20] And as will be presented today at the 25th annual meeting of the Cognitive Neuroscience Society (CNS), cognitive neuroscientists increasingly are using those emerging artificial networks to enhance their understanding of one of the most elusive intelligence systems, the human brain. [19] U.S. Army Research Laboratory scientists have discovered a way to leverage emerging brain-like computer architectures for an age-old number-theoretic problem known as integer factorization. [18] Now researchers at the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) and UC Berkeley have come up with a novel machine learning method that enables scientists to derive insights from systems of previously intractable complexity in record time. [17] Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning. [16] Neural networks learn how to carry out certain tasks by analyzing large amounts of data displayed to them. [15]
Category: Artificial Intelligence

[448] viXra:1803.0675 [pdf] submitted on 2018-03-26 06:58:13

A Survey on Reasoning on Building Information Models Based on IFC

Authors: Hassan Sleiman
Comments: 17 Pages.

Building Information Models (BIM) are computer models that act as a main source of building information and integrate several aspects of engineering and architectural design, including building utilisation. They aim at enhancing the efficiency and the effectiveness of the projects during design, construction, and maintenance. Artificial Intelligence, which is used to automate tasks that would require intelligence, has found its way into BIM by applying reasoners, among other techniques. A reasoner is a piece of software that makes the implicit and hidden knowledge as explicit by using logical inferring techniques. Reasoners are applied on BIM to help take enhanced decisions and to assess the construction projects. The importance of BIM in both construction and information technology sectors has motivated many researchers to work on surveys that attempt to provide the current state of BIM, but unfortunately, none of these surveys has focused on reasoning on BIM. In this article we survey the research proposals and toolkits that rely on using reasoning systems on BIM, and we classify them into a two-level schema based on what they are intended for. According to our survey, reasoning is mainly used for solving design problems, and is especially applied for code consistency checking, with an emphasis on the semantic web technologies. Furthermore, user-friendliness is still a gap in this field and case-based reasoning, which was often applied in the past efforts, is still hardly applied for reasoning on BIM. The survey shows that this research area is active and that the research results are progressively being integrated into commercial toolkits.
Category: Artificial Intelligence

[447] viXra:1803.0652 [pdf] submitted on 2018-03-26 00:17:46

AI to Understand Human Brain

Authors: George Rajna
Comments: 30 Pages.

And as will be presented today at the 25th annual meeting of the Cognitive Neuroscience Society (CNS), cognitive neuroscientists increasingly are using those emerging artificial networks to enhance their understanding of one of the most elusive intelligence systems, the human brain. [19] U.S. Army Research Laboratory scientists have discovered a way to leverage emerging brain-like computer architectures for an age-old number-theoretic problem known as integer factorization. [18] have come up with a novel machine learning method that enables scientists to derive insights from systems of previously intractable complexity in record time. [17] Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning. [16] Neural networks learn how to carry out certain tasks by analyzing large amounts of data displayed to them. [15] Who is the better experimentalist, a human or a robot? When it comes to exploring synthetic and crystallization conditions for inorganic gigantic molecules, actively learning machines are clearly ahead, as demonstrated by British Scientists in an experiment with polyoxometalates published in the journal Angewandte Chemie. [14] Machine learning algorithms are designed to improve as they encounter more data, making them a versatile technology for understanding large sets of photos such as those accessible from Google Images. Elizabeth Holm, professor of materials science and engineering at Carnegie Mellon University, is leveraging this technology to better understand the enormous number of research images accumulated in the field of materials science. [13] With the help of artificial intelligence, chemists from the University of Basel in Switzerland have computed the characteristics of about two million crystals made up of four chemical elements. The researchers were able to identify 90 previously unknown thermodynamically stable crystals that can be regarded as new materials. [12] The artificial intelligence system's ability to set itself up quickly every morning and compensate for any overnight fluctuations would make this fragile technology much more useful for field measurements, said co-lead researcher Dr Michael Hush from UNSW ADFA. [11]
Category: Artificial Intelligence

[446] viXra:1803.0627 [pdf] submitted on 2018-03-24 08:48:04

Brain-Like Computers

Authors: George Rajna
Comments: 29 Pages.

U.S. Army Research Laboratory scientists have discovered a way to leverage emerging brain-like computer architectures for an age-old number-theoretic problem known as integer factorization. [18] have come up with a novel machine learning method that enables scientists to derive insights from systems of previously intractable complexity in record time. [17] Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning. [16] Neural networks learn how to carry out certain tasks by analyzing large amounts of data displayed to them. [15] Who is the better experimentalist, a human or a robot? When it comes to exploring synthetic and crystallization conditions for inorganic gigantic molecules, actively learning machines are clearly ahead, as demonstrated by British Scientists in an experiment with polyoxometalates published in the journal Angewandte Chemie. [14] Machine learning algorithms are designed to improve as they encounter more data, making them a versatile technology for understanding large sets of photos such as those accessible from Google Images. Elizabeth Holm, professor of materials science and engineering at Carnegie Mellon University, is leveraging this technology to better understand the enormous number of research images accumulated in the field of materials science. [13] With the help of artificial intelligence, chemists from the University of Basel in Switzerland have computed the characteristics of about two million crystals made up of four chemical elements. The researchers were able to identify 90 previously unknown thermodynamically stable crystals that can be regarded as new materials. [12] The artificial intelligence system's ability to set itself up quickly every morning and compensate for any overnight fluctuations would make this fragile technology much more useful for field measurements, said co-lead researcher Dr Michael Hush from UNSW ADFA. [11] Quantum physicist Mario Krenn and his colleagues in the group of Anton Zeilinger from the Faculty of Physics at the University of Vienna and the Austrian Academy of Sciences have developed an algorithm which designs new useful quantum experiments. As the computer does not rely on human intuition, it finds novel unfamiliar solutions. [10]
Category: Artificial Intelligence

[445] viXra:1803.0089 [pdf] submitted on 2018-03-07 02:50:41

Universal Forecasting Scheme : Two Methods {Version 1}

Authors: Ramesh Chandra Bagadi
Comments: 3 Pages.

In this research investigation, the author has detailed a novel method of forecasting.
Category: Artificial Intelligence

[444] viXra:1803.0083 [pdf] submitted on 2018-03-06 11:49:00

Machine Learning Guide Science

Authors: George Rajna
Comments: 25 Pages.

Now researchers at the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) and UC Berkeley have come up with a novel machine learning method that enables scientists to derive insights from systems of previously intractable complexity in record time. [17] Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning. [16] Neural networks learn how to carry out certain tasks by analyzing large amounts of data displayed to them. [15] Who is the better experimentalist, a human or a robot? When it comes to exploring synthetic and crystallization conditions for inorganic gigantic molecules, actively learning machines are clearly ahead, as demonstrated by British Scientists in an experiment with polyoxometalates published in the journal Angewandte Chemie. [14] Machine learning algorithms are designed to improve as they encounter more data, making them a versatile technology for understanding large sets of photos such as those accessible from Google Images. Elizabeth Holm, professor of materials science and engineering at Carnegie Mellon University, is leveraging this technology to better understand the enormous number of research images accumulated in the field of materials science. [13] With the help of artificial intelligence, chemists from the University of Basel in Switzerland have computed the characteristics of about two million crystals made up of four chemical elements. The researchers were able to identify 90 previously unknown thermodynamically stable crystals that can be regarded as new materials. [12] The artificial intelligence system's ability to set itself up quickly every morning and compensate for any overnight fluctuations would make this fragile technology much more useful for field measurements, said co-lead researcher Dr Michael Hush from UNSW ADFA. [11] Quantum physicist Mario Krenn and his colleagues in the group of Anton Zeilinger from the Faculty of Physics at the University of Vienna and the Austrian Academy of Sciences have developed an algorithm which designs new useful quantum experiments. As the computer does not rely on human intuition, it finds novel unfamiliar solutions. [10] Researchers at the University of Chicago's Institute for Molecular Engineering and the University of Konstanz have demonstrated the ability to generate a quantum logic operation, or rotation of the qubit, that - surprisingly—is intrinsically resilient to noise as well as to variations in the strength or duration of the control. Their achievement is based on a geometric concept known as the Berry phase and is implemented through entirely optical means within a single electronic spin in diamond. [9] New research demonstrates that particles at the quantum level can in fact be seen as behaving something like billiard balls rolling along a table, and not merely as the probabilistic smears that the standard interpretation of quantum mechanics suggests. But there's a catch - the tracks the particles follow do not always behave as one would expect from "realistic" trajectories, but often in a fashion that has been termed "surrealistic." [8] Quantum entanglement—which occurs when two or more particles are correlated in such a way that they can influence each other even across large distances—is not an all-or-nothing phenomenon, but occurs in various degrees. The more a quantum state is entangled with its partner, the better the states will perform in quantum information applications. Unfortunately, quantifying entanglement is a difficult process involving complex optimization problems that give even physicists headaches. [7] A trio of physicists in Europe has come up with an idea that they believe would allow a person to actually witness entanglement. Valentina Caprara Vivoli, with the University of Geneva, Pavel Sekatski, with the University of Innsbruck and Nicolas Sangouard, with the University of Basel, have together written a paper describing a scenario where a human subject would be able to witness an instance of entanglement—they have uploaded it to the arXiv server for review by others. [6] The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron’s spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the relativistic quantum theory.
Category: Artificial Intelligence

[443] viXra:1803.0072 [pdf] submitted on 2018-03-06 03:34:57

Universal Forecasting Scheme {Version 1}

Authors: Ramesh Chandra Bagadi
Comments: 2 Pages.

In this research investigation, the author has detailed a novel method of forecasting.
Category: Artificial Intelligence

[442] viXra:1803.0070 [pdf] submitted on 2018-03-06 04:39:44

Universal Forecasting Scheme {Version 2}

Authors: Ramesh Chandra Bagadi
Comments: 2 Pages.

In this research investigation, the author has detailed a novel method of forecasting.
Category: Artificial Intelligence

[441] viXra:1803.0069 [pdf] submitted on 2018-03-06 04:45:06

Universal Forecasting Scheme {Version 3}

Authors: Ramesh Chandra Bagadi
Comments: 2 Pages.

In this research investigation, the author has detailed a novel method of forecasting.
Category: Artificial Intelligence

[440] viXra:1803.0061 [pdf] submitted on 2018-03-04 22:32:07

Cancer Detection Through Handwriting

Authors: Alaa Tarek, Shorouk Alalem, Maryam El-Fdaly, Nehal Fooda
Comments: 31 Pages.

Having a look at the medical field in the previous years and the drawbacks in it, Egypt's health level is declining year after year; due to the statistical study on the health level published by the British medical journal Lancet in 2016 Egypt ranked 124 out of 188 countries. Cancer is a major burden of disease worldwide. Each year, 10,000,000 of people are diagnosed with cancer around the world, and more than half of the patients eventually die because of it. In many countries, cancer ranks the second most common cause of death following cardiovascular diseases. With significant improvement in treatment and prevention of cardiovascular diseases, cancer has or will soon become the number one killer in many parts of the world. Nearly 90,000 people do not know they have got cancer until they arrive at Accident and Emergency wards, by that time only 36 percent will live longer than a year. So, we needed to find controlled ways to diagnose patients earlier. As no aspect of human life has escaped the impact of the information age, and perhaps in no area of life is information more critical than in health and medicine. However, computers have become available for all aspects of human endeavors. After so, we have designed a program that could detect if a person has cancer or not through your handwriting. We chose “efficiency, cost, and applicability” as the design requirements that have been tested. The program could be tested by scanning the text, searching for specific features that are related to cancer and displaying “1” or “0” according to your state. After testing the program many times, we finally reached a mean efficiency of 93.75%. So, this program saves lives, time and money.
Category: Artificial Intelligence

[439] viXra:1803.0053 [pdf] submitted on 2018-03-04 05:55:27

Tunnel Similar Modeling Notation and Spherical Viewpoint

Authors: Alexey Podorov
Comments: 11 Pages.

The article proposes a spherical model of perception, groups and levels of complexity, notation for modeling abstractness, complexity
Category: Artificial Intelligence

[438] viXra:1803.0023 [pdf] submitted on 2018-03-01 09:41:28

AI for Safer Cities

Authors: George Rajna
Comments: 50 Pages.

Computers may better predict taxi and ride sharing service demand, paving the way toward smarter, safer and more sustainable cities, according to an international team of researchers. [29] For the first time, physicists have demonstrated that machine learning can reconstruct a quantum system based on relatively few experimental measurements. [28] AlphaZero plays very unusually; not like a human, but also not like a typical computer. Instead, it plays with "real artificial" intelligence. [27] Predictions for an AI-dominated future are increasingly common, but Antoine Blondeau has experience in reading, and arguably manipulating, the runes—he helped develop technology that evolved into predictive texting and Apple's Siri. [26] Artificial intelligence can improve health care by analyzing data from apps, smartphones and wearable technology. [25] Now, researchers at Google's DeepMind have developed a simple algorithm to handle such reasoning—and it has already beaten humans at a complex image comprehension test. [24] A marimba-playing robot with four arms and eight sticks is writing and playing its own compositions in a lab at the Georgia Institute of Technology. The pieces are generated using artificial intelligence and deep learning. [23] Now, a team of researchers at MIT and elsewhere has developed a new approach to such computations, using light instead of electricity, which they say could vastly improve the speed and efficiency of certain deep learning computations. [22] Physicists have found that the structure of certain types of quantum learning algorithms is very similar to their classical counterparts—a finding that will help scientists further develop the quantum versions. [21] We should remain optimistic that quantum computing and AI will continue to improve our lives, but we also should continue to hold companies, organizations, and governments accountable for how our private data is used, as well as the technology's impact on the environment. [20] It's man vs machine this week as Google's artificial intelligence programme AlphaGo faces the world's top-ranked Go player in a contest expected to end in another victory for rapid advances in AI. [19]
Category: Artificial Intelligence

[437] viXra:1802.0364 [pdf] submitted on 2018-02-26 10:27:48

AI of Quantum Systems

Authors: George Rajna
Comments: 49 Pages.

For the first time, physicists have demonstrated that machine learning can reconstruct a quantum system based on relatively few experimental measurements. [28] AlphaZero plays very unusually; not like a human, but also not like a typical computer. Instead, it plays with "real artificial" intelligence. [27] Predictions for an AI-dominated future are increasingly common, but Antoine Blondeau has experience in reading, and arguably manipulating, the runes—he helped develop technology that evolved into predictive texting and Apple's Siri. [26] Artificial intelligence can improve health care by analyzing data from apps, smartphones and wearable technology. [25] Now, researchers at Google's DeepMind have developed a simple algorithm to handle such reasoning—and it has already beaten humans at a complex image comprehension test. [24] A marimba-playing robot with four arms and eight sticks is writing and playing its own compositions in a lab at the Georgia Institute of Technology. The pieces are generated using artificial intelligence and deep learning. [23] Now, a team of researchers at MIT and elsewhere has developed a new approach to such computations, using light instead of electricity, which they say could vastly improve the speed and efficiency of certain deep learning computations. [22] Physicists have found that the structure of certain types of quantum learning algorithms is very similar to their classical counterparts—a finding that will help scientists further develop the quantum versions. [21] We should remain optimistic that quantum computing and AI will continue to improve our lives, but we also should continue to hold companies, organizations, and governments accountable for how our private data is used, as well as the technology's impact on the environment. [20] It's man vs machine this week as Google's artificial intelligence programme AlphaGo faces the world's top-ranked Go player in a contest expected to end in another victory for rapid advances in AI. [19] Google's computer programs are gaining a better understanding of the world, and now it wants them to handle more of the decision-making for the billions of people who use its services. [18]
Category: Artificial Intelligence

[436] viXra:1802.0330 [pdf] submitted on 2018-02-23 10:46:53

AlphaZero Just Playing

Authors: George Rajna
Comments: 47 Pages.

AlphaZero plays very unusually; not like a human, but also not like a typical computer. Instead, it plays with "real artificial" intelligence. [27] Predictions for an AI-dominated future are increasingly common, but Antoine Blondeau has experience in reading, and arguably manipulating, the runes—he helped develop technology that evolved into predictive texting and Apple's Siri. [26] Artificial intelligence can improve health care by analyzing data from apps, smartphones and wearable technology. [25] Now, researchers at Google's DeepMind have developed a simple algorithm to handle such reasoning—and it has already beaten humans at a complex image comprehension test. [24] A marimba-playing robot with four arms and eight sticks is writing and playing its own compositions in a lab at the Georgia Institute of Technology. The pieces are generated using artificial intelligence and deep learning. [23] Now, a team of researchers at MIT and elsewhere has developed a new approach to such computations, using light instead of electricity, which they say could vastly improve the speed and efficiency of certain deep learning computations. [22] Physicists have found that the structure of certain types of quantum learning algorithms is very similar to their classical counterparts—a finding that will help scientists further develop the quantum versions. [21] We should remain optimistic that quantum computing and AI will continue to improve our lives, but we also should continue to hold companies, organizations, and governments accountable for how our private data is used, as well as the technology's impact on the environment. [20] It's man vs machine this week as Google's artificial intelligence programme AlphaGo faces the world's top-ranked Go player in a contest expected to end in another victory for rapid advances in AI. [19] Google's computer programs are gaining a better understanding of the world, and now it wants them to handle more of the decision-making for the billions of people who use its services. [18] Microsoft on Wednesday unveiled new tools intended to democratize artificial intelligence by enabling machine smarts to be built into software from smartphone games to factory floors. [17]
Category: Artificial Intelligence

[435] viXra:1802.0087 [pdf] submitted on 2018-02-08 09:33:09

A Forecasting Scheme

Authors: Ramesh Chandra Bagadi
Comments: 1 Page.

In this research investigation, the author has detailed a novel method of finding the next term of a given Sequence.
Category: Artificial Intelligence

[434] viXra:1802.0038 [pdf] submitted on 2018-02-05 05:01:24

Quantum Algorithm Help AI

Authors: George Rajna
Comments: 55 Pages.

An international team has shown that quantum computers can do one such analysis faster than classical computers for a wider array of data types than was previously expected. [35] A team of researchers at Oak Ridge National Laboratory has demonstrated that it is possible to use cloud-based quantum computers to conduct quantum simulations and calculations. [34] Physicists have designed a new method for transmitting big quantum data across long distances that requires far fewer resources than previous methods, bringing the implementation of long-distance big quantum data transmission closer to reality. [33] A joint China-Austria team has performed quantum key distribution between the quantum-science satellite Micius and multiple ground stations located in Xinglong (near Beijing), Nanshan (near Urumqi), and Graz (near Vienna). [32] In the race to build a computer that mimics the massive computational power of the human brain, researchers are increasingly turning to memristors, which can vary their electrical resistance based on the memory of past activity. [31] Engineers worldwide have been developing alternative ways to provide greater memory storage capacity on even smaller computer chips. Previous research into two-dimensional atomic sheets for memory storage has failed to uncover their potential—until now. [30]
Category: Artificial Intelligence

[433] viXra:1802.0031 [pdf] submitted on 2018-02-03 05:25:39

A Feasible Path to a Machine that Can Pass the Turing Test.

Authors: Tofara Moyo
Comments: 5 Pages.

In this paper we will outline a NLP system that is based largely in graph theory, and together with techniques found in linear algebra we will be able to model the rules of logic. Inference from given data in natural language format will then be done by creating a mapping between the premises and the conclusion. During training the vector that is responsible for taking us from the space that the premise occupies to the space that the conclusion occupies will represent the particular logical rule used.Also training involves determining a particular vector for the job.
Category: Artificial Intelligence

[432] viXra:1801.0413 [pdf] submitted on 2018-01-30 20:38:58

On The Subject of Thinking Machines

Authors: John Olafenwa, Moses Olafenwa
Comments: 9 Pages. An investigation of the concepts of thought, imagination and consciousness in learning machines

68 years ago, Alan Turing proposed the question "Can Machines Think" in his seminal paper [1] titled "Computing Machinery and Intelligence" and he formulated the "Imitation Game" also known as the Turing test as a way to answer this question without referring to a rather ambiguous dictionary definition of the word "Think" We have come a long way to building intelligent machines, in fact, the rate of progress in Deep Learning and Reinforcement Learning, the two corner stones of artificial intelligence, is unprecedented. Alan Turing would have been proud of our achievements in computer vision, speech, natural language processing and autonomous systems. However, there are still many challenges and we are still some distance from building machines that can pass the Turing test. In this paper, we discuss some of the biggest questions concerning intelligent machines and we attempt to answer them, as much as can be explained by modern AI.
Category: Artificial Intelligence

[431] viXra:1801.0412 [pdf] submitted on 2018-01-30 21:56:30

A Predictor-Corrector Method for the Training of Deep Neural Networks

Authors: Yatin Saraiya
Comments: 6 pages, 2 figures, 2 tables

The training of deep neural nets is expensive. We present a predictor-corrector method for the training of deep neural nets. It alternates a predictor pass with a corrector pass using stochastic gradient descent with backpropagation such that there is no loss in validation accuracy. No special modifications to SGD with backpropagation is required by this methodology. Our experiments showed a time improvement of 9% on the CIFAR-10 dataset.
Category: Artificial Intelligence

[430] viXra:1801.0411 [pdf] submitted on 2018-01-30 22:00:29

Using Accumulation to Optimize Deep Residual Neural Nets

Authors: Yatin Saraiya
Comments: 7 pages, 6 figures, 1 table

Residual Neural Networks [1] won first place in all five main tracks of the ImageNet and COCO 2015 competitions. This kind of network involves the creation of pluggable modules such that the output contains a residual from the input. The residual in that paper is the identity function. We propose to include residuals from all lower layers, suitably normalized, to create the residual. This way, all previous layers contribute equally to the output of a layer. We show that our approach is an improvement on [1] for the CIFAR-10 dataset.
Category: Artificial Intelligence

[429] viXra:1801.0407 [pdf] submitted on 2018-01-29 13:36:59

Artificial Intelligence Weapon

Authors: George Rajna
Comments: 48 Pages.

A country that thinks its adversaries have or will get AI weapons will want to get them too. Wide use of AI-powered cyberattacks may still be some time away. [28] Following the old saying that "knowledge is power", companies are seeking to infer increasingly intimate properties about their customers as a way to gain an edge over their competitors. [27] Researchers from Human Longevity, Inc. (HLI) have published a study in which individual faces and other physical traits were predicted using whole genome sequencing data and machine learning. [26] Artificial intelligence can improve health care by analyzing data from apps, smartphones and wearable technology. [25] Now, researchers at Google's DeepMind have developed a simple algorithm to handle such reasoning—and it has already beaten humans at a complex image comprehension test. [24] A marimba-playing robot with four arms and eight sticks is writing and playing its own compositions in a lab at the Georgia Institute of Technology. The pieces are generated using artificial intelligence and deep learning. [23] Now, a team of researchers at MIT and elsewhere has developed a new approach to such computations, using light instead of electricity, which they say could vastly improve the speed and efficiency of certain deep learning computations. [22] Physicists have found that the structure of certain types of quantum learning algorithms is very similar to their classical counterparts—a finding that will help scientists further develop the quantum versions. [21] We should remain optimistic that quantum computing and AI will continue to improve our lives, but we also should continue to hold companies, organizations, and governments accountable for how our private data is used, as well as the technology's impact on the environment. [20] It's man vs machine this week as Google's artificial intelligence programme AlphaGo faces the world's top-ranked Go player in a contest expected to end in another victory for rapid advances in AI. [19] Google's computer programs are gaining a better understanding of the world, and now it wants them to handle more of the decision-making for the billions of people who use its services. [18]
Category: Artificial Intelligence

[428] viXra:1801.0367 [pdf] submitted on 2018-01-27 03:06:22

Superconducting Synapse

Authors: George Rajna
Comments: 26 Pages.

Researchers at the National Institute of Standards and Technology (NIST) have built a superconducting switch that "learns" like a biological system and could connect processors and store memories in future computers operating like the human brain. [17] Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning. [16] Neural networks learn how to carry out certain tasks by analyzing large amounts of data displayed to them. [15] Who is the better experimentalist, a human or a robot? When it comes to exploring synthetic and crystallization conditions for inorganic gigantic molecules, actively learning machines are clearly ahead, as demonstrated by British Scientists in an experiment with polyoxometalates published in the journal Angewandte Chemie. [14] Machine learning algorithms are designed to improve as they encounter more data, making them a versatile technology for understanding large sets of photos such as those accessible from Google Images. Elizabeth Holm, professor of materials science and engineering at Carnegie Mellon University, is leveraging this technology to better understand the enormous number of research images accumulated in the field of materials science. [13] With the help of artificial intelligence, chemists from the University of Basel in Switzerland have computed the characteristics of about two million crystals made up of four chemical elements. The researchers were able to identify 90 previously unknown thermodynamically stable crystals that can be regarded as new materials. [12] The artificial intelligence system's ability to set itself up quickly every morning and compensate for any overnight fluctuations would make this fragile technology much more useful for field measurements, said co-lead researcher Dr Michael Hush from UNSW ADFA. [11] Quantum physicist Mario Krenn and his colleagues in the group of Anton Zeilinger from the Faculty of Physics at the University of Vienna and the Austrian Academy of Sciences have developed an algorithm which designs new useful quantum experiments. As the computer does not rely on human intuition, it finds novel unfamiliar solutions. [10] Researchers at the University of Chicago's Institute for Molecular Engineering and the University of Konstanz have demonstrated the ability to generate a quantum logic operation, or rotation of the qubit, that - surprisingly—is intrinsically resilient to noise as well as to variations in the strength or duration of the control. Their achievement is based on a geometric concept known as the Berry phase and is implemented through entirely optical means within a single electronic spin in diamond. [9] New research demonstrates that particles at the quantum level can in fact be seen as behaving something like billiard balls rolling along a table, and not merely as the probabilistic smears that the standard interpretation of quantum mechanics suggests. But there's a catch - the tracks the particles follow do not always behave as one would expect from "realistic" trajectories, but often in a fashion that has been termed "surrealistic." [8] Quantum entanglement—which occurs when two or more particles are correlated in such a way that they can influence each other even across large distances—is not an all-or-nothing phenomenon, but occurs in various degrees. The more a quantum state is entangled with its partner, the better the states will perform in quantum information applications. Unfortunately, quantifying entanglement is a difficult process involving complex optimization problems that give even physicists headaches. [7] A trio of physicists in Europe has come up with an idea that they believe would allow a person to actually witness entanglement. Valentina Caprara Vivoli, with the University of Geneva, Pavel Sekatski, with the University of Innsbruck and Nicolas Sangouard, with the University of Basel, have together written a paper describing a scenario where a human subject would be able to witness an instance of entanglement—they have uploaded it to the arXiv server for review by others. [6] The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron’s spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the relativistic quantum theory.
Category: Artificial Intelligence

[427] viXra:1801.0366 [pdf] submitted on 2018-01-26 05:02:58

Mathematical Model of Inventions

Authors: George Rajna
Comments: 27 Pages.

Researchers from Queen Mary University of London have developed a mathematical model for the emergence of innovations. [17] Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning. [16] Neural networks learn how to carry out certain tasks by analyzing large amounts of data displayed to them. [15] Who is the better experimentalist, a human or a robot? When it comes to exploring synthetic and crystallization conditions for inorganic gigantic molecules, actively learning machines are clearly ahead, as demonstrated by British Scientists in an experiment with polyoxometalates published in the journal Angewandte Chemie. [14] Machine learning algorithms are designed to improve as they encounter more data, making them a versatile technology for understanding large sets of photos such as those accessible from Google Images. Elizabeth Holm, professor of materials science and engineering at Carnegie Mellon University, is leveraging this technology to better understand the enormous number of research images accumulated in the field of materials science. [13] With the help of artificial intelligence, chemists from the University of Basel in Switzerland have computed the characteristics of about two million crystals made up of four chemical elements. The researchers were able to identify 90 previously unknown thermodynamically stable crystals that can be regarded as new materials. [12] The artificial intelligence system's ability to set itself up quickly every morning and compensate for any overnight fluctuations would make this fragile technology much more useful for field measurements, said co-lead researcher Dr Michael Hush from UNSW ADFA. [11] Quantum physicist Mario Krenn and his colleagues in the group of Anton Zeilinger from the Faculty of Physics at the University of Vienna and the Austrian Academy of Sciences have developed an algorithm which designs new useful quantum experiments. As the computer does not rely on human intuition, it finds novel unfamiliar solutions. [10] Researchers at the University of Chicago's Institute for Molecular Engineering and the University of Konstanz have demonstrated the ability to generate a quantum logic operation, or rotation of the qubit, that-surprisingly—is intrinsically resilient to noise as well as to variations in the strength or duration of the control. Their achievement is based on a geometric concept known as the Berry phase and is implemented through entirely optical means within a single electronic spin in diamond. [9]
Category: Artificial Intelligence

[426] viXra:1801.0363 [pdf] submitted on 2018-01-26 07:55:13

Deep Learning for Gravitational Wave

Authors: George Rajna
Comments: 27 Pages.

Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning. [16] Neural networks learn how to carry out certain tasks by analyzing large amounts of data displayed to them. [15] Who is the better experimentalist, a human or a robot? When it comes to exploring synthetic and crystallization conditions for inorganic gigantic molecules, actively learning machines are clearly ahead, as demonstrated by British Scientists in an experiment with polyoxometalates published in the journal Angewandte Chemie. [14] Machine learning algorithms are designed to improve as they encounter more data, making them a versatile technology for understanding large sets of photos such as those accessible from Google Images. Elizabeth Holm, professor of materials science and engineering at Carnegie Mellon University, is leveraging this technology to better understand the enormous number of research images accumulated in the field of materials science. [13] With the help of artificial intelligence, chemists from the University of Basel in Switzerland have computed the characteristics of about two million crystals made up of four chemical elements. The researchers were able to identify 90 previously unknown thermodynamically stable crystals that can be regarded as new materials. [12] The artificial intelligence system's ability to set itself up quickly every morning and compensate for any overnight fluctuations would make this fragile technology much more useful for field measurements, said co-lead researcher Dr Michael Hush from UNSW ADFA. [11] Quantum physicist Mario Krenn and his colleagues in the group of Anton Zeilinger from the Faculty of Physics at the University of Vienna and the Austrian Academy of Sciences have developed an algorithm which designs new useful quantum experiments. As the computer does not rely on human intuition, it finds novel unfamiliar solutions. [10]
Category: Artificial Intelligence

[425] viXra:1801.0361 [pdf] submitted on 2018-01-26 09:19:57

Hyperspectral Artificial Intelligence

Authors: George Rajna
Comments: 29 Pages.

VTT Technical Research Centre of Finland has developed a highly cost-efficient hyperspectral imaging technology, which enables the introduction of new artificial intelligence applications into consumer devices. [19] Scientists at the National Center for Supercomputing Applications (NCSA), located at the University of Illinois at Urbana-Champaign, have pioneered the use of GPU-accelerated deep learning for rapid detection and characterization of gravitational waves. [18] Researchers from Queen Mary University of London have developed a mathematical model for the emergence of innovations. [17] Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning. [16] Neural networks learn how to carry out certain tasks by analyzing large amounts of data displayed to them. [15] Who is the better experimentalist, a human or a robot? When it comes to exploring synthetic and crystallization conditions for inorganic gigantic molecules, actively learning machines are clearly ahead, as demonstrated by British Scientists in an experiment with polyoxometalates published in the journal Angewandte Chemie. [14] Machine learning algorithms are designed to improve as they encounter more data, making them a versatile technology for understanding large sets of photos such as those accessible from Google Images. Elizabeth Holm, professor of materials science and engineering at Carnegie Mellon University, is leveraging this technology to better understand the enormous number of research images accumulated in the field of materials science. [13] With the help of artificial intelligence, chemists from the University of Basel in Switzerland have computed the characteristics of about two million crystals made up of four chemical elements. The researchers were able to identify 90 previously unknown thermodynamically stable crystals that can be regarded as new materials. [12] The artificial intelligence system's ability to set itself up quickly every morning and compensate for any overnight fluctuations would make this fragile technology much more useful for field measurements, said co-lead researcher Dr Michael Hush from UNSW ADFA. [11] Quantum physicist Mario Krenn and his colleagues in the group of Anton Zeilinger from the Faculty of Physics at the University of Vienna and the Austrian Academy of Sciences have developed an algorithm which designs new useful quantum experiments. As the computer does not rely on human intuition, it finds novel unfamiliar solutions. [10] Researchers at the University of Chicago's Institute for Molecular Engineering and the University of Konstanz have demonstrated the ability to generate a quantum logic operation, or rotation of the qubit, that - surprisingly—is intrinsically resilient to noise as well as to variations in the strength or duration of the control. Their achievement is based on a geometric concept known as the Berry phase and is implemented through entirely optical means within a single electronic spin in diamond. [9] New research demonstrates that particles at the quantum level can in fact be seen as behaving something like billiard balls rolling along a table, and not merely as the probabilistic smears that the standard interpretation of quantum mechanics suggests. But there's a catch - the tracks the particles follow do not always behave as one would expect from "realistic" trajectories, but often in a fashion that has been termed "surrealistic." [8] Quantum entanglement—which occurs when two or more particles are correlated in such a way that they can influence each other even across large distances—is not an all-or-nothing phenomenon, but occurs in various degrees. The more a quantum state is entangled with its partner, the better the states will perform in quantum information applications. Unfortunately, quantifying entanglement is a difficult process involving complex optimization problems that give even physicists headaches. [7] A trio of physicists in Europe has come up with an idea that they believe would allow a person to actually witness entanglement. Valentina Caprara Vivoli, with the University of Geneva, Pavel Sekatski, with the University of Innsbruck and Nicolas Sangouard, with the University of Basel, have together written a paper describing a scenario where a human subject would be able to witness an instance of entanglement—they have uploaded it to the arXiv server for review by others. [6] The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron’s spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the relativistic quantum theory.
Category: Artificial Intelligence

[424] viXra:1801.0271 [pdf] submitted on 2018-01-21 22:33:21

Refutation: Neutrosophic Logic by Florentin Smardanche as Generalized Intuitionistic, Fuzzy Logic © 2018 by Colin James III All Rights Reserved.

Authors: Colin James III
Comments: 2 Pages. © 2018 by Colin James III All rights reserved.

We map the neutrosophic logical values of truth, falsity, and indeterminacy on intervals "]0,1[" and "]-0,1+[" in equations for the Meth8/VL4 apparatus. We test the summation of the those values. The result is not tautologous, meaning neutrosophic logic is refuted and hence its use as a generalization of intuitionistic, fuzzy logic is likewise unworkable.
Category: Artificial Intelligence

[423] viXra:1801.0243 [pdf] submitted on 2018-01-19 09:05:53

AI Quantum Experiments

Authors: George Rajna
Comments: 38 Pages.

On the way to an intelligent laboratory, physicists from Innsbruck and Vienna present an artificial agent that autonomously designs quantum experiments. [24] An answer to a quantum-physical question provided by the algorithm Melvin has uncovered a hidden link between quantum experiments and the mathematical field of Graph Theory. [23] Engineers develop key mathematical formula for driving quantum experiments. [22] Physicists are developing quantum simulators, to help solve problems that are beyond the reach of conventional computers. [21] Engineers at Australia's University of New South Wales have invented a radical new architecture for quantum computing, based on novel 'flip-flop qubits', that promises to make the large-scale manufacture of quantum chips dramatically cheaper - and easier - than thought possible. [20] A team of researchers from the U.S. and Italy has built a quantum memory device that is approximately 1000 times smaller than similar devices— small enough to install on a chip. [19] The cutting edge of data storage research is working at the level of individual atoms and molecules, representing the ultimate limit of technological miniaturisation. [18] This is an important clue for our theoretical understanding of optically controlled magnetic data storage media. [17] A crystalline material that changes shape in response to light could form the heart of novel light-activated devices. [16] Now a team of Penn State electrical engineers have a way to simultaneously control diverse optical properties of dielectric waveguides by using a two-layer coating, each layer with a near zero thickness and weight. [15] Just like in normal road traffic, crossings are indispensable in optical signal processing. In order to avoid collisions, a clear traffic rule is required. A new method has now been developed at TU Wien to provide such a rule for light signals. [14] Researchers have developed a way to use commercial inkjet printers and readily available ink to print hidden images that are only visible when illuminated with appropriately polarized waves in the terahertz region of the electromagnetic spectrum. [13] That is, until now, thanks to the new solution devised at TU Wien: for the first time ever, permanent magnets can be produced using a 3D printer. This allows magnets to be produced in complex forms and precisely customised magnetic fields, required, for example, in magnetic sensors. [12] For physicists, loss of magnetisation in permanent magnets can be a real concern. In response, the Japanese company Sumitomo created the strongest available magnet— one offering ten times more magnetic energy than previous versions—in 1983. [11] New method of superstrong magnetic fields’ generation proposed by Russian scientists in collaboration with foreign colleagues. [10] By showing that a phenomenon dubbed the "inverse spin Hall effect" works in several organic semiconductors - including carbon-60 buckyballs - University of Utah physicists changed magnetic "spin current" into electric current. The efficiency of this new power conversion method isn't yet known, but it might find use in future electronic devices including batteries, solar cells and computers. [9] Researchers from the Norwegian University of Science and Technology (NTNU) and the University of Cambridge in the UK have demonstrated that it is possible to directly generate an electric current in a magnetic material by rotating its magnetization. [8] This paper explains the magnetic effect of the electric current from the observed effects of the accelerating electrons, causing naturally the experienced changes of the electric field potential along the electric wire. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the wave particle duality and the electron’s spin also, building the bridge between the Classical and Quantum Theories. The changing acceleration of the electrons explains the created negative electric field of the magnetic induction, the changing relativistic mass and the Gravitational Force, giving a Unified Theory of the physical forces. Taking into account the Planck Distribution Law of the electromagnetic oscillators also, we can explain the electron/proton mass rate and the Weak and Strong Interactions.
Category: Artificial Intelligence

[422] viXra:1801.0192 [pdf] submitted on 2018-01-16 07:03:26

FastNet: An Efficient Architecture for Smart Devices

Authors: John Olafenwa, Moses Olafenwa
Comments: 9 Pages.

Inception and the Resnet family of Convolutional Neural Network architectures have broken records in the past few years, but recent state of the art models have also incurred very high computational cost in terms of training, inference and model size. Making the deployment of these models on Edge devices, impractical. In light of this, we present a new novel architecture that is designed for high computational efficiency on both GPUs and CPUs, and is highly suited for deployment on Mobile Applications, Smart Cameras, Iot devices and controllers as well as low cost drones. Our architecture boasts competitive accuracies on standard datasets even outperforming the original Resnet. We present below the motivation for this research, the architecture of the network, single test accuracies on CIFAR 10 and CIFAR 100, a detailed comparison with other well-known architectures and link to an implementation in Keras.
Category: Artificial Intelligence

[421] viXra:1801.0102 [pdf] submitted on 2018-01-09 11:34:24

Bayesian Transfer Learning for Deep Networks

Authors: J. Wohlert, A. M. Munk, S. Sengupta, F. Laumann
Comments: 6 Pages.

We propose a method for transfer learning for deep networks through Bayesian inference, where an approximate posterior distribution q(w|θ) of model parameters w is learned through variational approximation. Utilizing Bayes by Backprop we optimize the parameters θ associated with the approximate distribution. When performing transfer learning we consider two tasks; A and B. Firstly, an approximate posterior q_A(w|θ) is learned from task A which is afterwards transferred as a prior p(w) → q_A(w|θ) when learning the approximate posterior distribution q_B(w|θ) for task B. Initially, we consider a multivariate normal distribution q(w|θ) = N (µ, Σ), with diagonal covariance matrix Σ. Secondly, we consider the prospects of introducing more expressive approximate distributions - specifically those known as normalizing flows. By investigating these concepts on the MNIST data set we conclude that utilizing normalizing flows does not improve Bayesian inference in the context presented here. Further, we show that transfer learning is not feasible using our proposed architecture and our definition of task A and task B, but no general conclusion regarding rejecting a Bayesian approach to transfer learning can be made.
Category: Artificial Intelligence

[420] viXra:1801.0050 [pdf] submitted on 2018-01-06 00:20:25

Fruit Recognition from Images Using Deep Learning

Authors: Horea Muresan, Mihai Oltean
Comments: 13 Pages. Data can be downloaded from https://github.com/Horea94/Fruit-Images-Dataset

In this paper we introduce a new, high-quality, dataset of images containing fruits. We also present the results of some numerical experiment for training a neural network to detect fruits. We discuss the reason why we chose to use fruits in this project by proposing a few applications that could use this kind of neural network.
Category: Artificial Intelligence

[419] viXra:1801.0041 [pdf] submitted on 2018-01-05 06:09:53

Taking Advantage of BiLSTM Encoding to Handle Punctuation in Dependency Parsing: A Brief Idea

Authors: Matteo Grella
Comments: 3 Pages.

In the context of the bidirectional-LSTMs neural parser (Kiperwasser and Goldberg, 2016), an idea is proposed to initialize the parsing state without punctuation-tokens but using them for the BiLSTM sentence encoding. The relevant information brought by the punctuation-tokens should be implicitly learned using the errors of the recurrent contributions only.
Category: Artificial Intelligence

[418] viXra:1712.0659 [pdf] submitted on 2017-12-29 06:21:14

TDBF: Two Dimensional Belief Function

Authors: Yangxue Li; Yong Deng
Comments: 15 Pages.

How to efficiently handle uncertain information is still an open issue. Inthis paper, a new method to deal with uncertain information, named as two dimensional belief function (TDBF), is presented. A TDBF has two components, T=(mA,mB). The first component, mA, is a classical belief function. The second component, mB, also is a classical belief function, but it is a measure of reliability of the first component. The definition of TDBF and the discounting algorithm are proposed. Compared with the classical discounting model, the proposed TDBF is more flexible and reasonable. Numerical examples are used to show the efficiency of the proposed method.
Category: Artificial Intelligence

[417] viXra:1712.0647 [pdf] submitted on 2017-12-28 23:25:34

A Total Uncertainty Measure for D Numbers Based on Belief Intervals

Authors: Xinyang Deng, Wen Jiang
Comments: 14 Pages.

As a generalization of Dempster-Shafer theory, the theory of D numbers is a new theoretical framework for uncertainty reasoning. Measuring the uncertainty of knowledge or information represented by D numbers is an unsolved issue in that theory. In this paper, inspired by distance based uncertainty measures for Dempster-Shafer theory, a total uncertainty measure for a D number is proposed based on its belief intervals. The proposed total uncertainty measure can simultaneously capture the discord, and non-specificity, and non-exclusiveness involved in D numbers. And some basic properties of this total uncertainty measure, including range, monotonicity, generalized set consistency, are also presented.
Category: Artificial Intelligence

[416] viXra:1712.0495 [pdf] submitted on 2017-12-18 08:50:22

Just Keep it in Mind: Information is a Complex Notion with Physical and Semantic Information Staying for Real and Imaginary Parts of the Expression

Authors: Emanuel Diamant
Comments: 3 Pages. Presented at the IS4SI 2017 Summit, Information Theory Section, Gothenburg, Sweden, 12–16 June 2017

Shannon’s Information was devised to improve the performance of a data communication channel. Since then, the situation has changed drastically and today a more generally applicable and suitable definition of information is urgently required. To meet this demand, I have proposed a definition of my own. According to it, information is a complex notion with Physical and Semantic information staying for Real and Imaginary parts of the term. The scientific community has very unfriendly accepted this idea. But without a better solution for the problem of: 1) intron-exon partition in genes, 2) information flow in neuronal networks, 3) memory creation and potentiation in brains, 4) thoughts and thinking materialization in human heads, and 5) the undeniable shift from Computational (that is, data processing based) approach to Cognitive (that is, information processing based) approach in the field of scientific research, they would be forced to admit one day that something worthy is in this new definition.
Category: Artificial Intelligence

[415] viXra:1712.0494 [pdf] submitted on 2017-12-18 09:05:26

Shannon's Definition of Information is Obsolete and Inadequate. it is Time to Embrace Kolmogorov’s Insights on the Matter

Authors: Emanuel Diamant
Comments: 3 Pages. Presented at the 2016 ICSEE International Conference, Eilat, Israel, 16 – 18 November 2016.

Information Theory, as developed by Claude Shannon in 1948, was about the communication of messages as electronic signals via a transmission channel. Only physical properties of the signal and the channel have been taken into account. While the meaning of the message has been ignored totally. Such an approach to information met very well the requirements of a data communication channel. But recent advances in almost all sciences put an urgent demand for meaningful information inclusion into the body of a communicated message. To meet this demand, I have proposed a new definition of information. In this definition, information is seen as a complex notion composed of two inseparable parts: Physical information and Semantic information. Classical informations such as Shannon, Fisher, Renyi, Kolmogorov’s complexity, and Chaitin’s algorithmic information – they are all physical information variants. Semantic information is a new concept and it desires to be properly studied, treated, and used.
Category: Artificial Intelligence

[414] viXra:1712.0469 [pdf] submitted on 2017-12-15 23:33:47

Predicting Yelp Star Reviews Based on Network Structure with Deep Learning

Authors: Luis Perez
Comments: 12 pages, 17 figures

In this paper, we tackle the real-world problem of predicting Yelp star-review rating based on business features (such as images, descriptions), user features (average previous ratings), and, of particular interest, network properties (which businesses has a user rated before). We compare multiple models on different sets of features -- from simple linear regression on network features only to deep learning models on network and item features. In recent years, breakthroughs in deep learning have led to increased accuracy in common supervised learning tasks, such as image classification, captioning, and language understanding. However, the idea of combining deep learning with network feature and structure appears to be novel. While the problem of predicting future interactions in a network has been studied at length, these approaches have often ignored either node-specific data or global structure. We demonstrate that taking a mixed approach combining both node-level features and network information can effectively be used to predict Yelp-review star ratings. We evaluate on the Yelp dataset by splitting our data along the time dimension (as would naturally occur in the real-world) and comparing our model against others which do no take advantage of the network structure and/or deep learning.
Category: Artificial Intelligence

[413] viXra:1712.0468 [pdf] submitted on 2017-12-15 23:41:37

The Effectiveness of Data Augmentation in Image Classification using Deep Learning

Authors: Luis Perez, Jason Wang
Comments: 8 Pages.

In this paper, we explore and compare multiple solutions to the problem of data augmentation in image classification. Previous work has demonstrated the effectiveness of data augmentation through simple techniques, such as cropping, rotating, and flipping input images. We artificially constrain our access to data to a small subset of the ImageNet dataset, and compare each data augmentation technique in turn. One of the more successful data augmentations strategies is the traditional transformations mentioned above. We also experiment with GANs to generate images of different styles. Finally, we propose a method to allow a neural net to learn augmentations that best improve the classifier, which we call neural augmentation. We discuss the successes and shortcomings of this method on various datasets.
Category: Artificial Intelligence

[412] viXra:1712.0467 [pdf] submitted on 2017-12-15 23:43:11

Gaussian Processes for Crime Prediction

Authors: Luis Perez, Alex Wang
Comments: 8 Pages.

The ability to predict crime is incredibly useful for police departments, city planners, and many other parties, but thus far current approaches have not made use of recent developments of machine learning techniques. In this paper, we present a novel approach to this task: Gaussian processes regression. Gaussian processes (GP) are a rich family of distributions that are able to learn functions. We train GPs on historic crime data to learn the underlying probability distribution of crime incidence to make predictions on future crime distributions.
Category: Artificial Intelligence

[411] viXra:1712.0465 [pdf] submitted on 2017-12-16 00:36:46

Reinforcement Learning with Swingy Monkey

Authors: Luis Perez, Aidi Zhang, Kevin Eskici
Comments: 7 Pages.

This paper explores model-free, model-based, and mixture models for reinforcement learning under the setting of a SwingyMonkey game \footnote{The code is hosted on a public repository \href{https://github.com/kandluis/machine-learning}{here} under the prac4 directory.}. SwingyMonkey is a simple game with well-defined goals and mechanisms, with a relatively small state-space. Using Bayesian Optimization \footnote{The optimization took place using the open-source software made available by HIPS \href{https://github.com/HIPS/Spearmint}{here}.} on a simple Q-Learning algorithm, we were able to obtain high scores within just a few training epochs. However, the system failed to scale well after continued training, and optimization over hundreds of iterations proved too time-consuming to be effective. After manually exploring multiple approaches, the best results were achieved using a mixture of $\epsilon$-greedy Q-Learning with a stable learning rate,$\alpha$, and $\delta \approx 1$ discount factor. Despite the theoretical limitations of this approach, the settings, resulted in maximum scores of over 5000 points with an average score of $\bar{x} \approx 684$ (averaged over the final 100 testing epochs, median of $\bar{m} = 357.5$). The results show an continuing linear log-relation capping only after 20,000 training epochs.
Category: Artificial Intelligence

[410] viXra:1712.0464 [pdf] submitted on 2017-12-16 00:38:28

Multi-Document Text Summarization

Authors: Luis Perez, Kevin Eskici
Comments: 24 Pages. 24

We tackle the problem of multi-document extractive summarization by implementing two well-known algorithms for single-text summarization -- {\sc TextRank} and {\sc Grasshopper}. We use ROUGE-1 and ROUGE-2 precision scores with the DUC 2004 Task 2 data set to measure the performance of these two algorithms, with optimized parameters as described in their respective papers ($\alpha =0.25$ and $\lambda=0.5$ for Grasshopper and $d=0.85$ for TextRank). We compare these modified algorithms to common baselines as well as non-naive, novel baselines and we present the resulting ROUGE-1 and ROUGE-2 recall scores. Subsequently, we implement two novel algorithms as extensions of {\sc GrassHopper} and {\sc TextRank}, each termed {\sc ModifiedGrassHopper} and {\sc ModifiedTextRank}. The modified algorithms intuitively attempt to ``maximize'' diversity across the summary. We present the resulting ROUGE scores. We expect that with further optimizations, this unsupervised approach to extractive text summarization will prove useful in practice.
Category: Artificial Intelligence

[409] viXra:1712.0446 [pdf] submitted on 2017-12-13 08:17:06

A New Divergence Measure for Basic Probability Assignment and Its Applications in Extremely Uncertain Environments

Authors: Liguo Fei, Yong Hu, Yong Deng, Sankaran Mahadevan
Comments: 9 Pages.

Information fusion under extremely uncertain environments is an important issue in pattern classification and decision-making problem. Dempster-Shafer evidence theory (D-S theory) is more and more extensively applied to information fusion for its advantage to deal with uncertain information. However, the results opposite to common sense are often obtained when combining the different evidences using Dempster’s combination rules. How to measure the difference between different evidences is still an open issue. In this paper, a new divergence is proposed based on Kullback-Leibler divergence in order to measure the difference between different basic probability assignments (BPAs). Numerical examples are used to illustrate the computational process of the proposed divergence. Then the similarity for different BPAs is also defined based on the proposed divergence. The basic knowledge about pattern recognition is introduced and a new classification algorithm is presented using the proposed divergence and similarity under extremely uncertain environments, which is illustrated by a small example handling robot sensing. The method put forward is motivated by desperately in need to develop intelligent systems, such as sensor-based data fusion manipulators, which need to work in complicated, extremely uncertain environments. Sensory data satisfy the conditions 1) fragmentary and 2) collected from multiple levels of resolution.
Category: Artificial Intelligence

[408] viXra:1712.0444 [pdf] submitted on 2017-12-13 08:59:01

Environmental Impact Assessment Using D-Vikor Approach

Authors: Liguo Fei, Yong Deng
Comments: 15 Pages.

Environmental impact assessment (EIA) is an open and important issue depends on factors such as social, ecological, economic, etc. Due to human judgment, a variety of uncertainties are brought into the EIA process. With regard to uncertainty, many existing methods seem powerless to represent and deal with it effectively. A new theory called D numbers, because of its advantage to handle uncertain information, is widely used to uncertainty modeling and decision making. VIKOR method has its unique advantages in dealing with multiple criteria decision making problems (MCDM), especially when the criteria are non-commensurable and even conflicting, it can also obtain the compromised optimal solution. In order to solve EIA problems more effectively, in this paper, a D-VIKOR approach is proposed, which expends the VIKOR method by D numbers theory. In the proposed approach, assessment information of environmental factors is expressed and modeled by D numbers. And a new combination rule for multiple D numbers is defined. Subjective weights and objective weights are considered in VIKOR process for more reasonable ranking results. A numerical example is conducted to analyze and demonstrate the practicality and effectiveness of the proposed D-VIKOR approach.
Category: Artificial Intelligence

[407] viXra:1712.0439 [pdf] submitted on 2017-12-13 11:45:00

Large Scale Traffic Surveillance :Vehicle Detection and Classification Using Cascade Classifier and Convolutional Neural Network

Authors: Shaif Chowdhury
Comments: 8 Pages.

In this Paper, we are presenting a traffic surveillance system for detection and classification of vehicles in large scale videos. Vehicle detection is crucial part of Road safety. There are lots of different intelligent systems proposed for traffic surveillance. The system presented here is based on two steps, a descriptor of the image type haar-like, and a classifier type convolutional neural networks. A cascade classifier is used to extract objects rapidly and a neural network is used for final classification of cars. In case of Haar Cascades, the learning of the system is performed on a set of positive images (vehicles) and negative images (non-vehicle), and the test is done on another set of scenes. For the second, we have used faster R-CNN architecture. The cascade classifier gives faster processing time and Neural Network is used to increase the detection rate.
Category: Artificial Intelligence

[406] viXra:1712.0432 [pdf] submitted on 2017-12-13 22:28:48

DS-Vikor: a New Methodology for Supplier Selection

Authors: Liguo Fei, Yong Deng, Yong Hu
Comments: 15 Pages.

How to select the optimal supplier is an open and important issue in supply chain management (SCM), which needs to solve the problem of assessment and sorting the potential suppliers, and can be considered as a multi-criteria decision-making (MCDM) problem. Experts’ assessment play a very important role in the process of supplier selection, while the subjective judgment of human beings could introduce unpredictable uncertainty. However, existing methods seem powerless to represent and deal with this uncertainty effectively. Dempster-Shafer evidence theory (D- S theory) is widely used to uncertainty modeling, decision making and conflicts management due to its advantage to handle uncertain information. The VIKOR method has a great advantage to handle MCDM problems with non-commensurable and even conflicting criteria, and to obtain the compromised optimal solution. In this paper, a DS- VIKOR method is proposed for the supplier selection problem which expends the VIKOR method by D-S theory. In this method, the basic probability assignment (BPA) is used to denote the decision makers’ assessment for suppliers, Deng entropy weight-based method is defined and applied to determine the weights of multi-criteria, and VIKOR method is used for getting the final ranking results. An illustrative example under real life is conducted to analyze and demonstrate the practicality and effectiveness of the proposed DS-VIKOR method.
Category: Artificial Intelligence

[405] viXra:1712.0400 [pdf] submitted on 2017-12-13 06:52:57

Adaptively Evidential Weighted Classifier Combination

Authors: Liguo Fei, Bingyi Kang, Van-Nam Huynh, Yong Deng
Comments: 9 Pages.

Classifier combination plays an important role in classification. Due to the efficiency to handle and fuse uncertain information, Dempster-Shafer evidence theory is widely used in multi-classifiers fusion. In this paper, a method of adaptively evidential weighted classifier combination is presented. In our proposed method, the output of each classifier is modelled by basic probability assignment (BPA). Then, the weights are determined adaptively for individual classifier according to the uncertainty degree of the corresponding BPA. The uncertainty degree is measured by a belief entropy, named as Deng entropy. Discounting-and-combination scheme in D-S theory is used to calculate the weighted BPAs and combine them for the final BPA for classification. The effectiveness of the proposed weighted combination method is illustrated by numerical experimental results.
Category: Artificial Intelligence

[404] viXra:1712.0347 [pdf] submitted on 2017-12-07 09:10:57

Finding The Next Term Of Any Time Series Type Or Non Time Series Type Sequence Using Total Similarity & Dissimilarity {Version 6} ISSN 1751-3030.

Authors: Ramesh Chandra Bagadi
Comments: 3 Pages.

In this research investigation, the author has detailed a novel scheme of finding the next term of any given time series type or non-time series type sequence.
Category: Artificial Intelligence

[403] viXra:1712.0138 [pdf] submitted on 2017-12-05 14:07:08

Topological Clustering as a Method of Control for Certain Critical-Point Sensitive Systems

Authors: Martin J. Dudziiak
Comments: 6 Pages. submitted to CoDIT 2018 (Thessaloniki, Greece, April 2018)

New methods can provide more sensitive modeling and more reliable control, through use of dynamically-alterable local neighborhood clusters comprised of of the state-space parameters most disposed to be influential in non-linear systemic changes. Particular attention is directed to systems with extreme non-linearity and uncertainty in measurement and in control communications (e.g., micro-scalar, remote and inaccessible to real-time control). An architecture for modeling based upon topological similarity mapping principles is introduced as an alternative to classical Turing machine models including new “quantum computers.”
Category: Artificial Intelligence

[402] viXra:1712.0071 [pdf] submitted on 2017-12-03 19:12:51

The Intelligence Quotient of the Artificial Intelligence

Authors: Dimiter Dobrev
Comments: 15 Pages. Bulgarian. Serdica Journal of Computing.

To say which programs are AI, it's enough to run an exam and recognize for AI those programs that passed the exam. The exam grade will be called IQ. We cannot say just how big the IQ has to be in order one program to be AI, but we will choose one specific value. So our definition of AI will be any program whose IQ is above this specific value. This idea has already been realized in [1], but here we will repeat this construction by bringing some improvements.
Category: Artificial Intelligence

[401] viXra:1711.0477 [pdf] submitted on 2017-11-30 18:22:54

Okay, Google: a Preliminary Evaluation of the Robustness of Scholar Metrics

Authors: H Qadrawxu-Korbau, D Smith, K Beryllium
Comments: 4 Pages.

Google Scholar provides a number of metrics often used as proxies for scientific productivity. It is, however, possible to consciously manipulate Scholar metrics, for instance via copious self-citation or upload of fake papers to indexed websites. Here, we post a paper on vixra, a preprint forum, and arbitrarily cite a completely random study to evaluate whether Scholar will count this submission toward the overall citation count of that study. We publish no results, as the publication of the paper is, in this case, the experiment.
Category: Artificial Intelligence

[400] viXra:1711.0470 [pdf] submitted on 2017-11-30 02:13:24

Multi-Scalar Multi-Agent Control for Optimization of Dynamic Networks Operating in Remote Environment

Authors: Martin Dudziak
Comments: 7 Pages.

Multi-agent control systems have demonstrated effectiveness in a variety of physical applications including cooperative robot networks and multi-target tracking in high-noise network and group environments. We introduce the use of multi-scalar models that extend cellular automaton regional neighborhood comparisons and local voting measures based upon stochastic approximation in order to provide more efficient and time-sensitive solutions to non-deterministic problems. The scaling factors may be spatial, temporal or in other semantic values. The exercising of both cooperative and competitive functions by the devices in such networks offers a method for optimizing system parameters to reduce search, sorting, ranking and anomaly evaluation tasks. Applications are illustration for a group of robots assigned different tasks in remote operating environments with highly constrained communications and critical fail-safe conditions.
Category: Artificial Intelligence

[399] viXra:1711.0433 [pdf] submitted on 2017-11-26 23:19:36

Finding The Next Term Of Any Time Series Type Sequence Using Total Similarity & Dissimilarity {Version 5} ISSN 1751-3030

Authors: Ramesh Chandra Bagadi
Comments: 3 Pages.

In this research investigation, the author has detailed a novel scheme of finding the next term of any given time series type sequence.
Category: Artificial Intelligence

[398] viXra:1711.0429 [pdf] submitted on 2017-11-27 05:14:34

Finding The Next Term Of Any Sequence Using Total Similarity & Dissimilarity {Version 5}. ISSN 1751-3030

Authors: Ramesh Chandra Bagadi
Comments: 3 Pages.

In this research investigation, the author has detailed a novel scheme of finding the next term of any given sequence.
Category: Artificial Intelligence

[397] viXra:1711.0420 [pdf] submitted on 2017-11-26 01:39:24

Move the Tip to the Right a Language Based Computeranimation System in Box2d

Authors: Frank Schröder
Comments: 8 Pages.

Not only “robots need language”, but sometimes a human-operator too. To interact with complex domains, he needs a vocabulary to init the robot, let him walk and grasping objects. Natural language interfaces can support semi-autonomous and fully-autonomous systems on both sides. Instead of using neural networks, the language grounding problem can be solved with object-oriented programming. In the following paper a simulation of micro-manipulation under a microscope is given which is controlled with a C++ script. The small vocabulary consists of init, pregrasp, grasp and place.
Category: Artificial Intelligence

[396] viXra:1711.0382 [pdf] submitted on 2017-11-22 02:30:08

A Survey on Evolutionary Computation: Methods and Their Applications in Engineering

Authors: Morteza Husainy Yar, Vahid Rahmati, Hamid Reza Dalili Oskouei
Comments: 9 Pages.

Evolutionary computation is now an inseparable branch of artificial intelligence and smart methods based on evolutional algorithms aimed at solving different real world problems by natural procedures involving living creatures. It's based on random methods, regeneration of data, choosing by changing or replacing data within a system such as personal computer (PC), cloud, or any other data center. This paper briefly studies different evolutionary computation techniques used in some applications specifically image processing, cloud computing and grid computing. These methods are generally categorized as evolutionary algorithms and swarm intelligence. Each of these subfields contains a variety of algorithms and techniques which are presented with their applications. This work tries to demonstrate the benefits of the field by presenting the real world applications of these methods implemented already. Among these applications is cloud computing scheduling problem improved by genetic algorithms, ant colony optimization, and bees algorithm. Some other applications are improvement of grid load balancing, image processing, improved bi-objective dynamic cell formation problem, robust machine cells for dynamic part production, integrated mixed-integer linear programming, robotic applications, and power control in wind turbines.
Category: Artificial Intelligence

[395] viXra:1711.0370 [pdf] submitted on 2017-11-20 22:14:32

Finding The Next Term Of Any Given Sequence Using Total Similarity & Dissimilarity {Version 3} ISSN 1751-3030

Authors: Ramesh Chandra Bagadi
Comments: 1 Page.

In this research investigation, the author has detailed a novel scheme of finding the next term of any given sequence.
Category: Artificial Intelligence

[394] viXra:1711.0367 [pdf] submitted on 2017-11-21 00:18:32

One Step Evolution Of Any Real Positive Number {Version 2}

Authors: Ramesh Chandra Bagadi
Comments: 2 Pages.

In this research investigation, the author has detailed the Theory Of One Step Evolution Of Any Real Positive Number.
Category: Artificial Intelligence

[393] viXra:1711.0361 [pdf] submitted on 2017-11-20 02:12:39

Finding The Next Term Of Any Given Sequence Using Total Similarity & Dissimilarity. ISSN 1751-3030

Authors: Ramesh Chandra Bagadi
Comments: 1 Page.

In this research investigation, the author has detailed a novel scheme of finding the next term of any given sequence.
Category: Artificial Intelligence

[392] viXra:1711.0360 [pdf] submitted on 2017-11-20 02:43:10

Ontology Engineering for Robotics

Authors: Frank Schröder
Comments: 8 Pages.

Ontologies are a powerfull alternative to reinforcement learning. They store knowledge in a domain-specific language. The best-practice for implementing ontologies is a distributed version control system which is filled manually by programmers.
Category: Artificial Intelligence

[391] viXra:1711.0359 [pdf] submitted on 2017-11-20 05:21:55

Finding The Next Term Of Any Given Sequence Using Total Similarity & Dissimilarity {New} ISSN 1751-3030

Authors: Ramesh Chandra Bagadi
Comments: 1 Page.

In this research investigation, the author has detailed a novel scheme of finding the next term of any given sequence.
Category: Artificial Intelligence

[390] viXra:1711.0292 [pdf] submitted on 2017-11-12 09:29:57

Strengths and Potential of the SP Theory of Intelligence in General, Human-Like Artificial Intelligence

Authors: J Gerard Wolff
Comments: 20 Pages.

This paper first defines "general, human-like artificial intelligence" (GHLAI) in terms of five principles. In the light of the definition, the paper summarises the strengths and potential of the "SP theory of intelligence" and its realisation in the "computer model", outlined in an appendix, in three main areas: the versatility of the SP system in aspects of intelligence; its versatility in the representation of diverse kinds of knowledge; and its potential for the seamless integration of diverse aspects of intelligence and diverse kinds of knowledge, in any combination. There are reasons to believe that a mature version of the SP system may attain full GHLAI in diverse aspects of intelligence and in the representation of diverse kinds of knowledge.
Category: Artificial Intelligence

[389] viXra:1711.0266 [pdf] submitted on 2017-11-11 03:38:23

Revisit Fuzzy Neural Network: Demystifying Batch Normalization and ReLU with Generalized Hamming Network

Authors: Lixin Fan
Comments: 10 Pages. NIPS 2017 publication.

We revisit fuzzy neural network with a cornerstone notion of generalized hamming distance, which provides a novel and theoretically justified framework to re-interpret many useful neural network techniques in terms of fuzzy logic. In particular, we conjecture and empirically illustrate that, the celebrated batch normalization (BN) technique actually adapts the “normalized” bias such that it approximates the rightful bias induced by the generalized hamming distance. Once the due bias is enforced analytically, neither the optimization of bias terms nor the sophisticated batch normalization is needed. Also in the light of generalized hamming distance, the popular rectified linear units (ReLU) can be treated as setting a minimal hamming distance threshold between network inputs and weights. This thresholding scheme, on the one hand, can be improved by introducing double-thresholding on both positive and negative extremes of neuron outputs. On the other hand, ReLUs turn out to be non-essential and can be removed from networks trained for simple tasks like MNIST classification. The proposed generalized hamming network (GHN) as such not only lends itself to rigorous analysis and interpretation within the fuzzy logic theory but also demonstrates fast learning speed, well-controlled behaviour and state-of-the-art performances on a variety of learning tasks.
Category: Artificial Intelligence

[388] viXra:1711.0265 [pdf] submitted on 2017-11-11 04:14:07

Revisit Fuzzy Neural Network: Bridging the Gap Between Fuzzy Logic and Deep Learning

Authors: Lixin Fan
Comments: 75 Pages. bridging the gap between symbolic versus connectionist.

This article aims to establish a concrete and fundamental connection between two important fields in artificial intelligence i.e. deep learning and fuzzy logic. On the one hand, we hope this article will pave the way for fuzzy logic researchers to develop convincing applications and tackle challenging problems which are of interest to machine learning community too. On the other hand, deep learning could benefit from the comparative research by re-examining many trail-and-error heuristics in the lens of fuzzy logic, and consequently, distilling the essential ingredients with rigorous foundations. Based on the new findings reported in [38] and this article, we believe the time is ripe to revisit fuzzy neural network as a crucial bridge between two schools of AI research i.e. symbolic versus connectionist [93] and eventually open the black-box of artificial neural networks.
Category: Artificial Intelligence

[387] viXra:1711.0250 [pdf] submitted on 2017-11-08 06:37:55

Total Intra Similarity And Dissimilarity Measure For The Values Taken By A Parameter Of Concern. {Version 1}. ISSN 1751-3030

Authors: Ramesh Chandra Bagadi
Comments: 3 Pages.

In this research investigation, the author has detailed a novel method of finding the ‘Total Intra Similarity And Dissimilarity Measure For The Values Taken By A Parameter Of Concern’. The advantage of such a measure is that using this measure we can clearly distinguish the contribution of Intra aspect variation and Inter aspect variation when both are bound to occur in a given phenomenon of concern. This measure provides the same advantages as that provided by the popular F-Statistic measure.
Category: Artificial Intelligence

[386] viXra:1711.0241 [pdf] submitted on 2017-11-07 03:26:43

Dysfunktionale Methoden der Robotik

Authors: Frank Schröder
Comments: 8 Pages. German

Bei der Realisierung von Robotik-Projekten kann man eine ganze Menge verkehrt machen. Damit sind nicht nur kalte Lötstellen oder abstürzende Software gemeint, sondern sehr viel grundsätzlichere Dinge spielen eine Rolle. Um Fehler zu vermeiden, muss man sich zunächst einmal mit den Failure-Patterns näher auseinandersetzen, also jenen Entwicklungsmethoden, nach denen man auf gar keinen Fall einen Roboter bauen und wie die Software möglichst nicht funktionieren sollte.
Category: Artificial Intelligence

[385] viXra:1711.0235 [pdf] submitted on 2017-11-06 20:27:28

Not Merely Memorization in Deep Networks: Universal Fitting and Specific Generalization

Authors: Xiuyi Yang
Comments: 7 Pages.

We reinterpret the training of convolutional neural nets(CNNs) with universal classification theorem(UCT). This theory implies any disjoint datasets can be classified by two or more layers of CNNs based on ReLUs and rigid transformation switch units(RTSUs) we propose here, this explains why CNNs could memorize noise and real data. Subsequently, we present another fresh new hypothesis that CNN is insensitive to some variant from input training data example, this variant relates to original training input by generating functions. This hypothesis means CNNs can generalize well even for randomly generated training data and illuminates the paradox Why CNNs fit real and noise data and fail drastically when making predictions for noise data. Our findings suggest the study about generalization theory of CNNs should turn to generating functions instead of traditional statistics machine learning theory based on assumption that the training data and testing data are independent and identically distributed(IID), and apparently IID assumption contradicts our experiments in this paper.We experimentally verify these ideas correspondingly.
Category: Artificial Intelligence

[384] viXra:1711.0226 [pdf] submitted on 2017-11-07 01:52:12

Theory Of Universal Evolution Along Prime Basis (Time Like) ISSN 1751-3030.

Authors: Ramesh Chandra Bagadi
Comments: 2 Pages.

In this research investigation, the author has detailed the Theory Of Evolution.
Category: Artificial Intelligence

[383] viXra:1711.0208 [pdf] submitted on 2017-11-07 02:22:45

Theory Of Universal Evolution Along Prime Basis (Time Like) {Version 2} ISSN 1751-3030.

Authors: Ramesh Chandra Bagadi
Comments: 2 Pages.

In this research investigation, the author has detailed the Theory Of Evolution.
Category: Artificial Intelligence

[382] viXra:1711.0116 [pdf] submitted on 2017-11-02 23:51:41

Dynamic Thresholding For Linear Binary Classifiers. {Version 2} ISSN 1751-3030

Authors: Ramesh Chandra Bagadi
Comments: 3 Pages.

In this research investigation, the author has detailed a novel method of finding the Thresholding for Linear Binary Classifiers.
Category: Artificial Intelligence

[381] viXra:1711.0034 [pdf] submitted on 2017-11-02 06:05:21

Dynamic Thresholding For Linear Binary Classifiers. ISSN 1751-3030

Authors: Ramesh Chandra Bagadi
Comments: 2 Pages.

In this research investigation, the author has detailed a novel method of finding the Thresholding for Linear Binary Classifiers.
Category: Artificial Intelligence

[380] viXra:1710.0336 [pdf] submitted on 2017-10-31 23:50:38

Scheme For Finding The Next Term Of A Sequence Based On Evolution. {Version 7}. ISSN 1751-3030

Authors: Ramesh Chandra Bagadi
Comments: 7 Pages.

In this research investigation, the author has detailed a novel method of finding the next term of a sequence based on Evolution.
Category: Artificial Intelligence

[379] viXra:1710.0299 [pdf] submitted on 2017-10-27 04:13:49

Scheme For Finding The Next Term Of A Sequence Based On Evolution. {Version 6}. ISSN 1751-3030

Authors: Ramesh Chandra Bagadi
Comments: 6 Pages.

In this research investigation, the author has detailed a novel method of finding the next term of a sequence based on Evolution.
Category: Artificial Intelligence

[378] viXra:1710.0297 [pdf] submitted on 2017-10-25 03:57:32

Scheme For Finding The Next Term Of A Sequence Based On Evolution {File Closing Version 2}. ISSN 1751-3030

Authors: Ramesh Chandra Bagadi
Comments: 5 Pages.

In this research investigation, the author has detailed a novel method of finding the next term of a sequence based on Evolution.
Category: Artificial Intelligence

[377] viXra:1710.0294 [pdf] submitted on 2017-10-25 23:47:37

Scheme For Finding The Next Term Of A Sequence Based On Evolution {File Closing Version 3}. ISSN 1751-3030

Authors: Ramesh Chandra Bagadi
Comments: 5 Pages.

In this research investigation, the author has detailed a novel method of finding the next term of a sequence based on Evolution.
Category: Artificial Intelligence

[376] viXra:1710.0293 [pdf] submitted on 2017-10-26 01:24:46

Scheme For Finding The Next Term Of A Sequence Based On Evolution {File Closing Version 4}. ISSN 1751-3030

Authors: Ramesh Chandra Bagadi
Comments: 6 Pages.

In this research investigation, the author has detailed a novel method of finding the next term of a sequence based on Evolution.
Category: Artificial Intelligence

[375] viXra:1710.0289 [pdf] submitted on 2017-10-26 03:56:28

Scheme For Finding The Next Term Of A Sequence Based On Evolution {File Closing Version 5}. ISSN 1751-3030

Authors: Ramesh Chandra Bagadi
Comments: 6 Pages.

In this research investigation, the author has detailed a novel method of finding the next term of a sequence based on Evolution.
Category: Artificial Intelligence

[374] viXra:1710.0279 [pdf] submitted on 2017-10-24 04:45:19

Scheme For Finding The Next Term Of A Sequence Based On Evolution {File Closing Version 1}. ISSN 1751-3030

Authors: Ramesh Chandra Bagadi
Comments: 3 Pages.

In this research investigation, the author has detailed a novel method of finding the next term of a sequence based on Evolution.
Category: Artificial Intelligence

[373] viXra:1710.0271 [pdf] submitted on 2017-10-23 23:14:04

The Average Computed In Primes Basis {File Closing Version 2}. ISSN 1751-3030

Authors: Ramesh Chandra Bagadi
Comments: 2 Pages.

In this research investigation, the author has detailed a novel method of finding the average of a sequence in Primes Basis.
Category: Artificial Intelligence

[372] viXra:1710.0267 [pdf] submitted on 2017-10-23 06:21:13

The Average Computed In Primes Basis {File Closing Version 1}. ISSN 1751-3030

Authors: Ramesh Chandra Bagadi
Comments: 2 Pages.

In this research investigation, the author has detailed a novel method of finding the average of a sequence in Primes Basis.
Category: Artificial Intelligence

[371] viXra:1710.0259 [pdf] submitted on 2017-10-23 00:38:01

Universe’s Way Of Recursively Finding The Next Term Of Any Sequence {File Closing Version 3}. ISSN 1751-3030

Authors: Ramesh Chandra Bagadi
Comments: 3 Pages.

In this research investigation, the author has detailed a novel method of Universe’s Way Of Recursively Finding The Next Term Of Any Sequence.
Category: Artificial Intelligence

[370] viXra:1710.0208 [pdf] submitted on 2017-10-18 23:07:44

The Recursive Future Equation Based On The Ananda-Damayanthi Normalized Similarity Measure. {File Closing Version 4}. ISSN 1751-3030

Authors: Ramesh Chandra Bagadi
Comments: 4 Pages.

In this research Technical Note the author have presented a Recursive Future Average Of A Time Series Data Based on Cosine Similarity.
Category: Artificial Intelligence

[369] viXra:1710.0141 [pdf] submitted on 2017-10-12 10:42:50

Miguel A. Sanchez-Rey

Authors: Advances in the Collective Interface
Comments: 5 Pages.

A byproduct of 2AI.
Category: Artificial Intelligence

[368] viXra:1710.0003 [pdf] submitted on 2017-10-01 06:54:11

Nature-Like Technology for Communication Network Selfactualization in the Mode Advancing Real-Time

Authors: Popov Boris
Comments: 7 Pages.

In order to provide control system operation in real-time mode, communication system should operate in the mode advancing real-time that can be achieved only by means of providing the communication system with mechanism for network structure forward adaptation to the variations in the user query topics and rates as well as their self-actualization. A technique for developing such nature-like technology that is based on fundamental natural inertia phenomenon and widespread symbiotic cooperation, distinguished by building-up (developing) resources being used, is proposed.
Category: Artificial Intelligence

[367] viXra:1709.0404 [pdf] submitted on 2017-09-26 13:26:47

A Suggestion on CLIPS/JIProlog/JNNS/ImageJ/Java Agents/JikesRVM Based Analysis of Cryo-EM/TEM/SEM Images Using HDF5 Image Format – Some Interesting & Feasible Implementations of Expert Systems to Understand Nano- Bio Material Systems and EM

Authors: D.N.T.Kumar
Comments: 7 Pages. Prolog/NN/Expert Systems/JikesRVM/Informatics/EM/Cryo-EM/TEM/SEM/Material Science/Java Agents/Nanotechnology.

In this short communication the importance of expert systems based imaging framework to probe Cryo-EM images is presented from a practical implementation point of view. Neural Networks or NN are an excellent tool to probe various domains of science and technology. Cryo-EM Technique holds bright future based on the application of NN.Prolog-NN based algorithms could form a powerful informatics and computational framework for researching the challenges of nano-bio Applications. Further,it is useful and important to study the behavior of NN in domains where knowledge does not exist, i.e to use the models to make bold predictions which form the basis for Cryo-EM Image Processing tasks and the discovery of new nano-bio phenomena.Indeed, the performance of NN is most useful to researchers in domains where the modeling and predicting “uncertainty” is known to be the greatest factor. All the methods presented here are also applicable to TEM/SEM/other EM Image Processing tasks as well.
Category: Artificial Intelligence

[366] viXra:1709.0403 [pdf] submitted on 2017-09-26 13:33:02

Kernel Principal Component Analysis as Mathematical Tool In Processing Cryo- EM Images – A Suggestion Using Kernel Based Data Processing Techniques in a Java Virtual Machine(JVM) Environment.

Authors: D.N.T.Kumar
Comments: 7 Pages. A Suggestion Using Kernel Based Data Processing Techniques in a Java Virtual Machine(JVM) Environment.

In this short communication,it was proposed to highlight some novel methodologies to probe,process and compute Cryo-EM Images in a unique way by using an open source Kernel-PCA and by interfacing the KERNEL-PCA via Java Matlab Interface(JMI) – JikesRVM system or any other Java Virtual Machine(JVM).The main reason to design and develop this kind of computing approach is to utilize the features of Java based technologies for futuristic applications in the promising and demanding domains of CRYO-EM Imaging in the nano-bio domains.This is one of the pioneering research topics in this domain with a lot of promise.Image de-noising and novelty detection paves the way and holds the key for better Cryo-EM image processing.
Category: Artificial Intelligence

[365] viXra:1709.0394 [pdf] submitted on 2017-09-26 11:50:52

How Does the ai Understand What's Going on

Authors: Dimiter Dobrev
Comments: 22 Pages.

Most researchers regard AI as a static function without memory. This is one of the few articles where AI is seen as a device with memory. When we have memory, we can ask ourselves: "Where am I?", and "What is going on?" When we have no memory, we have to assume that we are always in the same place and that the world is always in the same state.
Category: Artificial Intelligence

[364] viXra:1709.0323 [pdf] submitted on 2017-09-21 05:35:00

Recursive Future Average Of A Time Series Data Based On Cosine Similarity-RF

Authors: Ramesh Chandra Bagadi
Comments: 2 Pages.

In this research Technical Note the author have presented a Recursive Future Average Of A Time Series Data Based on Cosine Similarity.
Category: Artificial Intelligence

[363] viXra:1709.0322 [pdf] submitted on 2017-09-21 05:49:31

Recursive Future Average Of A Time Series Data Based On Cosine Similarity-RF {Version 2}

Authors: Ramesh Chandra Bagadi
Comments: 2 Pages.

In this research Technical Note the author have presented a Recursive Future Average Of A Time Series Data Based on Cosine Similarity.
Category: Artificial Intelligence

[362] viXra:1709.0313 [pdf] submitted on 2017-09-22 00:01:00

The Recursive Future Equation Based On The Ananda-Damayanthi Normalized Similarity Measure. {File Closing Version 2}. ISSN 1751-3030

Authors: Ramesh Chandra Bagadi
Comments: 3 Pages.

In this research Technical Note the author have presented a Recursive Future Average Of A Time Series Data Based on Cosine Similarity.
Category: Artificial Intelligence

[361] viXra:1709.0242 [pdf] submitted on 2017-09-15 20:34:58

Exact Map Inference in General Higher-Order Graphical Models Using Linear Programming

Authors: Ikhlef Bechar
Comments: 50 Pages.

This paper is concerned with the problem of exact MAP inference in general higher-order graphical models by means of a traditional linear programming relaxation approach. In fact, the proof that we have developed in this paper is a rather simple algebraic proof being made straightforward, above all, by the introduction of two novel algebraic tools. Indeed, on the one hand, we introduce the notion of delta-distribution which merely stands for the difference of two arbitrary probability distributions, and which mainly serves to alleviate the sign constraint inherent to a traditional probability distribution. On the other hand, we develop an approximation framework of general discrete functions by means of an orthogonal projection expressing in terms of linear combinations of function margins with respect to a given collection of point subsets, though, we rather exploit the latter approach for the purpose of modeling locally consistent sets of discrete functions from a global perspective. After that, as a first step, we develop from scratch the expectation optimization framework which is nothing else than a reformulation, on stochastic grounds, of the convex-hull approach, as a second step, we develop the traditional LP relaxation of such an expectation optimization approach, and we show that it enables to solve the MAP inference problem in graphical models under rather general assumptions. Last but not least, we describe an algorithm which allows to compute an exact MAP solution from a perhaps fractional optimal (probability) solution of the proposed LP relaxation.
Category: Artificial Intelligence

[360] viXra:1709.0217 [pdf] submitted on 2017-09-14 08:11:16

Quantum Thinking Machines

Authors: George Rajna
Comments: 24 Pages.

Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning. [16] Neural networks learn how to carry out certain tasks by analyzing large amounts of data displayed to them. [15] Who is the better experimentalist, a human or a robot? When it comes to exploring synthetic and crystallization conditions for inorganic gigantic molecules, actively learning machines are clearly ahead, as demonstrated by British Scientists in an experiment with polyoxometalates published in the journal Angewandte Chemie. [14] Machine learning algorithms are designed to improve as they encounter more data, making them a versatile technology for understanding large sets of photos such as those accessible from Google Images. Elizabeth Holm, professor of materials science and engineering at Carnegie Mellon University, is leveraging this technology to better understand the enormous number of research images accumulated in the field of materials science. [13] With the help of artificial intelligence, chemists from the University of Basel in Switzerland have computed the characteristics of about two million crystals made up of four chemical elements. The researchers were able to identify 90 previously unknown thermodynamically stable crystals that can be regarded as new materials. [12] The artificial intelligence system's ability to set itself up quickly every morning and compensate for any overnight fluctuations would make this fragile technology much more useful for field measurements, said co-lead researcher Dr Michael Hush from UNSW ADFA. [11] Quantum physicist Mario Krenn and his colleagues in the group of Anton Zeilinger from the Faculty of Physics at the University of Vienna and the Austrian Academy of Sciences have developed an algorithm which designs new useful quantum experiments. As the computer does not rely on human intuition, it finds novel unfamiliar solutions. [10] Researchers at the University of Chicago's Institute for Molecular Engineering and the University of Konstanz have demonstrated the ability to generate a quantum logic operation, or rotation of the qubit, that-surprisingly—is intrinsically resilient to noise as well as to variations in the strength or duration of the control. Their achievement is based on a geometric concept known as the Berry phase and is implemented through entirely optical means within a single electronic spin in diamond. [9]
Category: Artificial Intelligence

[359] viXra:1709.0211 [pdf] submitted on 2017-09-14 06:46:27

Analyzing Huge Volumes of Data

Authors: George Rajna
Comments: 23 Pages.

Neural networks learn how to carry out certain tasks by analyzing large amounts of data displayed to them. [15] Who is the better experimentalist, a human or a robot? When it comes to exploring synthetic and crystallization conditions for inorganic gigantic molecules, actively learning machines are clearly ahead, as demonstrated by British Scientists in an experiment with polyoxometalates published in the journal Angewandte Chemie. [14] Machine learning algorithms are designed to improve as they encounter more data, making them a versatile technology for understanding large sets of photos such as those accessible from Google Images. Elizabeth Holm, professor of materials science and engineering at Carnegie Mellon University, is leveraging this technology to better understand the enormous number of research images accumulated in the field of materials science. [13] With the help of artificial intelligence, chemists from the University of Basel in Switzerland have computed the characteristics of about two million crystals made up of four chemical elements. The researchers were able to identify 90 previously unknown thermodynamically stable crystals that can be regarded as new materials. [12] The artificial intelligence system's ability to set itself up quickly every morning and compensate for any overnight fluctuations would make this fragile technology much more useful for field measurements, said co-lead researcher Dr Michael Hush from UNSW ADFA. [11] Quantum physicist Mario Krenn and his colleagues in the group of Anton Zeilinger from the Faculty of Physics at the University of Vienna and the Austrian Academy of Sciences have developed an algorithm which designs new useful quantum experiments. As the computer does not rely on human intuition, it finds novel unfamiliar solutions. [10] Researchers at the University of Chicago's Institute for Molecular Engineering and the University of Konstanz have demonstrated the ability to generate a quantum logic operation, or rotation of the qubit, that - surprisingly—is intrinsically resilient to noise as well as to variations in the strength or duration of the control. Their achievement is based on a geometric concept known as the Berry phase and is implemented through entirely optical means within a single electronic spin in diamond. [9]
Category: Artificial Intelligence

[358] viXra:1709.0161 [pdf] submitted on 2017-09-13 10:30:45

AI is Reinforcing Stereotypes

Authors: George Rajna
Comments: 46 Pages.

Following the old saying that "knowledge is power", companies are seeking to infer increasingly intimate properties about their customers as a way to gain an edge over their competitors. [27] Researchers from Human Longevity, Inc. (HLI) have published a study in which individual faces and other physical traits were predicted using whole genome sequencing data and machine learning. [26] Artificial intelligence can improve health care by analyzing data from apps, smartphones and wearable technology. [25] Now, researchers at Google's DeepMind have developed a simple algorithm to handle such reasoning—and it has already beaten humans at a complex image comprehension test. [24] A marimba-playing robot with four arms and eight sticks is writing and playing its own compositions in a lab at the Georgia Institute of Technology. The pieces are generated using artificial intelligence and deep learning. [23] Now, a team of researchers at MIT and elsewhere has developed a new approach to such computations, using light instead of electricity, which they say could vastly improve the speed and efficiency of certain deep learning computations. [22] Physicists have found that the structure of certain types of quantum learning algorithms is very similar to their classical counterparts—a finding that will help scientists further develop the quantum versions. [21] We should remain optimistic that quantum computing and AI will continue to improve our lives, but we also should continue to hold companies, organizations, and governments accountable for how our private data is used, as well as the technology's impact on the environment. [20] It's man vs machine this week as Google's artificial intelligence programme AlphaGo faces the world's top-ranked Go player in a contest expected to end in another victory for rapid advances in AI. [19] Google's computer programs are gaining a better understanding of the world, and now it wants them to handle more of the decision-making for the billions of people who use its services. [18] Microsoft on Wednesday unveiled new tools intended to democratize artificial intelligence by enabling machine smarts to be built into software from smartphone games to factory floors. [17]
Category: Artificial Intelligence

[357] viXra:1709.0159 [pdf] submitted on 2017-09-13 06:47:26

Mergeable Nervous Robots

Authors: George Rajna
Comments: 49 Pages.

Researchers at the Université libre de Bruxelles have developed self-reconfiguring modular robots that can merge, split and even self-heal while retaining full sensorimotor control. [29] A challenging brain technique called whole-cell patch clamp electrophysiology or whole-cell recording (WCR) is a procedure so delicate and complex that only a handful of humans in the whole world can do it. [28] ComText allows robots to understand contextual commands such as, " Pick up the box I put down. " [27] McMaster and Ryerson universities today announced the Smart Robots for Health Communication project, a joint research initiative designed to introduce social robotics and artificial intelligence into clinical health care. [26] Artificial intelligence can improve health care by analyzing data from apps, smartphones and wearable technology. [25] Now, researchers at Google's DeepMind have developed a simple algorithm to handle such reasoning—and it has already beaten humans at a complex image comprehension test. [24] A marimba-playing robot with four arms and eight sticks is writing and playing its own compositions in a lab at the Georgia Institute of Technology. The pieces are generated using artificial intelligence and deep learning. [23] Now, a team of researchers at MIT and elsewhere has developed a new approach to such computations, using light instead of electricity, which they say could vastly improve the speed and efficiency of certain deep learning computations. [22] Physicists have found that the structure of certain types of quantum learning algorithms is very similar to their classical counterparts—a finding that will help scientists further develop the quantum versions. [21] We should remain optimistic that quantum computing and AI will continue to improve our lives, but we also should continue to hold companies, organizations, and governments accountable for how our private data is used, as well as the technology's impact on the environment. [20] It's man vs machine this week as Google's artificial intelligence programme AlphaGo faces the world's top-ranked Go player in a contest expected to end in another victory for rapid advances in AI. [19]
Category: Artificial Intelligence

[356] viXra:1709.0142 [pdf] submitted on 2017-09-11 20:53:40

Brain Emotional Learning Based Intelligent Controller for Velocity Control of an Electro Hydraulic Servo System

Authors: Zohreh Alzahra Sanai Dashti, Milad Gholami, Masoud Hajimani
Comments: 7 Pages. IOSR Journal of Electrical and Electronics Engineering (IOSR - JEEE) e - ISSN: 2278 - 1676,p - ISSN: 2320 - 3331, Volume 12, Issue 4 Ver. I I (Jul. – Aug. 2017), PP 29 - 35

In this paper, a biologically motivated controller based on mammalian limbic system called Brain Emotional Learning Based Intelligent Controller (BELBIC) is used for velocity control of an Electro Hydraulic Servo System (EHSS) in presence of flow nonlinearities, internal friction and noise. It is shown that this technique can be successfully used to stabilize any chosen operating point of the system with noise and without noise. All derived results are validated by computer simulation of a nonlinear mathematical model of the system. The controllers which introduced have big range for control the system. We compare BELBIC controller results with feedbacks linearization, backstepping and PID controller.
Category: Artificial Intelligence

[355] viXra:1709.0141 [pdf] submitted on 2017-09-11 20:56:32

Design & Implementation of Fuzzy Parallel Distributed Compensation Controller for Magnetic Levitation System

Authors: Milad Gholami, Zohreh Alzahra Sanai Dashti, Masoud Hajimani
Comments: 9 Pages. IOSR Journal of Electrical and Electronics Engineering (IOSR - JEEE) e - ISSN : 2278 - 1676,p - ISSN: 2320 - 3331, Volume 12, Issue 4 Ver. I I (Jul. – Aug. 2017), PP 20 - 28

This study applies technique parallel distributed compensation (PDC) for position control of a Magnetic levitation system. PDC method is based on nonlinear Takagi-Sugeno (T-S) fuzzy model. It is shown that this technique can be successfully used to stabilize any chosen operating point of the system. All derived results are validated by experimental and computer simulation of a nonlinear mathematical model of the system. The controllers which introduced have big range for control the system.
Category: Artificial Intelligence

[354] viXra:1709.0125 [pdf] submitted on 2017-09-11 07:51:38

Machine Learning Monitoring Air Quality

Authors: George Rajna
Comments: 23 Pages.

UCLA researchers have developed a cost-effective mobile device to measure air quality. It works by detecting pollutants and determining their concentration and size using a mobile microscope connected to a smartphone and a machine-learning algorithm that automatically analyzes the images of the pollutants. [15] Who is the better experimentalist, a human or a robot? When it comes to exploring synthetic and crystallization conditions for inorganic gigantic molecules, actively learning machines are clearly ahead, as demonstrated by British Scientists in an experiment with polyoxometalates published in the journal Angewandte Chemie. [14] Machine learning algorithms are designed to improve as they encounter more data, making them a versatile technology for understanding large sets of photos such as those accessible from Google Images. Elizabeth Holm, professor of materials science and engineering at Carnegie Mellon University, is leveraging this technology to better understand the enormous number of research images accumulated in the field of materials science. [13] With the help of artificial intelligence, chemists from the University of Basel in Switzerland have computed the characteristics of about two million crystals made up of four chemical elements. The researchers were able to identify 90 previously unknown thermodynamically stable crystals that can be regarded as new materials. [12] The artificial intelligence system's ability to set itself up quickly every morning and compensate for any overnight fluctuations would make this fragile technology much more useful for field measurements, said co-lead researcher Dr Michael Hush from UNSW ADFA. [11]
Category: Artificial Intelligence

[353] viXra:1709.0108 [pdf] submitted on 2017-09-10 06:02:53

A New Semantic Theory of Nature Language

Authors: Kun Xing
Comments: 70 Pages.

Formal Semantics and Distributional Semantics are two important semantic frameworks in Natural Language Processing (NLP). Cognitive Semantics belongs to the movement of Cognitive Linguistics, which is based on contemporary cognitive science. Each framework could deal with some meaning phenomena, but none of them fulfills all requirements proposed by applications. A unified semantic theory characterizing all important language phenomena has both theoretical and practical significance; however, although many attempts have been made in recent years, no existing theory has achieved this goal yet. This article introduces a new semantic theory that has the potential to characterize most of the important meaning phenomena of natural language and to fulfill most of the necessary requirements for philosophical analysis and for NLP applications. The theory is based on a unified representation of information, and constructs a kind of mathematical model called cognitive model to interpret natural language expressions in a compositional manner. It accepts the empirical assumption of Cognitive Semantics, and overcomes most shortcomings of Formal Semantics and of Distributional Semantics. The theory, however, is not a simple combination of existing theories, but an extensive generalization of classic logic and Formal Semantics. It inherits nearly all advantages of Formal Semantics, and also provides descriptive contents for objects and events as fine-gram as possible, descriptive contents which represent the results of human cognition.
Category: Artificial Intelligence

[352] viXra:1709.0096 [pdf] submitted on 2017-09-08 13:34:21

Robots Understand Brain Function

Authors: George Rajna
Comments: 48 Pages.

A challenging brain technique called whole-cell patch clamp electrophysiology or whole-cell recording (WCR) is a procedure so delicate and complex that only a handful of humans in the whole world can do it. [28] ComText allows robots to understand contextual commands such as, " Pick up the box I put down. " [27] McMaster and Ryerson universities today announced the Smart Robots for Health Communication project, a joint research initiative designed to introduce social robotics and artificial intelligence into clinical health care. [26] Artificial intelligence can improve health care by analyzing data from apps, smartphones and wearable technology. [25] Now, researchers at Google's DeepMind have developed a simple algorithm to handle such reasoning—and it has already beaten humans at a complex image comprehension test. [24] A marimba-playing robot with four arms and eight sticks is writing and playing its own compositions in a lab at the Georgia Institute of Technology. The pieces are generated using artificial intelligence and deep learning. [23] Now, a team of researchers at MIT and elsewhere has developed a new approach to such computations, using light instead of electricity, which they say could vastly improve the speed and efficiency of certain deep learning computations. [22] Physicists have found that the structure of certain types of quantum learning algorithms is very similar to their classical counterparts—a finding that will help scientists further develop the quantum versions. [21] We should remain optimistic that quantum computing and AI will continue to improve our lives, but we also should continue to hold companies, organizations, and governments accountable for how our private data is used, as well as the technology's impact on the environment. [20] It's man vs machine this week as Google's artificial intelligence programme AlphaGo faces the world's top-ranked Go player in a contest expected to end in another victory for rapid advances in AI. [19] Google's computer programs are gaining a better understanding of the world, and now it wants them to handle more of the decision-making for the billions of people who use its services. [18]
Category: Artificial Intelligence

[351] viXra:1709.0068 [pdf] submitted on 2017-09-06 07:16:28

Identification of Individuals

Authors: George Rajna
Comments: 44 Pages.

Researchers from Human Longevity, Inc. (HLI) have published a study in which individual faces and other physical traits were predicted using whole genome sequencing data and machine learning. [26] Artificial intelligence can improve health care by analyzing data from apps, smartphones and wearable technology. [25] Now, researchers at Google's DeepMind have developed a simple algorithm to handle such reasoning—and it has already beaten humans at a complex image comprehension test. [24] A marimba-playing robot with four arms and eight sticks is writing and playing its own compositions in a lab at the Georgia Institute of Technology. The pieces are generated using artificial intelligence and deep learning. [23] Now, a team of researchers at MIT and elsewhere has developed a new approach to such computations, using light instead of electricity, which they say could vastly improve the speed and efficiency of certain deep learning computations. [22] Physicists have found that the structure of certain types of quantum learning algorithms is very similar to their classical counterparts—a finding that will help scientists further develop the quantum versions. [21] We should remain optimistic that quantum computing and AI will continue to improve our lives, but we also should continue to hold companies, organizations, and governments accountable for how our private data is used, as well as the technology's impact on the environment. [20] It's man vs machine this week as Google's artificial intelligence programme AlphaGo faces the world's top-ranked Go player in a contest expected to end in another victory for rapid advances in AI. [19] Google's computer programs are gaining a better understanding of the world, and now it wants them to handle more of the decision-making for the billions of people who use its services. [18] Microsoft on Wednesday unveiled new tools intended to democratize artificial intelligence by enabling machine smarts to be built into software from smartphone games to factory floors. [17] The closer we can get a machine translation to be on par with expert human translation, the happier lots of people struggling with translations will be. [16]
Category: Artificial Intelligence

[350] viXra:1709.0048 [pdf] submitted on 2017-09-05 04:26:06

On the Dual Nature of Logical Variables and Clause-Sets

Authors: Elnaserledinellah Mahmood Abdelwahab
Comments: © 2016 Journal Academica Foundation. All rights reserved. With perpetual, non-exclusive license for viXra.org - Originally received 08-04-2016 - accepted 09-12-2016 - published 09-15-2016 J.Acad.(N.Y.)6,3:202-239 (38 pages) - ISSN 2161-3338

This paper describes the conceptual approach behind the proposed solution of the 3SAT problem recently published in [Abdelwahab 2016]. It is intended for interested readers providing a step-by-step, mostly informal explanation of the new paradigm proposed there completing the picture from an epistemological point of view with the concept of duality on center-stage. After a brief introduction discussing the importance of duality in both, physics and mathematics as well as past efforts to solve the P vs. NP problem, a theorem is proven showing that true randomness of input-variables is a property of algorithms which has to be given up when discrete, finite domains are considered. This insight has an already known side effect on computation paradigms, namely: The ability to de-randomize probabilistic algorithms. The theorem uses a canonical type of de-randomization which reveals dual properties of logical variables and Clause-Sets. A distinction is made between what we call the syntactical Container Expression (CE) and the semantic Pattern Expression (PE). A single sided approach is presumed to be insufficient to solve anyone of the dual problems of efficiently finding an assignment validating a 3CNF Clause-Set and finding a 3CNF-representation for a given semantic pattern. The deeply rooted reason, hereafter referred to as The Inefficiency Principle, is conjectured to be the inherent difficulty of translating one expression into the other based on a single-sided perspective. It expresses our inability to perceive and efficiently calculate complementary properties of a logical formula applying one view only. It is proposed as an alternative to the commonly accepted P≠NP conjecture. On the other hand, the idea of algorithmically using information deduced from PE to guide the instantiation of variables in a resolution procedure applied on a CE is as per [Abdelwahab 2016] able to provide an efficient solution to the 3SAT-problem. Finally, linking de-randomization to this positive solution has various well-established and important consequences for probabilistic complexity classes which are shown to hold.
Category: Artificial Intelligence

[349] viXra:1709.0019 [pdf] submitted on 2017-09-02 07:01:40

Understanding Robots

Authors: George Rajna
Comments: 46 Pages.

ComText allows robots to understand contextual commands such as, " Pick up the box I put down. " [27] McMaster and Ryerson universities today announced the Smart Robots for Health Communication project, a joint research initiative designed to introduce social robotics and artificial intelligence into clinical health care. [26] Artificial intelligence can improve health care by analyzing data from apps, smartphones and wearable technology. [25] Now, researchers at Google's DeepMind have developed a simple algorithm to handle such reasoning—and it has already beaten humans at a complex image comprehension test. [24] A marimba-playing robot with four arms and eight sticks is writing and playing its own compositions in a lab at the Georgia Institute of Technology. The pieces are generated using artificial intelligence and deep learning. [23] Now, a team of researchers at MIT and elsewhere has developed a new approach to such computations, using light instead of electricity, which they say could vastly improve the speed and efficiency of certain deep learning computations. [22] Physicists have found that the structure of certain types of quantum learning algorithms is very similar to their classical counterparts—a finding that will help scientists further develop the quantum versions. [21] We should remain optimistic that quantum computing and AI will continue to improve our lives, but we also should continue to hold companies, organizations, and governments accountable for how our private data is used, as well as the technology's impact on the environment. [20] It's man vs machine this week as Google's artificial intelligence programme AlphaGo faces the world's top-ranked Go player in a contest expected to end in another victory for rapid advances in AI. [19] Google's computer programs are gaining a better understanding of the world, and now it wants them to handle more of the decision-making for the billions of people who use its services. [18] Microsoft on Wednesday unveiled new tools intended to democratize artificial intelligence by enabling machine smarts to be built into software from smartphone games to factory floors. [17]
Category: Artificial Intelligence

[348] viXra:1709.0007 [pdf] submitted on 2017-09-01 10:31:26

Computing, Cognition and Information Compression

Authors: J Gerard Wolff
Comments: 21 Pages.

This article develops the idea that the storage and processing of information in computers and in brains may often be understood as information compression. The article first reviews what is meant by information and, in particular, what is meant by redundancy, a concept which is fundamental in all methods for information compression. Principles of information compression are described. The major part of the article describes how these principles may be seen in a range of observations and ideas in computing and cognition: the phenomena of adaptation and inhibition in nervous systems; 'neural' computing; the creation and recognition of 'objects' and 'classes'in perception and cognition; stereoscopic vision and random-dot stereograms; the organisation of natural languages; the organisation of grammars; the organisation of functional, structured, logic and object-oriented computer programs; the application and de-referencing of identifiers in computing; retrieval of information from databases; access and retrieval of information from computer memory; logical deduction and resolution theorem proving; inductive reasoning and probabilistic inference; parsing; normalisation of databases.
Category: Artificial Intelligence

[347] viXra:1709.0004 [pdf] submitted on 2017-09-01 06:49:15

Simple Chess Puzzle

Authors: George Rajna
Comments: 26 Pages.

Researchers at the University of St Andrews have thrown down the gauntlet to computer programmers to find a solution to a "simple" chess puzzle which could, in fact, take thousands of years to solve and net a $1m prize. [11] It appears that we are approaching a unique time in the history of man and science where empirical measures and deductive reasoning can actually inform us spiritually. Integrated Information Theory (IIT)-put forth by neuroscientists Giulio Tononi and Christof Koch-is a new framework that describes a way to experimentally measure the extent to which a system is conscious. [10] There is also connection between statistical physics and evolutionary biology, since the arrow of time is working in the biological evolution also. From the standpoint of physics, there is one essential difference between living things and inanimate clumps of carbon atoms: The former tend to be much better at capturing energy from their environment and dissipating that energy as heat. [8] This paper contains the review of quantum entanglement investigations in living systems, and in the quantum mechanically modeled photoactive prebiotic kernel systems. [7] The human body is a constant flux of thousands of chemical/biological interactions and processes connecting molecules, cells, organs, and fluids, throughout the brain, body, and nervous system. Up until recently it was thought that all these interactions operated in a linear sequence, passing on information much like a runner passing the baton to the next runner. However, the latest findings in quantum biology and biophysics have discovered that there is in fact a tremendous degree of coherence within all living systems. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to understand the Quantum Biology.
Category: Artificial Intelligence

[346] viXra:1708.0482 [pdf] submitted on 2017-08-31 14:55:22

AI Analyzes Gravitational Lenses

Authors: George Rajna
Comments: 25 Pages.

Researchers from the Department of Energy's SLAC National Accelerator Laboratory and Stanford University have for the first time shown that neural networks - a form of artificial intelligence - can accurately analyze the complex distortions in spacetime known as gravitational lenses 10 million times faster than traditional methods. [16] By listening to the acoustic signal emitted by a laboratory-created earthquake, a computer science approach using machine learning can predict the time remaining before the fault fails. [15] Who is the better experimentalist, a human or a robot? When it comes to exploring synthetic and crystallization conditions for inorganic gigantic molecules, actively learning machines are clearly ahead, as demonstrated by British Scientists in an experiment with polyoxometalates published in the journal Angewandte Chemie. [14] Machine learning algorithms are designed to improve as they encounter more data, making them a versatile technology for understanding large sets of photos such as those accessible from Google Images. Elizabeth Holm, professor of materials science and engineering at Carnegie Mellon University, is leveraging this technology to better understand the enormous number of research images accumulated in the field of materials science. [13] With the help of artificial intelligence, chemists from the University of Basel in Switzerland have computed the characteristics of about two million crystals made up of four chemical elements. The researchers were able to identify 90 previously unknown thermodynamically stable crystals that can be regarded as new materials. [12] The artificial intelligence system's ability to set itself up quickly every morning and compensate for any overnight fluctuations would make this fragile technology much more useful for field measurements, said co-lead researcher Dr Michael Hush from UNSW ADFA. [11] Quantum physicist Mario Krenn and his colleagues in the group of Anton Zeilinger from the Faculty of Physics at the University of Vienna and the Austrian Academy of Sciences have developed an algorithm which designs new useful quantum experiments. As the computer does not rely on human intuition, it finds novel unfamiliar solutions. [10] Researchers at the University of Chicago's Institute for Molecular Engineering and the University of Konstanz have demonstrated the ability to generate a quantum logic operation, or rotation of the qubit, that - surprisingly—is intrinsically resilient to noise as well as to variations in the strength or duration of the control. Their achievement is based on a geometric concept known as the Berry phase and is implemented through entirely optical means within a single electronic spin in diamond. [9]
Category: Artificial Intelligence

[345] viXra:1708.0471 [pdf] submitted on 2017-08-30 12:48:09

Earthquake Machine Learning

Authors: George Rajna
Comments: 23 Pages.

By listening to the acoustic signal emitted by a laboratory-created earthquake, a computer science approach using machine learning can predict the time remaining before the fault fails. [15] Who is the better experimentalist, a human or a robot? When it comes to exploring synthetic and crystallization conditions for inorganic gigantic molecules, actively learning machines are clearly ahead, as demonstrated by British Scientists in an experiment with polyoxometalates published in the journal Angewandte Chemie. [14] Machine learning algorithms are designed to improve as they encounter more data, making them a versatile technology for understanding large sets of photos such as those accessible from Google Images. Elizabeth Holm, professor of materials science and engineering at Carnegie Mellon University, is leveraging this technology to better understand the enormous number of research images accumulated in the field of materials science. [13] With the help of artificial intelligence, chemists from the University of Basel in Switzerland have computed the characteristics of about two million crystals made up of four chemical elements. The researchers were able to identify 90 previously unknown thermodynamically stable crystals that can be regarded as new materials. [12] The artificial intelligence system's ability to set itself up quickly every morning and compensate for any overnight fluctuations would make this fragile technology much more useful for field measurements, said co-lead researcher Dr Michael Hush from UNSW ADFA. [11] Quantum physicist Mario Krenn and his colleagues in the group of Anton Zeilinger from the Faculty of Physics at the University of Vienna and the Austrian Academy of Sciences have developed an algorithm which designs new useful quantum experiments. As the computer does not rely on human intuition, it finds novel unfamiliar solutions. [10] Researchers at the University of Chicago's Institute for Molecular Engineering and the University of Konstanz have demonstrated the ability to generate a quantum logic operation, or rotation of the qubit, that-surprisingly—is intrinsically resilient to noise as well as to variations in the strength or duration of the control. Their achievement is based on a geometric concept known as the Berry phase and is implemented through entirely optical means within a single electronic spin in diamond. [9]
Category: Artificial Intelligence

[344] viXra:1708.0414 [pdf] submitted on 2017-08-28 08:55:08

Artificial Intelligence Cyber Attacks

Authors: George Rajna
Comments: 24 Pages.

The next major cyberattack could involve artificial intelligence systems. [13] Steve was a security robot employed by the Washington Harbour center in the Georgetown district of the US capital. [12] Combining the intuition of humans with the impartiality of computers could improve decision-making for organizations, eventually leading to lower costs and better profits, according to a team of researchers. [11] A team researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[343] viXra:1708.0381 [pdf] submitted on 2017-08-27 07:40:31

Security Robots

Authors: George Rajna
Comments: 22 Pages.

Combining the intuition of humans with the impartiality of computers could improve decision-making for organizations, eventually leading to lower costs and better profits, according to a team of researchers. [11] A team researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[342] viXra:1708.0341 [pdf] submitted on 2017-08-24 22:13:50

Routing Games Over Time with Fifo Policy

Authors: Anisse Ismaili
Comments: 16 Pages. Submission to conference WINE 2017 on August 2nd.

We study atomic routing games where every agent travels both along its decided edges and through time. The agents arriving on an edge are first lined up in a \emph{first-in-first-out} queue and may wait: an edge is associated with a capacity, which defines how many agents-per-time-step can pop from the queue's head and enter the edge, to transit for a fixed delay. We show that the best-response optimization problem is not approximable, and that deciding the existence of a Nash equilibrium is complete for the second level of the polynomial hierarchy. Then, we drop the rationality assumption, introduce a behavioral concept based on GPS navigation, and study its worst-case efficiency ratio to coordination.
Category: Artificial Intelligence

[341] viXra:1708.0331 [pdf] submitted on 2017-08-24 13:23:16

Computers Improve Decision Making

Authors: George Rajna
Comments: 20 Pages.

Combining the intuition of humans with the impartiality of computers could improve decision-making for organizations, eventually leading to lower costs and better profits, according to a team of researchers. [11] A team researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[340] viXra:1708.0246 [pdf] submitted on 2017-08-21 10:02:18

AI that can Understand Us

Authors: George Rajna
Comments: 47 Pages.

Computing pioneer Alan Turing's most pertinent thoughts on machine intelligence come from a neglected paragraph of the same paper that first proposed his famous test for whether a computer could be considered as smart as a human. [27] Predictions for an AI-dominated future are increasingly common, but Antoine Blondeau has experience in reading, and arguably manipulating, the runes—he helped develop technology that evolved into predictive texting and Apple's Siri. [26] Artificial intelligence can improve health care by analyzing data from apps, smartphones and wearable technology. [25] Now, researchers at Google's DeepMind have developed a simple algorithm to handle such reasoning—and it has already beaten humans at a complex image comprehension test. [24] A marimba-playing robot with four arms and eight sticks is writing and playing its own compositions in a lab at the Georgia Institute of Technology. The pieces are generated using artificial intelligence and deep learning. [23] Now, a team of researchers at MIT and elsewhere has developed a new approach to such computations, using light instead of electricity, which they say could vastly improve the speed and efficiency of certain deep learning computations. [22] Physicists have found that the structure of certain types of quantum learning algorithms is very similar to their classical counterparts—a finding that will help scientists further develop the quantum versions. [21] We should remain optimistic that quantum computing and AI will continue to improve our lives, but we also should continue to hold companies, organizations, and governments accountable for how our private data is used, as well as the technology's impact on the environment. [20] It's man vs machine this week as Google's artificial intelligence programme AlphaGo faces the world's top-ranked Go player in a contest expected to end in another victory for rapid advances in AI. [19] Google's computer programs are gaining a better understanding of the world, and now it wants them to handle more of the decision-making for the billions of people who use its services. [18]
Category: Artificial Intelligence

[339] viXra:1708.0239 [pdf] submitted on 2017-08-20 09:31:39

Artificial Intelligence Revolution

Authors: George Rajna
Comments: 45 Pages.

Predictions for an AI-dominated future are increasingly common, but Antoine Blondeau has experience in reading, and arguably manipulating, the runes—he helped develop technology that evolved into predictive texting and Apple's Siri. [26] Artificial intelligence can improve health care by analyzing data from apps, smartphones and wearable technology. [25] Now, researchers at Google's DeepMind have developed a simple algorithm to handle such reasoning—and it has already beaten humans at a complex image comprehension test. [24] A marimba-playing robot with four arms and eight sticks is writing and playing its own compositions in a lab at the Georgia Institute of Technology. The pieces are generated using artificial intelligence and deep learning. [23] Now, a team of researchers at MIT and elsewhere has developed a new approach to such computations, using light instead of electricity, which they say could vastly improve the speed and efficiency of certain deep learning computations. [22] Physicists have found that the structure of certain types of quantum learning algorithms is very similar to their classical counterparts—a finding that will help scientists further develop the quantum versions. [21] We should remain optimistic that quantum computing and AI will continue to improve our lives, but we also should continue to hold companies, organizations, and governments accountable for how our private data is used, as well as the technology's impact on the environment. [20] It's man vs machine this week as Google's artificial intelligence programme AlphaGo faces the world's top-ranked Go player in a contest expected to end in another victory for rapid advances in AI. [19] Google's computer programs are gaining a better understanding of the world, and now it wants them to handle more of the decision-making for the billions of people who use its services. [18] Microsoft on Wednesday unveiled new tools intended to democratize artificial intelligence by enabling machine smarts to be built into software from smartphone games to factory floors. [17]
Category: Artificial Intelligence

[338] viXra:1708.0238 [pdf] submitted on 2017-08-19 14:27:54

Machine-Learning Device

Authors: George Rajna
Comments: 24 Pages.

In what could be a small step for science potentially leading to a breakthrough, an engineer at Washington University in St. Louis has taken steps toward using nanocrystal networks for artificial intelligence applications. [16] Physicists have applied the ability of machine learning algorithms to learn from experience to one of the biggest challenges currently facing quantum computing: quantum error correction, which is used to design noise-tolerant quantum computing protocols. [15] Who is the better experimentalist, a human or a robot? When it comes to exploring synthetic and crystallization conditions for inorganic gigantic molecules, actively learning machines are clearly ahead, as demonstrated by British Scientists in an experiment with polyoxometalates published in the journal Angewandte Chemie. [14] Machine learning algorithms are designed to improve as they encounter more data, making them a versatile technology for understanding large sets of photos such as those accessible from Google Images. Elizabeth Holm, professor of materials science and engineering at Carnegie Mellon University, is leveraging this technology to better understand the enormous number of research images accumulated in the field of materials science. [13] With the help of artificial intelligence, chemists from the University of Basel in Switzerland have computed the characteristics of about two million crystals made up of four chemical elements. The researchers were able to identify 90 previously unknown thermodynamically stable crystals that can be regarded as new materials. [12] The artificial intelligence system's ability to set itself up quickly every morning and compensate for any overnight fluctuations would make this fragile technology much more useful for field measurements, said co-lead researcher Dr Michael Hush from UNSW ADFA. [11] Quantum physicist Mario Krenn and his colleagues in the group of Anton Zeilinger from the Faculty of Physics at the University of Vienna and the Austrian Academy of Sciences have developed an algorithm which designs new useful quantum experiments. As the computer does not rely on human intuition, it finds novel unfamiliar solutions. [10]
Category: Artificial Intelligence

[337] viXra:1708.0176 [pdf] submitted on 2017-08-16 01:32:34

Machine Learning Quantum Error Correction

Authors: George Rajna
Comments: 23 Pages.

Physicists have applied the ability of machine learning algorithms to learn from experience to one of the biggest challenges currently facing quantum computing: quantum error correction, which is used to design noise-tolerant quantum computing protocols. [15] Who is the better experimentalist, a human or a robot? When it comes to exploring synthetic and crystallization conditions for inorganic gigantic molecules, actively learning machines are clearly ahead, as demonstrated by British Scientists in an experiment with polyoxometalates published in the journal Angewandte Chemie. [14] Machine learning algorithms are designed to improve as they encounter more data, making them a versatile technology for understanding large sets of photos such as those accessible from Google Images. Elizabeth Holm, professor of materials science and engineering at Carnegie Mellon University, is leveraging this technology to better understand the enormous number of research images accumulated in the field of materials science. [13] With the help of artificial intelligence, chemists from the University of Basel in Switzerland have computed the characteristics of about two million crystals made up of four chemical elements. The researchers were able to identify 90 previously unknown thermodynamically stable crystals that can be regarded as new materials. [12] The artificial intelligence system's ability to set itself up quickly every morning and compensate for any overnight fluctuations would make this fragile technology much more useful for field measurements, said co-lead researcher Dr Michael Hush from UNSW ADFA. [11] Quantum physicist Mario Krenn and his colleagues in the group of Anton Zeilinger from the Faculty of Physics at the University of Vienna and the Austrian Academy of Sciences have developed an algorithm which designs new useful quantum experiments. As the computer does not rely on human intuition, it finds novel unfamiliar solutions. [10] Researchers at the University of Chicago's Institute for Molecular Engineering and the University of Konstanz have demonstrated the ability to generate a quantum logic operation, or rotation of the qubit, that - surprisingly—is intrinsically resilient to noise as well as to variations in the strength or duration of the control. Their achievement is based on a geometric concept known as the Berry phase and is implemented through entirely optical means within a single electronic spin in diamond. [9] New research demonstrates that particles at the quantum level can in fact be seen as behaving something like billiard balls rolling along a table, and not merely as the probabilistic smears that the standard interpretation of quantum mechanics suggests. But there's a catch - the tracks the particles follow do not always behave as one would expect from "realistic" trajectories, but often in a fashion that has been termed "surrealistic." [8]
Category: Artificial Intelligence

[336] viXra:1708.0167 [pdf] submitted on 2017-08-15 06:17:08

Organismic Learning

Authors: George Rajna
Comments: 24 Pages.

A new computing technology called "organismoids" mimics some aspects of human thought by learning how to forget unimportant memories while retaining more vital ones. [15] Who is the better experimentalist, a human or a robot? When it comes to exploring synthetic and crystallization conditions for inorganic gigantic molecules, actively learning machines are clearly ahead, as demonstrated by British Scientists in an experiment with polyoxometalates published in the journal Angewandte Chemie. [14] Machine learning algorithms are designed to improve as they encounter more data, making them a versatile technology for understanding large sets of photos such as those accessible from Google Images. Elizabeth Holm, professor of materials science and engineering at Carnegie Mellon University, is leveraging this technology to better understand the enormous number of research images accumulated in the field of materials science. [13] With the help of artificial intelligence, chemists from the University of Basel in Switzerland have computed the characteristics of about two million crystals made up of four chemical elements. The researchers were able to identify 90 previously unknown thermodynamically stable crystals that can be regarded as new materials. [12] The artificial intelligence system's ability to set itself up quickly every morning and compensate for any overnight fluctuations would make this fragile technology much more useful for field measurements, said co-lead researcher Dr Michael Hush from UNSW ADFA. [11] Quantum physicist Mario Krenn and his colleagues in the group of Anton Zeilinger from the Faculty of Physics at the University of Vienna and the Austrian Academy of Sciences have developed an algorithm which designs new useful quantum experiments. As the computer does not rely on human intuition, it finds novel unfamiliar solutions. [10] Researchers at the University of Chicago's Institute for Molecular Engineering and the University of Konstanz have demonstrated the ability to generate a quantum logic operation, or rotation of the qubit, that-surprisingly—is intrinsically resilient to noise as well as to variations in the strength or duration of the control. Their achievement is based on a geometric concept known as the Berry phase and is implemented through entirely optical means within a single electronic spin in diamond. [9]
Category: Artificial Intelligence

[335] viXra:1708.0131 [pdf] submitted on 2017-08-11 13:16:12

Adaptive Plant Propagation Algorithm for Solving Economic Load Dispatch Problem

Authors: Sayan Nag
Comments: 11 Pages.

Optimization problems in design engineering are complex by nature, often because of the involvement of critical objective functions accompanied by a number of rigid constraints associated with the products involved. One such problem is Economic Load Dispatch (ED) problem which focuses on the optimization of the fuel cost while satisfying some system constraints. Classical optimization algorithms are not sufficient and also inefficient for the ED problem involving highly nonlinear, and non-convex functions both in the objective and in the constraints. This led to the development of metaheuristic optimization approaches which can solve the ED problem almost efficiently. This paper presents a novel robust plant intelligence based Adaptive Plant Propagation Algorithm (APPA) which is used to solve the classical ED problem. The application of the proposed method to the 3-generator and 6-generator systems shows the efficiency and robustness of the proposed algorithm. A comparative study with another state-of-the-art algorithm (APSO) demonstrates the quality of the solution achieved by the proposed method along with the convergence characteristics of the proposed approach.
Category: Artificial Intelligence

[334] viXra:1708.0065 [pdf] submitted on 2017-08-06 17:11:22

Meta Mass Function

Authors: Yong Deng
Comments: 11 Pages.

In this paper, a meta mass function (MMF) is presented. A new evidence theory with complex numbers is developed. Different with existing evidence theory, the new mass function in complex evidence theory is modelled as complex numbers and named as meta mass function. The classical evidence theory is the special case under the condition that the mass function is degenerated from complex number as real number.
Category: Artificial Intelligence

[333] viXra:1708.0038 [pdf] submitted on 2017-08-04 04:30:39

Holistic Unique Clustering. {File Clsoing Version 4} ISSN 1751-3030

Authors: Ramesh Chandra Bagadi
Comments: 3 Pages.

In this research Technical Note the author has presented a novel method to find all Possible Clusters given a set of M points in N Space.
Category: Artificial Intelligence

[332] viXra:1708.0030 [pdf] submitted on 2017-08-03 10:30:43

Machine Learning for Discovery

Authors: George Rajna
Comments: 22 Pages.

Who is the better experimentalist, a human or a robot? When it comes to exploring synthetic and crystallization conditions for inorganic gigantic molecules, actively learning machines are clearly ahead, as demonstrated by British Scientists in an experiment with polyoxometalates published in the journal Angewandte Chemie. [14] Machine learning algorithms are designed to improve as they encounter more data, making them a versatile technology for understanding large sets of photos such as those accessible from Google Images. Elizabeth Holm, professor of materials science and engineering at Carnegie Mellon University, is leveraging this technology to better understand the enormous number of research images accumulated in the field of materials science. [13] With the help of artificial intelligence, chemists from the University of Basel in Switzerland have computed the characteristics of about two million crystals made up of four chemical elements. The researchers were able to identify 90 previously unknown thermodynamically stable crystals that can be regarded as new materials. [12] The artificial intelligence system's ability to set itself up quickly every morning and compensate for any overnight fluctuations would make this fragile technology much more useful for field measurements, said co-lead researcher Dr Michael Hush from UNSW ADFA. [11] Quantum physicist Mario Krenn and his colleagues in the group of Anton Zeilinger from the Faculty of Physics at the University of Vienna and the Austrian Academy of Sciences have developed an algorithm which designs new useful quantum experiments. As the computer does not rely on human intuition, it finds novel unfamiliar solutions. [10] Researchers at the University of Chicago's Institute for Molecular Engineering and the University of Konstanz have demonstrated the ability to generate a quantum logic operation, or rotation of the qubit, that-surprisingly—is intrinsically resilient to noise as well as to variations in the strength or duration of the control. Their achievement is based on a geometric concept known as the Berry phase and is implemented through entirely optical means within a single electronic spin in diamond. [9] New research demonstrates that particles at the quantum level can in fact be seen as behaving something like billiard balls rolling along a table, and not merely as the probabilistic smears that the standard interpretation of quantum mechanics suggests. But there's a catch-the tracks the particles follow do not always behave as one would expect from "realistic" trajectories, but often in a fashion that has been termed "surrealistic." [8] Quantum entanglement—which occurs when two or more particles are correlated in such a way that they can influence each other even across large distances—is not an all-or-nothing phenomenon, but occurs in various degrees. The more a quantum state is entangled with its partner, the better the states will perform in quantum information applications. Unfortunately, quantifying entanglement is a difficult process involving complex optimization problems that give even physicists headaches. [7] A trio of physicists in Europe has come up with an idea that they believe would allow a person to actually witness entanglement. Valentina Caprara Vivoli, with the University of Geneva, Pavel Sekatski, with the University of Innsbruck and Nicolas Sangouard, with the University of Basel, have together written a paper describing a scenario where a human subject would be able to witness an instance of entanglement—they have uploaded it to the arXiv server for review by others. [6] The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the relativistic quantum theory.
Category: Artificial Intelligence

[331] viXra:1708.0029 [pdf] submitted on 2017-08-03 10:54:39

Future Search Engines

Authors: George Rajna
Comments: 25 Pages.

The outcome is the result of two powerful forces in the evolution of information retrieval: artificial intelligence—especially natural language processing—and crowdsourcing. [15] Who is the better experimentalist, a human or a robot? When it comes to exploring synthetic and crystallization conditions for inorganic gigantic molecules, actively learning machines are clearly ahead, as demonstrated by British Scientists in an experiment with polyoxometalates published in the journal Angewandte Chemie. [14] Machine learning algorithms are designed to improve as they encounter more data, making them a versatile technology for understanding large sets of photos such as those accessible from Google Images. Elizabeth Holm, professor of materials science and engineering at Carnegie Mellon University, is leveraging this technology to better understand the enormous number of research images accumulated in the field of materials science. [13] With the help of artificial intelligence, chemists from the University of Basel in Switzerland have computed the characteristics of about two million crystals made up of four chemical elements. The researchers were able to identify 90 previously unknown thermodynamically stable crystals that can be regarded as new materials. [12] The artificial intelligence system's ability to set itself up quickly every morning and compensate for any overnight fluctuations would make this fragile technology much more useful for field measurements, said co-lead researcher Dr Michael Hush from UNSW ADFA. [11] Quantum physicist Mario Krenn and his colleagues in the group of Anton Zeilinger from the Faculty of Physics at the University of Vienna and the Austrian Academy of Sciences have developed an algorithm which designs new useful quantum experiments. As the computer does not rely on human intuition, it finds novel unfamiliar solutions. [10] Researchers at the University of Chicago's Institute for Molecular Engineering and the University of Konstanz have demonstrated the ability to generate a quantum logic operation, or rotation of the qubit, that-surprisingly—is intrinsically resilient to noise as well as to variations in the strength or duration of the control. Their achievement is based on a geometric concept known as the Berry phase and is implemented through entirely optical means within a single electronic spin in diamond. [9]
Category: Artificial Intelligence

[330] viXra:1708.0025 [pdf] submitted on 2017-08-02 23:22:10

Similarity Measure Of Any Two Vectors Of Same Size

Authors: Ramesh Chandra Bagadi
Comments: 2 Pages.

In this research Technical Note the author has presented a novel method of finding a Generalized Similarity Measure between two Vectors of the same size.
Category: Artificial Intelligence

[329] viXra:1708.0019 [pdf] submitted on 2017-08-03 06:42:09

Holistic Unique Clustering. ISSN 1751-3030

Authors: Ramesh Chandra Bagadi
Comments: 2 Pages.

In this research Technical Note the author has presented a novel method to find all Possible Clusters given a set of M points in N Space.
Category: Artificial Intelligence

[328] viXra:1708.0010 [pdf] submitted on 2017-08-02 04:36:45

A Generalized Similarity Measure {File Closing Version 3} ISSN 1751-3030

Authors: Ramesh Chandra Bagadi
Comments: 2 Pages.

In this research Technical Note the author has presented a novel method of finding a Generalized Similarity Measure between two Vectors or Matrices or Higher Dimensional Data of different sizes.
Category: Artificial Intelligence

[327] viXra:1707.0394 [pdf] submitted on 2017-07-30 02:17:51

The Recursive Future Equation And The Recursive Past Equation Based On The Ananda-Damayanthi Normalized Similarity Measure. {File Closing Version-2}

Authors: Ramesh Chandra Bagadi
Comments: 3 Pages.

In this research Technical Note the author have presented a Recursive Future Equation and Recursive Past Equation to find one Step Future Element or a one Step Past Element of a given Time Series data Set.
Category: Artificial Intelligence

[326] viXra:1707.0389 [pdf] submitted on 2017-07-29 07:23:01

Machine Learning and Deep Learning

Authors: George Rajna
Comments: 27 Pages.

Deep learning and machine learning both offer ways to train models and classify data. This article compares the two and it offers ways to help you decide which one to use. [15] Physicists have shown that quantum effects have the potential to significantly improve a variety of interactive learning tasks in machine learning. [14] A Chinese team of physicists have trained a quantum computer to recognise handwritten characters, the first demonstration of " quantum artificial intelligence ". Physicists have long claimed that quantum computers have the potential to dramatically outperform the most powerful conventional processors. The secret sauce at work here is the strange quantum phenomenon of superposition, where a quantum object can exist in two states at the same time. [13] One of biology's biggest mysteries-how a sliced up flatworm can regenerate into new organisms-has been solved independently by a computer. The discovery marks the first time that a computer has come up with a new scientific theory without direct human help. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[325] viXra:1707.0372 [pdf] submitted on 2017-07-28 06:35:21

The Recursive Future Equation And The Recursive Past Equation Based On The Ananda-Damayanthi Normalized Similarity Measure. {Future}

Authors: Ramesh Chandra Bagadi
Comments: 2 Pages.

In this research Technical Note the author have presented a Recursive Future Equation and Recursive Past Equation to find one Step Future Element or a one Step Past Element of a given Time Series data Set.
Category: Artificial Intelligence

[324] viXra:1707.0268 [pdf] submitted on 2017-07-20 02:20:32

Finding The Optimal Number ‘K’ In The K-Means Algorithm

Authors: Ramesh Chandra Bagadi
Comments: 2 Pages.

In this research Technical Note the author has presented a novel method to find the Optimal Number ‘K’ in the K-Means Algorithm.
Category: Artificial Intelligence

[323] viXra:1707.0255 [pdf] submitted on 2017-07-19 05:05:13

Humanize Artificial Intelligent

Authors: George Rajna
Comments: 41 Pages.

Google recently launched PAIR, an acronym of People + AI Research, in an attempt to increase the utility of AI and improve human to AI interaction. [25] Now, researchers at Google's DeepMind have developed a simple algorithm to handle such reasoning—and it has already beaten humans at a complex image comprehension test. [24] A marimba-playing robot with four arms and eight sticks is writing and playing its own compositions in a lab at the Georgia Institute of Technology. The pieces are generated using artificial intelligence and deep learning. [23] Now, a team of researchers at MIT and elsewhere has developed a new approach to such computations, using light instead of electricity, which they say could vastly improve the speed and efficiency of certain deep learning computations. [22] Physicists have found that the structure of certain types of quantum learning algorithms is very similar to their classical counterparts—a finding that will help scientists further develop the quantum versions. [21] We should remain optimistic that quantum computing and AI will continue to improve our lives, but we also should continue to hold companies, organizations, and governments accountable for how our private data is used, as well as the technology's impact on the environment. [20] It's man vs machine this week as Google's artificial intelligence programme AlphaGo faces the world's top-ranked Go player in a contest expected to end in another victory for rapid advances in AI. [19] Google's computer programs are gaining a better understanding of the world, and now it wants them to handle more of the decision-making for the billions of people who use its services. [18] Microsoft on Wednesday unveiled new tools intended to democratize artificial intelligence by enabling machine smarts to be built into software from smartphone games to factory floors. [17] The closer we can get a machine translation to be on par with expert human translation, the happier lots of people struggling with translations will be. [16] Researchers have created a large, open source database to support the development of robot activities based on natural language input. [15]
Category: Artificial Intelligence

[322] viXra:1707.0254 [pdf] submitted on 2017-07-19 06:01:57

Using the Appropriate Norm In The K-Nearest Neighbours Analysis. ISSN 1751-3030

Authors: Ramesh Chandra Bagadi
Comments: 1 Page.

In this research Technical Note, the author has detailed a novel technique of finding the distance metric to be used for any given set of points.
Category: Artificial Intelligence

[321] viXra:1707.0252 [pdf] submitted on 2017-07-19 06:40:54

A Generalized Similarity Measure {File Closing Version 2} ISSN 1751-3030

Authors: Ramesh Chandra Bagadi
Comments: 2 Pages.

In this research Technical Note the author has presented a novel method of finding a Generalized Similarity Measure between two Vectors or Matrices or Higher Dimensional Data of different sizes.
Category: Artificial Intelligence

[320] viXra:1707.0230 [pdf] submitted on 2017-07-17 05:49:20

A Generalized Similarity Measure ISSN 1751-3030

Authors: Ramesh Chandra Bagadi
Comments: 2 Pages.

In this research Technical Note the author has presented a novel method of finding a Generalized Similarity Measure between two Vectors or Matrices or Higher Dimensional Data of different sizes.
Category: Artificial Intelligence

[319] viXra:1707.0225 [pdf] submitted on 2017-07-17 01:50:21

Multi Class Classification Using Holistic Non-Unique Clustering {File Closing Version 8}. ISSN 1751-3030

Authors: Ramesh Chandra Bagadi
Comments: 3 Pages.

In this research Technical Note the author has presented a novel method to find all Possible Clusters given a set of M points in N Space.
Category: Artificial Intelligence

[318] viXra:1707.0200 [pdf] submitted on 2017-07-14 04:55:42

Multi Class Classification Using Holistic Non-Unique Clustering ISSN 1751-3030

Authors: Ramesh Chandra Bagadi
Comments: 1 Page.

In this research technical Note the author have presented a novel method to find all Possible Clusters given a set of M points in N Space.
Category: Artificial Intelligence

[317] viXra:1707.0198 [pdf] submitted on 2017-07-14 05:30:10

Multi Class Classification Using Holistic Non-Unique Clustering. {File Closing Version 7} ISSN 1751-3030

Authors: Ramesh Chandra Bagadi
Comments: 1 Page.

In this research technical Note the author have presented a novel method to find all Possible Clusters given a set of M points in N Space.
Category: Artificial Intelligence

[316] viXra:1707.0179 [pdf] submitted on 2017-07-13 01:20:46

Modification To The Scaling Aspect In Gower’s Scheme Of Calculating Similarity Coefficient

Authors: Ramesh Chandra Bagadi
Comments: 1 Page.

In this research technical Note the author have presented a tiny modification to the Numeric Variables Scaling Aspect In Gower’s Scheme of calculating Similarity Coefficient.
Category: Artificial Intelligence

[315] viXra:1707.0178 [pdf] submitted on 2017-07-13 02:34:27

Recursive Future Average Of A Time Series Data Based On Cosine Similarity

Authors: Ramesh Chandra Bagadi
Comments: 2 Pages.

In this research Technical Note the author have presented a Recursive Future Average Of A Time Series Data Based on Cosine Similarity.
Category: Artificial Intelligence

[314] viXra:1707.0166 [pdf] submitted on 2017-07-12 01:12:07

Theoretical Materials

Authors: George Rajna
Comments: 49 Pages.

University have created the first general-purpose method for using machine learning to predict the properties of new metals, ceramics and other crystalline materials and to find new uses for existing materials, a discovery that could save countless hours wasted in the trial-and-error process of creating new and better materials. [28] As machine learning breakthroughs abound, researchers look to democratize benefits. [27] Machine-learning system spontaneously reproduces aspects of human neurology. [26] Surviving breast cancer changed the course of Regina Barzilay's research. The experience showed her, in stark relief, that oncologists and their patients lack tools for data-driven decision making. [25] New research, led by the University of Southampton, has demonstrated that a nanoscale device, called a memristor, could be used to power artificial systems that can mimic the human brain. [24] Scientists at Helmholtz-Zentrum Dresden-Rossendorf conducted electricity through DNA-based nanowires by placing gold-plated nanoparticles on them. In this way it could become possible to develop circuits based on genetic material. [23] Researchers at the Nanoscale Transport Physics Laboratory from the School of Physics at the University of the Witwatersrand have found a technique to improve carbon superlattices for quantum electronic device applications. [22] The researchers have found that these previously underestimated interactions can play a significant role in preventing heat dissipation in microelectronic devices. [21] LCLS works like an extraordinary strobe light: Its ultrabright X-rays take snapshots of materials with atomic resolution and capture motions as fast as a few femtoseconds, or millionths of a billionth of a second. For comparison, one femtosecond is to a second what seven minutes is to the age of the universe. [20] A 'nonlinear' effect that seemingly turns materials transparent is seen for the first time in X-rays at SLAC's LCLS. [19]
Category: Artificial Intelligence

[313] viXra:1707.0165 [pdf] submitted on 2017-07-12 01:25:24

Multi Class Classification Using Holistic Non-Unique Clustering

Authors: Ramesh Chandra Bagadi
Comments: 2 Pages.

In this research technical Note the author have presented a novel method to find all Possible Clusters given a set of M points in N Space.
Category: Artificial Intelligence

[312] viXra:1707.0145 [pdf] submitted on 2017-07-11 02:29:17

A Novel Type Of Time Series Type Forecasting

Authors: Ramesh Chandra Bagadi
Comments: 3 Pages.

In this research investigation, the author has detailed a novel Time series type of forecasting.
Category: Artificial Intelligence

[311] viXra:1707.0142 [pdf] submitted on 2017-07-11 04:48:06

A Novel Type Of Time Series Type Forecasting. {File Closing Version 1}

Authors: Ramesh Chandra Bagadi
Comments: 3 Pages.

In this research investigation, the author has detailed a novel Time series type of forecasting.
Category: Artificial Intelligence

[310] viXra:1707.0102 [pdf] submitted on 2017-07-07 01:23:03

Holistic Non-Unique Clsutering. {File Closing Version 1} ISSN 1751-3030

Authors: Ramesh Chandra Bagadi
Comments: 2 Pages.

In this research technical Note the author have presented a novel method to find all Possible Clusters given a set of points in N Space.
Category: Artificial Intelligence

[309] viXra:1707.0098 [pdf] submitted on 2017-07-07 01:44:57

Holistic Non-Unique Clsutering. {File Closing Version 2} ISSN 1751-3030

Authors: Ramesh Chandra Bagadi
Comments: 2 Pages.

In this research technical Note the author have presented a novel method to find all Possible Clusters given a set of M points in N Space.
Category: Artificial Intelligence

[308] viXra:1707.0071 [pdf] submitted on 2017-07-05 08:51:43

Seeing All The Clusters

Authors: Ramesh Chandra Bagadi
Comments: 1 Page.

In this technical note the author has presented a novel method to find all the clusters (overlapping and non-unique) formed by a given set of points.
Category: Artificial Intelligence

[307] viXra:1707.0070 [pdf] submitted on 2017-07-05 08:58:23

Seeing All Clusters Formed By A Given Set Of Points (File Closing Version) ISSN 1751-3030

Authors: Ramesh Chandra Bagadi
Comments: 1 Page.

In this research investigation, the author has presented a novel technique to find all Clusters that may be overlapping to some extent.
Category: Artificial Intelligence

[306] viXra:1707.0061 [pdf] submitted on 2017-07-05 06:54:24

Holistic Non-Unique Clsutering. ISSN 1751-3030

Authors: Ramesh Chandra Bagadi
Comments: 1 Page.

In this technical note, the author has presented a novel scheme of Holistic Non-Unique Clustering.
Category: Artificial Intelligence

[305] viXra:1707.0043 [pdf] submitted on 2017-07-03 22:47:02

Using the Appropriate Norm In The K-Nearest Neighbours Analysis

Authors: Ramesh Chandra Bagadi
Comments: 1 Page.

In this Technical Note the author has presented and alternative to the use of L2 Norm for Nearness Analysis in K-Nearest Neighbours Algorithm.
Category: Artificial Intelligence

[304] viXra:1707.0002 [pdf] submitted on 2017-07-01 04:24:01

Inner Workings of Neural Networks

Authors: George Rajna
Comments: 33 Pages.

Neural networks learn to perform computational tasks by analyzing large sets of training data. But once they've been trained, even their designers rarely have any idea what data elements they're processing. [20] Researchers from Disney Research, Pixar Animation Studios, and the University of California, Santa Barbara have developed a new technology based on artificial intelligence (AI) and deep learning that eliminates this noise and thereby enables production-quality rendering at much faster speeds. [19] Now, one group reports in ACS Nano that they have developed an artificial synapse capable of simulating a fundamental function of our nervous system— the release of inhibitory and stimulatory signals from the same "pre-synaptic" terminal. [18] Researchers from France and the University of Arkansas have created an artificial synapse capable of autonomous learning, a component of artificial intelligence. [17] Intelligent machines of the future will help restore memory, mind your children, fetch your coffee and even care for aging parents. [16] Unlike experimental neuroscientists who deal with real-life neurons, computational neuroscientists use model simulations to investigate how the brain functions. [15] A pair of physicists with ETH Zurich has developed a way to use an artificial neural network to characterize the wave function of a quantum many-body system. [14] A team of researchers at Google's DeepMind Technologies has been working on a means to increase the capabilities of computers by combining aspects of data processing and artificial intelligence and have come up with what they are calling a differentiable neural computer (DNC.) In their paper published in the journal Nature, they describe the work they are doing and where they believe it is headed. To make the work more accessible to the public team members, Alexander Graves and Greg Wayne have posted an explanatory page on the DeepMind website. [13] Nobody understands why deep neural networks are so good at solving complex problems. Now physicists say the secret is buried in the laws of physics. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip
Category: Artificial Intelligence

[303] viXra:1706.0570 [pdf] submitted on 2017-06-30 12:07:02

Convolutional Neural Network

Authors: George Rajna
Comments: 31 Pages.

Researchers from Disney Research, Pixar Animation Studios, and the University of California, Santa Barbara have developed a new technology based on artificial intelligence (AI) and deep learning that eliminates this noise and thereby enables production-quality rendering at much faster speeds. [19] Now, one group reports in ACS Nano that they have developed an artificial synapse capable of simulating a fundamental function of our nervous system— the release of inhibitory and stimulatory signals from the same "pre-synaptic" terminal. [18] Researchers from France and the University of Arkansas have created an artificial synapse capable of autonomous learning, a component of artificial intelligence. [17] Intelligent machines of the future will help restore memory, mind your children, fetch your coffee and even care for aging parents. [16] Unlike experimental neuroscientists who deal with real-life neurons, computational neuroscientists use model simulations to investigate how the brain functions. [15] A pair of physicists with ETH Zurich has developed a way to use an artificial neural network to characterize the wave function of a quantum many-body system. [14] A team of researchers at Google's DeepMind Technologies has been working on a means to increase the capabilities of computers by combining aspects of data processing and artificial intelligence and have come up with what they are calling a differentiable neural computer (DNC.) In their paper published in the journal Nature, they describe the work they are doing and where they believe it is headed. To make the work more accessible to the public team members, Alexander Graves and Greg Wayne have posted an explanatory page on the DeepMind website. [13] Nobody understands why deep neural networks are so good at solving complex problems. Now physicists say the secret is buried in the laws of physics. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11]
Category: Artificial Intelligence

[302] viXra:1706.0523 [pdf] submitted on 2017-06-28 09:17:30

Artificial Synapse for AI

Authors: George Rajna
Comments: 30 Pages.

Now, one group reports in ACS Nano that they have developed an artificial synapse capable of simulating a fundamental function of our nervous system— the release of inhibitory and stimulatory signals from the same "pre-synaptic" terminal. [18] Researchers from France and the University of Arkansas have created an artificial synapse capable of autonomous learning, a component of artificial intelligence. [17] Intelligent machines of the future will help restore memory, mind your children, fetch your coffee and even care for aging parents. [16] Unlike experimental neuroscientists who deal with real-life neurons, computational neuroscientists use model simulations to investigate how the brain functions. [15] A pair of physicists with ETH Zurich has developed a way to use an artificial neural network to characterize the wave function of a quantum many-body system. [14] A team of researchers at Google's DeepMind Technologies has been working on a means to increase the capabilities of computers by combining aspects of data processing and artificial intelligence and have come up with what they are calling a differentiable neural computer (DNC.) In their paper published in the journal Nature, they describe the work they are doing and where they believe it is headed. To make the work more accessible to the public team members, Alexander Graves and Greg Wayne have posted an explanatory page on the DeepMind website. [13] Nobody understands why deep neural networks are so good at solving complex problems. Now physicists say the secret is buried in the laws of physics. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain.
Category: Artificial Intelligence

[301] viXra:1706.0469 [pdf] submitted on 2017-06-25 08:35:27

Quantum Machine Learning Computer Hybrids

Authors: George Rajna
Comments: 28 Pages.

Creative Destruction Lab, a technology program affiliated with the University of Toronto's Rotman School of Management in Toronto, Canada hopes to nurture numerous quantum learning machine start-ups in only a few years. [15] Physicists have shown that quantum effects have the potential to significantly improve a variety of interactive learning tasks in machine learning. [14] A Chinese team of physicists have trained a quantum computer to recognise handwritten characters, the first demonstration of " quantum artificial intelligence ". Physicists have long claimed that quantum computers have the potential to dramatically outperform the most powerful conventional processors. The secret sauce at work here is the strange quantum phenomenon of superposition, where a quantum object can exist in two states at the same time. [13] One of biology's biggest mysteries-how a sliced up flatworm can regenerate into new organisms-has been solved independently by a computer. The discovery marks the first time that a computer has come up with a new scientific theory without direct human help. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[300] viXra:1706.0468 [pdf] submitted on 2017-06-25 10:31:28

Weak AI, Strong AI and Superintelligence

Authors: George Rajna
Comments: 29 Pages.

Should we fear artificial intelligence and all it will bring us? Not so long as we remember to make sure to build artificial emotional intelligence into the technology, according to the website The School of Life. [16] Creative Destruction Lab, a technology program affiliated with the University of Toronto’s Rotman School of Management in Toronto, Canada hopes to nurture numerous quantum learning machine start-ups in only a few years. [15] Physicists have shown that quantum effects have the potential to significantly improve a variety of interactive learning tasks in machine learning. [14] A Chinese team of physicists have trained a quantum computer to recognise handwritten characters, the first demonstration of “quantum artificial intelligence”. Physicists have long claimed that quantum computers have the potential to dramatically outperform the most powerful conventional processors. The secret sauce at work here is the strange quantum phenomenon of superposition, where a quantum object can exist in two states at the same time. [13] One of biology's biggest mysteries - how a sliced up flatworm can regenerate into new organisms - has been solved independently by a computer. The discovery marks the first time that a computer has come up with a new scientific theory without direct human help. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron’s spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[299] viXra:1706.0462 [pdf] submitted on 2017-06-25 02:34:26

Brain-Inspired Supercomputing

Authors: George Rajna
Comments: 48 Pages.

IBM and the Air Force Research Laboratory are working to develop an artificial intelligence-based supercomputer with a neural network design that is inspired by the human brain. [28] Researchers have built a new type of "neuron transistor"—a transistor that behaves like a neuron in a living brain. [27] Research team led by Professor Hoi-Jun Yoo of the Department of Electrical Engineering has developed a semiconductor chip, CNNP (CNN Processor), that runs AI algorithms with ultra-low power, and K-Eye, a face recognition system using CNNP. [26] Artificial intelligence can improve health care by analyzing data from apps, smartphones and wearable technology. [25] Now, researchers at Google's DeepMind have developed a simple algorithm to handle such reasoning—and it has already beaten humans at a complex image comprehension test. [24] A marimba-playing robot with four arms and eight sticks is writing and playing its own compositions in a lab at the Georgia Institute of Technology. The pieces are generated using artificial intelligence and deep learning. [23] Now, a team of researchers at MIT and elsewhere has developed a new approach to such computations, using light instead of electricity, which they say could vastly improve the speed and efficiency of certain deep learning computations. [22] Physicists have found that the structure of certain types of quantum learning algorithms is very similar to their classical counterparts—a finding that will help scientists further develop the quantum versions. [21] We should remain optimistic that quantum computing and AI will continue to improve our lives, but we also should continue to hold companies, organizations, and governments accountable for how our private data is used, as well as the technology's impact on the environment. [20] It's man vs machine this week as Google's artificial intelligence programme AlphaGo faces the world's top-ranked Go player in a contest expected to end in another victory for rapid advances in AI. [19] Google's computer programs are gaining a better understanding of the world, and now it wants them to handle more of the decision-making for the billions of people who use its services. [18]
Category: Artificial Intelligence

[298] viXra:1706.0433 [pdf] submitted on 2017-06-23 06:57:24

AI and Robots can Help Patients

Authors: George Rajna
Comments: 45 Pages.

McMaster and Ryerson universities today announced the Smart Robots for Health Communication project, a joint research initiative designed to introduce social robotics and artificial intelligence into clinical health care. [26] Artificial intelligence can improve health care by analyzing data from apps, smartphones and wearable technology. [25] Now, researchers at Google's DeepMind have developed a simple algorithm to handle such reasoning—and it has already beaten humans at a complex image comprehension test. [24] A marimba-playing robot with four arms and eight sticks is writing and playing its own compositions in a lab at the Georgia Institute of Technology. The pieces are generated using artificial intelligence and deep learning. [23] Now, a team of researchers at MIT and elsewhere has developed a new approach to such computations, using light instead of electricity, which they say could vastly improve the speed and efficiency of certain deep learning computations. [22] Physicists have found that the structure of certain types of quantum learning algorithms is very similar to their classical counterparts—a finding that will help scientists further develop the quantum versions. [21] We should remain optimistic that quantum computing and AI will continue to improve our lives, but we also should continue to hold companies, organizations, and governments accountable for how our private data is used, as well as the technology's impact on the environment. [20] It's man vs machine this week as Google's artificial intelligence programme AlphaGo faces the world's top-ranked Go player in a contest expected to end in another victory for rapid advances in AI. [19] Google's computer programs are gaining a better understanding of the world, and now it wants them to handle more of the decision-making for the billions of people who use its services. [18] Microsoft on Wednesday unveiled new tools intended to democratize artificial intelligence by enabling machine smarts to be built into software from smartphone games to factory floors. [17]
Category: Artificial Intelligence

[297] viXra:1706.0402 [pdf] submitted on 2017-06-20 10:02:53

Neuron Transistor

Authors: George Rajna
Comments: 45 Pages.

Researchers have built a new type of "neuron transistor"—a transistor that behaves like a neuron in a living brain. [27] Research team led by Professor Hoi-Jun Yoo of the Department of Electrical Engineering has developed a semiconductor chip, CNNP (CNN Processor), that runs AI algorithms with ultra-low power, and K-Eye, a face recognition system using CNNP. [26] Artificial intelligence can improve health care by analyzing data from apps, smartphones and wearable technology. [25] Now, researchers at Google's DeepMind have developed a simple algorithm to handle such reasoning—and it has already beaten humans at a complex image comprehension test. [24] A marimba-playing robot with four arms and eight sticks is writing and playing its own compositions in a lab at the Georgia Institute of Technology. The pieces are generated using artificial intelligence and deep learning. [23] Now, a team of researchers at MIT and elsewhere has developed a new approach to such computations, using light instead of electricity, which they say could vastly improve the speed and efficiency of certain deep learning computations. [22] Physicists have found that the structure of certain types of quantum learning algorithms is very similar to their classical counterparts—a finding that will help scientists further develop the quantum versions. [21] We should remain optimistic that quantum computing and AI will continue to improve our lives, but we also should continue to hold companies, organizations, and governments accountable for how our private data is used, as well as the technology's impact on the environment. [20] It's man vs machine this week as Google's artificial intelligence programme AlphaGo faces the world's top-ranked Go player in a contest expected to end in another victory for rapid advances in AI. [19] Google's computer programs are gaining a better understanding of the world, and now it wants them to handle more of the decision-making for the billions of people who use its services. [18] Microsoft on Wednesday unveiled new tools intended to democratize artificial intelligence by enabling machine smarts to be built into software from smartphone games to factory floors. [17]
Category: Artificial Intelligence

[296] viXra:1706.0389 [pdf] submitted on 2017-06-19 04:15:18

Artificial Intelligence Health Revolution

Authors: George Rajna
Comments: 43 Pages.

Artificial intelligence can improve health care by analyzing data from apps, smartphones and wearable technology. [25] Now, researchers at Google's DeepMind have developed a simple algorithm to handle such reasoning—and it has already beaten humans at a complex image comprehension test. [24] A marimba-playing robot with four arms and eight sticks is writing and playing its own compositions in a lab at the Georgia Institute of Technology. The pieces are generated using artificial intelligence and deep learning. [23] Now, a team of researchers at MIT and elsewhere has developed a new approach to such computations, using light instead of electricity, which they say could vastly improve the speed and efficiency of certain deep learning computations. [22] Physicists have found that the structure of certain types of quantum learning algorithms is very similar to their classical counterparts—a finding that will help scientists further develop the quantum versions. [21] We should remain optimistic that quantum computing and AI will continue to improve our lives, but we also should continue to hold companies, organizations, and governments accountable for how our private data is used, as well as the technology's impact on the environment. [20] It's man vs machine this week as Google's artificial intelligence programme AlphaGo faces the world's top-ranked Go player in a contest expected to end in another victory for rapid advances in AI. [19] Google's computer programs are gaining a better understanding of the world, and now it wants them to handle more of the decision-making for the billions of people who use its services. [18] Microsoft on Wednesday unveiled new tools intended to democratize artificial intelligence by enabling machine smarts to be built into software from smartphone games to factory floors. [17] The closer we can get a machine translation to be on par with expert human translation, the happier lots of people struggling with translations will be. [16] Researchers have created a large, open source database to support the development of robot activities based on natural language input. [15]
Category: Artificial Intelligence

[295] viXra:1706.0387 [pdf] submitted on 2017-06-19 04:54:30

K-Eye Face Recognition System

Authors: George Rajna
Comments: 45 Pages.

A research team led by Professor Hoi-Jun Yoo of the Department of Electrical Engineering has developed a semiconductor chip, CNNP (CNN Processor), that runs AI algorithms with ultra-low power, and K-Eye, a face recognition system using CNNP. [26] Artificial intelligence can improve health care by analyzing data from apps, smartphones and wearable technology. [25] Now, researchers at Google's DeepMind have developed a simple algorithm to handle such reasoning—and it has already beaten humans at a complex image comprehension test. [24] A marimba-playing robot with four arms and eight sticks is writing and playing its own compositions in a lab at the Georgia Institute of Technology. The pieces are generated using artificial intelligence and deep learning. [23] Now, a team of researchers at MIT and elsewhere has developed a new approach to such computations, using light instead of electricity, which they say could vastly improve the speed and efficiency of certain deep learning computations. [22] Physicists have found that the structure of certain types of quantum learning algorithms is very similar to their classical counterparts—a finding that will help scientists further develop the quantum versions. [21] We should remain optimistic that quantum computing and AI will continue to improve our lives, but we also should continue to hold companies, organizations, and governments accountable for how our private data is used, as well as the technology's impact on the environment. [20] It's man vs machine this week as Google's artificial intelligence programme AlphaGo faces the world's top-ranked Go player in a contest expected to end in another victory for rapid advances in AI. [19] Google's computer programs are gaining a better understanding of the world, and now it wants them to handle more of the decision-making for the billions of people who use its services. [18] Microsoft on Wednesday unveiled new tools intended to democratize artificial intelligence by enabling machine smarts to be built into software from smartphone games to factory floors. [17]
Category: Artificial Intelligence

[294] viXra:1706.0293 [pdf] submitted on 2017-06-16 06:05:08

Computers Reason Like Humans

Authors: George Rajna
Comments: 40 Pages.

Now, researchers at Google's DeepMind have developed a simple algorithm to handle such reasoning—and it has already beaten humans at a complex image comprehension test. [24] A marimba-playing robot with four arms and eight sticks is writing and playing its own compositions in a lab at the Georgia Institute of Technology. The pieces are generated using artificial intelligence and deep learning. [23] Now, a team of researchers at MIT and elsewhere has developed a new approach to such computations, using light instead of electricity, which they say could vastly improve the speed and efficiency of certain deep learning computations. [22] Physicists have found that the structure of certain types of quantum learning algorithms is very similar to their classical counterparts—a finding that will help scientists further develop the quantum versions. [21] We should remain optimistic that quantum computing and AI will continue to improve our lives, but we also should continue to hold companies, organizations, and governments accountable for how our private data is used, as well as the technology's impact on the environment. [20] It's man vs machine this week as Google's artificial intelligence programme AlphaGo faces the world's top-ranked Go player in a contest expected to end in another victory for rapid advances in AI. [19] Google's computer programs are gaining a better understanding of the world, and now it wants them to handle more of the decision-making for the billions of people who use its services. [18] Microsoft on Wednesday unveiled new tools intended to democratize artificial intelligence by enabling machine smarts to be built into software from smartphone games to factory floors. [17] The closer we can get a machine translation to be on par with expert human translation, the happier lots of people struggling with translations will be. [16] Researchers have created a large, open source database to support the development of robot activities based on natural language input. [15] A pair of physicists with ETH Zurich has developed a way to use an artificial neural network to characterize the wave function of a quantum many-body system. [14]
Category: Artificial Intelligence

[293] viXra:1706.0235 [pdf] submitted on 2017-06-13 02:02:47

Deep Learning with Light

Authors: George Rajna
Comments: 37 Pages.

Now, a team of researchers at MIT and elsewhere has developed a new approach to such computations, using light instead of electricity, which they say could vastly improve the speed and efficiency of certain deep learning computations. [22] Physicists have found that the structure of certain types of quantum learning algorithms is very similar to their classical counterparts—a finding that will help scientists further develop the quantum versions. [21] We should remain optimistic that quantum computing and AI will continue to improve our lives, but we also should continue to hold companies, organizations, and governments accountable for how our private data is used, as well as the technology's impact on the environment. [20] It's man vs machine this week as Google's artificial intelligence programme AlphaGo faces the world's top-ranked Go player in a contest expected to end in another victory for rapid advances in AI. [19] Google's computer programs are gaining a better understanding of the world, and now it wants them to handle more of the decision-making for the billions of people who use its services. [18] Microsoft on Wednesday unveiled new tools intended to democratize artificial intelligence by enabling machine smarts to be built into software from smartphone games to factory floors. [17] The closer we can get a machine translation to be on par with expert human translation, the happier lots of people struggling with translations will be. [16] Researchers have created a large, open source database to support the development of robot activities based on natural language input. [15] A pair of physicists with ETH Zurich has developed a way to use an artificial neural network to characterize the wave function of a quantum many-body system. [14] A team of researchers at Google's DeepMind Technologies has been working on a means to increase the capabilities of computers by combining aspects of data processing and artificial intelligence and have come up with what they are calling a differentiable neural computer (DNC.) In their paper published in the journal Nature, they describe the work they are doing and where they believe it is headed. To make the work more accessible to the public team members,
Category: Artificial Intelligence

[292] viXra:1706.0207 [pdf] submitted on 2017-06-13 11:45:22

Neural Networks and Quantum Entanglement

Authors: George Rajna
Comments: 39 Pages.

Specifying a number for each connection and mathematically forgetting the hidden neurons can produce a compact representation of many interesting quantum states, including states with topological characteristics and some with surprising amounts of entanglement. [23] Now, a team of researchers at MIT and elsewhere has developed a new approach to such computations, using light instead of electricity, which they say could vastly improve the speed and efficiency of certain deep learning computations. [22] Physicists have found that the structure of certain types of quantum learning algorithms is very similar to their classical counterparts—a finding that will help scientists further develop the quantum versions. [21] We should remain optimistic that quantum computing and AI will continue to improve our lives, but we also should continue to hold companies, organizations, and governments accountable for how our private data is used, as well as the technology's impact on the environment. [20] It's man vs machine this week as Google's artificial intelligence programme AlphaGo faces the world's top-ranked Go player in a contest expected to end in another victory for rapid advances in AI. [19] Google's computer programs are gaining a better understanding of the world, and now it wants them to handle more of the decision-making for the billions of people who use its services. [18] Microsoft on Wednesday unveiled new tools intended to democratize artificial intelligence by enabling machine smarts to be built into software from smartphone games to factory floors. [17] The closer we can get a machine translation to be on par with expert human translation, the happier lots of people struggling with translations will be. [16] Researchers have created a large, open source database to support the development of robot activities based on natural language input. [15] A pair of physicists with ETH Zurich has developed a way to use an artificial neural network to characterize the wave function of a quantum many-body system. [14]
Category: Artificial Intelligence

[291] viXra:1706.0198 [pdf] submitted on 2017-06-14 08:06:29

Robot Write and Play its own Music

Authors: George Rajna
Comments: 38 Pages.

A marimba-playing robot with four arms and eight sticks is writing and playing its own compositions in a lab at the Georgia Institute of Technology. The pieces are generated using artificial intelligence and deep learning. [23] Now, a team of researchers at MIT and elsewhere has developed a new approach to such computations, using light instead of electricity, which they say could vastly improve the speed and efficiency of certain deep learning computations. [22] Physicists have found that the structure of certain types of quantum learning algorithms is very similar to their classical counterparts—a finding that will help scientists further develop the quantum versions. [21] We should remain optimistic that quantum computing and AI will continue to improve our lives, but we also should continue to hold companies, organizations, and governments accountable for how our private data is used, as well as the technology’s impact on the environment. [20] It's man vs machine this week as Google's artificial intelligence programme AlphaGo faces the world's top-ranked Go player in a contest expected to end in another victory for rapid advances in AI. [19] Google's computer programs are gaining a better understanding of the world, and now it wants them to handle more of the decision-making for the billions of people who use its services. [18] Microsoft on Wednesday unveiled new tools intended to democratize artificial intelligence by enabling machine smarts to be built into software from smartphone games to factory floors. [17] The closer we can get a machine translation to be on par with expert human translation, the happier lots of people struggling with translations will be. [16] Researchers have created a large, open source database to support the development of robot activities based on natural language input. [15] A pair of physicists with ETH Zurich has developed a way to use an artificial neural network to characterize the wave function of a quantum many-body system. [14] A team of researchers at Google's DeepMind Technologies has been working on a means to increase the capabilities of computers by combining aspects of data processing and artificial intelligence and have come up with what they are calling a differentiable neural computer (DNC.) In their paper published in the journal Nature, they describe the work they are doing and where they believe it is headed. To make the work more accessible to the public team members, Alexander Graves and Greg Wayne have posted an explanatory page on the DeepMind website. [13] Nobody understands why deep neural networks are so good at solving complex problems. Now physicists say the secret is buried in the laws of physics. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron’s spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[290] viXra:1706.0144 [pdf] submitted on 2017-06-11 07:47:04

Classical and Quantum Machine Learning

Authors: George Rajna
Comments: 35 Pages.

Physicists have found that the structure of certain types of quantum learning algorithms is very similar to their classical counterparts—a finding that will help scientists further develop the quantum versions. [21] We should remain optimistic that quantum computing and AI will continue to improve our lives, but we also should continue to hold companies, organizations, and governments accountable for how our private data is used, as well as the technology's impact on the environment. [20] It's man vs machine this week as Google's artificial intelligence programme AlphaGo faces the world's top-ranked Go player in a contest expected to end in another victory for rapid advances in AI. [19] Google's computer programs are gaining a better understanding of the world, and now it wants them to handle more of the decision-making for the billions of people who use its services. [18] Microsoft on Wednesday unveiled new tools intended to democratize artificial intelligence by enabling machine smarts to be built into software from smartphone games to factory floors. [17] The closer we can get a machine translation to be on par with expert human translation, the happier lots of people struggling with translations will be. [16] Researchers have created a large, open source database to support the development of robot activities based on natural language input. [15] A pair of physicists with ETH Zurich has developed a way to use an artificial neural network to characterize the wave function of a quantum many-body system. [14] A team of researchers at Google's DeepMind Technologies has been working on a means to increase the capabilities of computers by combining aspects of data processing and artificial intelligence and have come up with what they are calling a differentiable neural computer (DNC.) In their paper published in the journal Nature, they describe the work they are doing and where they believe it is headed. To make the work more accessible to the public team members, Alexander Graves and Greg Wayne have posted an explanatory page on the DeepMind website. [13]
Category: Artificial Intelligence

[289] viXra:1705.0404 [pdf] submitted on 2017-05-28 12:05:57

Using Student Learning Based on Fluency for the Learning Rate in a Deep Convolutional Neural Network

Authors: Abien Fred Agarap
Comments: 23 Pages.

This is a proposal for mathematically determining the learning rate to be used in a deep supervised convolutional neural network (CNN), based on student fluency. The CNN model shall be tasked to imitate how students play the game “Packet Attack”, a form of gamification of information security awareness training, and learn in the same rate as the students did. The student fluency shall be represented by a mathematical function constructed using natural cubic spline interpolation, and its derivative shall serve as the learning rate for the CNN model. If proven right, the results will imply a more human-like rate of learning by machines.
Category: Artificial Intelligence

[288] viXra:1705.0362 [pdf] submitted on 2017-05-25 03:53:34

Artificial Intelligence by Quantum Computing

Authors: George Rajna
Comments: 34 Pages.

We should remain optimistic that quantum computing and AI will continue to improve our lives, but we also should continue to hold companies, organizations, and governments accountable for how our private data is used, as well as the technology's impact on the environment. [20] It's man vs machine this week as Google's artificial intelligence programme AlphaGo faces the world's top-ranked Go player in a contest expected to end in another victory for rapid advances in AI. [19] Google's computer programs are gaining a better understanding of the world, and now it wants them to handle more of the decision-making for the billions of people who use its services. [18] Microsoft on Wednesday unveiled new tools intended to democratize artificial intelligence by enabling machine smarts to be built into software from smartphone games to factory floors. [17] The closer we can get a machine translation to be on par with expert human translation, the happier lots of people struggling with translations will be. [16] Researchers have created a large, open source database to support the development of robot activities based on natural language input. [15] A pair of physicists with ETH Zurich has developed a way to use an artificial neural network to characterize the wave function of a quantum many-body system. [14] A team of researchers at Google's DeepMind Technologies has been working on a means to increase the capabilities of computers by combining aspects of data processing and artificial intelligence and have come up with what they are calling a differentiable neural computer (DNC.) In their paper published in the journal Nature, they describe the work they are doing and where they believe it is headed. To make the work more accessible to the public team members, Alexander Graves and Greg Wayne have posted an explanatory page on the DeepMind website. [13] Nobody understands why deep neural networks are so good at solving complex problems. Now physicists say the secret is buried in the laws of physics. [12]
Category: Artificial Intelligence

[287] viXra:1705.0340 [pdf] submitted on 2017-05-22 19:18:05

Verifying the Validity of a Conformant Plan is co-NP-Complete

Authors: Alban Grastien, Enrico Scala
Comments: 3 Pages.

The purpose of this document is to show the complexity of verifying the validity of a deterministic conformant plan. We concentrate on a simple version of the conformant planning problem (i.e., one where there is no precondition on the actions and where all conditions are defined as sets of positive or negative facts) in order to show that the complexity does not come from solving a single such formula.
Category: Artificial Intelligence

[286] viXra:1705.0313 [pdf] submitted on 2017-05-21 09:43:28

Rematch of Man vs Machine

Authors: George Rajna
Comments: 32 Pages.

It's man vs machine this week as Google's artificial intelligence programme AlphaGo faces the world's top-ranked Go player in a contest expected to end in another victory for rapid advances in AI. [19] Google's computer programs are gaining a better understanding of the world, and now it wants them to handle more of the decision-making for the billions of people who use its services. [18] Microsoft on Wednesday unveiled new tools intended to democratize artificial intelligence by enabling machine smarts to be built into software from smartphone games to factory floors. [17] The closer we can get a machine translation to be on par with expert human translation, the happier lots of people struggling with translations will be. [16] Researchers have created a large, open source database to support the development of robot activities based on natural language input. [15] A pair of physicists with ETH Zurich has developed a way to use an artificial neural network to characterize the wave function of a quantum many-body system. [14] A team of researchers at Google's DeepMind Technologies has been working on a means to increase the capabilities of computers by combining aspects of data processing and artificial intelligence and have come up with what they are calling a differentiable neural computer (DNC.) In their paper published in the journal Nature, they describe the work they are doing and where they believe it is headed. To make the work more accessible to the public team members, Alexander Graves and Greg Wayne have posted an explanatory page on the DeepMind website. [13] Nobody understands why deep neural networks are so good at solving complex problems. Now physicists say the secret is buried in the laws of physics. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron’s spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[285] viXra:1705.0273 [pdf] submitted on 2017-05-18 10:06:56

Google Latest Tech Tricks

Authors: George Rajna
Comments: 31 Pages.

Google's computer programs are gaining a better understanding of the world, and now it wants them to handle more of the decision-making for the billions of people who use its services. [18] Microsoft on Wednesday unveiled new tools intended to democratize artificial intelligence by enabling machine smarts to be built into software from smartphone games to factory floors. [17] The closer we can get a machine translation to be on par with expert human translation, the happier lots of people struggling with translations will be. [16] Researchers have created a large, open source database to support the development of robot activities based on natural language input. [15] A pair of physicists with ETH Zurich has developed a way to use an artificial neural network to characterize the wave function of a quantum many-body system. [14] A team of researchers at Google's DeepMind Technologies has been working on a means to increase the capabilities of computers by combining aspects of data processing and artificial intelligence and have come up with what they are calling a differentiable neural computer (DNC.) In their paper published in the journal Nature, they describe the work they are doing and where they believe it is headed. To make the work more accessible to the public team members, Alexander Graves and Greg Wayne have posted an explanatory page on the DeepMind website. [13] Nobody understands why deep neural networks are so good at solving complex problems. Now physicists say the secret is buried in the laws of physics. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10]
Category: Artificial Intelligence

[284] viXra:1705.0223 [pdf] submitted on 2017-05-15 03:07:04

A Novel Pandemonium Architecture Based on Visual Topological Invariants and Mental Matching Descriptions

Authors: Arturo Tozzi, James F Peters
Comments: 13 Pages.

A novel daemon-based architecture is introduced to elucidate some brain functions, such as pattern recognition during human perception and mental interpretation of visual scenes. By taking into account the concepts of invariance and persistence in topology, we introduce a Selfridge pandemonium variant of brain activity that takes into account a novel feature, namely, extended feature daemons that, in addition to the usual recognition of short straight as well as curved lines, recognize topological features of visual scene shapes, such as shape interior, density and texture. A series of transformations can be gradually applied to a pattern, in particular to the shape of an object, without affecting its invariant properties, such as its boundedness and connectedness of the parts of a visual scene. We also introduce another Pandemonium implementation: low-level representations of objects can be mapped to higher-level views (our mental interpretations), making it possible to construct a symbolic multidimensional representation of the environment. The representations can be projected continuously to an object that we have seen and continue to see, thanks to the mapping from shapes in our memory to shapes in Euclidean space. A multidimensional vista detectable by the brain (brainscapes) results from the presence of daemons (mind channels) that detect not only ordinary views of the shapes in visual scenes, but also the features of the shapes. Although perceived shapes are 3-dimensional (3+1 dimensional, if we include time), shape features (volume, colour, contour, closeness, texture, and so on) lead to n-dimensional brainscapes, We arrive at 5 as a minimum shape feature space, since every visual shape has at least a contour in space-time. We discuss the advantages of our parallel, hierarchical model in pattern recognition, computer vision and biological nervous system’s evolution.
Category: Artificial Intelligence

[283] viXra:1705.0217 [pdf] submitted on 2017-05-14 04:25:18

Popular Routes Discovery

Authors: Tal Ben Yakar
Comments: 6 Pages.

Finding the optimal driving route has attracted considerable attention in recent years, the problem sounds simple however different companies these days, taxi alternatives companies like Uber and Via trying to find what is the best route to drive find it as a very challenging problem. Ridesharing and maps companies like HERE, navigation companies like waze and public transportation companies like moovit and others. AI robots in addition, need to have the ability to route in the optimal manner. In this work we formulate the problem of finding optimal routes as an optimization problem and come up with a neat, low memory and fast solution to the problem using machine learning algorithms.
Category: Artificial Intelligence

[282] viXra:1705.0172 [pdf] submitted on 2017-05-10 12:43:05

Democratize Artificial Intelligence

Authors: George Rajna
Comments: 29 Pages.

Microsoft on Wednesday unveiled new tools intended to democratize artificial intelligence by enabling machine smarts to be built into software from smartphone games to factory floors. [17] The closer we can get a machine translation to be on par with expert human translation, the happier lots of people struggling with translations will be. [16] Researchers have created a large, open source database to support the development of robot activities based on natural language input. [15] A pair of physicists with ETH Zurich has developed a way to use an artificial neural network to characterize the wave function of a quantum many-body system. [14] A team of researchers at Google's DeepMind Technologies has been working on a means to increase the capabilities of computers by combining aspects of data processing and artificial intelligence and have come up with what they are calling a differentiable neural computer (DNC.) In their paper published in the journal Nature, they describe the work they are doing and where they believe it is headed. To make the work more accessible to the public team members, Alexander Graves and Greg Wayne have posted an explanatory page on the DeepMind website. [13] Nobody understands why deep neural networks are so good at solving complex problems. Now physicists say the secret is buried in the laws of physics. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9]
Category: Artificial Intelligence

[281] viXra:1705.0108 [pdf] submitted on 2017-05-05 09:20:09

Incorrect Moves and Testable States

Authors: Dimiter Dobrev
Comments: 17 Pages.

How do we describe the invisible? Let’s take a sequence: input, output, input, output ... Behind this sequence stands a world and the sequence of its internal states. We do not see the internal state of the world, but only a part of it. To describe that part which is invisible, we will use the concept of ‘incorrect move’ and its generalization ‘testable state’. Thus, we will reduce the problem of partial observability to the problem of full observability.
Category: Artificial Intelligence

[280] viXra:1705.0094 [pdf] submitted on 2017-05-04 04:17:51

Rotation Invariance Neural Network

Authors: Shiyuan.Li
Comments: 7 Pages.

Rotation invariance and translate invariance have great values in image recognition. In this paper, we bring a new architecture in convolutional neural network (CNN) to achieve rotation invariance and translate invariance in 2-D symbol recognition. We can also get the position and orientation of the 2-D symbol by the network to achieve detection purpose for multiple non-overlap target. Human being have the ability look at an object by one glance and remember it, we also can use this architecture to achieve this one shot learning.
Category: Artificial Intelligence

[279] viXra:1705.0027 [pdf] submitted on 2017-05-02 21:38:43

Obstacle Detection and Pathfinding for Mobile Robots

Authors: Murat Arslan
Comments: 116 Pages.

In this thesis, obstacle detection via image of objects and then pathfinding problems of NAO humanoid robot is considered. NAO's camera is used to capture the images of world map. The captured image is processed and classified into two classes; area with obstacles and area without obstacles. For classification of images, Support Vector Machine (SVM) is used. After classification the map of world is obtained as area with obstacles and area without obstacles. This map is input for path finding algorithm. In the thesis A* path finding algorithm is used to find path from the start point to the goal. The aim of this work is to implement a support vector machine based solution to robot guidance problem, visual path planning and obstacle avoidance. The used algorithms allow to detect obstacles and find an optimal path. The thesis describe basic steps of navigation of mobile robots.
Category: Artificial Intelligence

[278] viXra:1704.0353 [pdf] submitted on 2017-04-26 06:56:36

Artificial Synapse

Authors: George Rajna
Comments: 29 Pages.

Researchers from France and the University of Arkansas have created an artificial synapse capable of autonomous learning, a component of artificial intelligence. [17] Intelligent machines of the future will help restore memory, mind your children, fetch your coffee and even care for aging parents. [16] Unlike experimental neuroscientists who deal with real-life neurons, computational neuroscientists use model simulations to investigate how the brain functions. [15] A pair of physicists with ETH Zurich has developed a way to use an artificial neural network to characterize the wave function of a quantum many-body system. [14] A team of researchers at Google's DeepMind Technologies has been working on a means to increase the capabilities of computers by combining aspects of data processing and artificial intelligence and have come up with what they are calling a differentiable neural computer (DNC.) In their paper published in the journal Nature, they describe the work they are doing and where they believe it is headed. To make the work more accessible to the public team members, Alexander Graves and Greg Wayne have posted an explanatory page on the DeepMind website. [13] Nobody understands why deep neural networks are so good at solving complex problems. Now physicists say the secret is buried in the laws of physics. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9]
Category: Artificial Intelligence

[277] viXra:1704.0337 [pdf] submitted on 2017-04-26 03:14:10

Digital Assistant

Authors: George Rajna
Comments: 29 Pages.

Intelligent machines of the future will help restore memory, mind your children, fetch your coffee and even care for aging parents. [16] Unlike experimental neuroscientists who deal with real-life neurons, computational neuroscientists use model simulations to investigate how the brain functions. [15] A pair of physicists with ETH Zurich has developed a way to use an artificial neural network to characterize the wave function of a quantum many-body system. [14] A team of researchers at Google's DeepMind Technologies has been working on a means to increase the capabilities of computers by combining aspects of data processing and artificial intelligence and have come up with what they are calling a differentiable neural computer (DNC.) In their paper published in the journal Nature, they describe the work they are doing and where they believe it is headed. To make the work more accessible to the public team members, Alexander Graves and Greg Wayne have posted an explanatory page on the DeepMind website. [13] Nobody understands why deep neural networks are so good at solving complex problems. Now physicists say the secret is buried in the laws of physics. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[276] viXra:1704.0308 [pdf] submitted on 2017-04-23 11:14:37

3D Printed Dancing Humanoid Robot “Buddy” for Homecare

Authors: Akshay Potnuru, Mohsen Jafarzadeh, Yonas Tadesse
Comments: 6 Pages.

This paper describes a 3D printed humanoid robot that can perform dancing and demonstrate human-like facial expressions to expand humanoid robotics in entertainment and at the same time to have an assistive role for children and elderly people. The humanoid is small and has an expressive face that is in a comfort zone for a child or an older person. It can maneuver in a day care or home care environment using its wheeled base. This paper discusses on the capabilities of the robot to carry and handle small loads like pills, common measurement tools such as pressure and temperature measurement units. The paper also discusses the use of IP camera for color identification and an Arduino based audio system to synchronize music with dance movements of the robot.
Category: Artificial Intelligence

[275] viXra:1704.0307 [pdf] submitted on 2017-04-23 11:21:17

Humanoid Robot Path Planning with Fuzzy Markov Decision Processes‏

Authors: Mahdi Fakoor, Amirreza Kosari, Mohsen Jafarzadeh
Comments: 11 Pages.

In contrast to the case of known environments, path planning in unknown environments, mostly for humanoid robots, is yet to be opened for further development. This is mainly attributed to the fact that obtaining thorough sensory information about an unknown environment is not functionally or economically applicable. This study alleviates the latter problem by resorting to a novel approach through which the decision is made according to fuzzy Markov decision processes (FMDP), with regard to the pace. The experimental results show the efficiency of the proposed method.
Category: Artificial Intelligence

[274] viXra:1704.0298 [pdf] submitted on 2017-04-22 19:23:44

Design and Motion Control of Bioinspired Humanoid Robot Head from Servo Motors Toward Artificial Muscles

Authors: Yara Almubarak, Yonas Tadesse
Comments: 9 Pages.

The potential applications of humanoid robots in social environments, motivates researchers to design, and control biomimetic humanoid robots. Generally, people are more interested to interact with robots that have similar attributes and movements to humans. The head is one of most important part of any social robot. Currently, most humanoid heads use electrical motors, pneumatic actuators, and shape memory alloy (SMA) actuators for actuation. Electrical and pneumatic actuators take most of the space and would cause unsmooth motions. SMAs are expensive to use in humanoids. Recently, in many robotic projects, Twisted and Coiled Polymer (TCP) artificial muscles are used as linear actuators which take up little space compared to the motors. In this paper, we will demonstrate the designing process and motion control of a robotic head with TCP muscles. Servo motors and artificial muscles are used for actuating the head motion, which have been controlled by a cost efficient ARM Cortex-M7 based development board. A complete comparison between the two actuators is presented.
Category: Artificial Intelligence

[273] viXra:1704.0205 [pdf] submitted on 2017-04-17 01:40:24

Formula Analyzer: Find the Formula by Parameters

Authors: Artur Eduardovich Sibgatullin
Comments: 27 Pages. MIT License, https://figshare.com/articles/Formula_analyzer_Find_the_formula_by_parameters/4880012

Let it be a formula, e.g.: x + y^2 - z = r. It is usually necessary to find a parameter’s value by knowing others’ ones. However, let’s set another problem to find the formula itself, knowing only its parameters. The solution of such a problem we call reverse computing. For that we'll create an algorithm and accomplish it as a program code.
Category: Artificial Intelligence

[272] viXra:1704.0113 [pdf] submitted on 2017-04-09 11:21:19

Automatic Speech Recognition

Authors: George Rajna
Comments: 27 Pages.

The closer we can get a machine translation to be on par with expert human translation, the happier lots of people struggling with translations will be. [16] Researchers have created a large, open source database to support the development of robot activities based on natural language input. [15] A pair of physicists with ETH Zurich has developed a way to use an artificial neural network to characterize the wave function of a quantum many-body system. [14] A team of researchers at Google's DeepMind Technologies has been working on a means to increase the capabilities of computers by combining aspects of data processing and artificial intelligence and have come up with what they are calling a differentiable neural computer (DNC.) In their paper published in the journal Nature, they describe the work they are doing and where they believe it is headed. To make the work more accessible to the public team members, Alexander Graves and Greg Wayne have posted an explanatory page on the DeepMind website. [13] Nobody understands why deep neural networks are so good at solving complex problems. Now physicists say the secret is buried in the laws of physics. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[271] viXra:1704.0090 [pdf] submitted on 2017-04-07 11:26:30

Toward Self-Govern and Self-Protected Data: a Proposal

Authors: Kasra Madadipouya
Comments: 3 Pages. Unpublished research proposal

We live in an era of an explosion of data. The rate of generating data has been increased significantly in the last few years especially by popularization of Web 2.0. In addition to that, our surrounding environments are becoming more dynamics and rapidly emerging as computing systems morph from monolithic and closed entities into globally disaggregated collaborating entities which require sensitive data sharing. As an instance content owners lose full control of their data once it is given away to consumers and hence data can be unlimitedly copied, access, modified and redistributed without data owner awareness.
Category: Artificial Intelligence

[270] viXra:1704.0089 [pdf] submitted on 2017-04-07 11:40:51

Machine Learning Chip

Authors: George Rajna
Comments: 23 Pages.

Google has said the TPU beat Nvidia and Intel. Let's explain that. There is so much to explain. TPU stands for Tensor Processing Unit. This is described by a Google engineer as "an entirely new class of custom machine learning accelerator." [14] Machine learning algorithms are designed to improve as they encounter more data, making them a versatile technology for understanding large sets of photos such as those accessible from Google Images. Elizabeth Holm, professor of materials science and engineering at Carnegie Mellon University, is leveraging this technology to better understand the enormous number of research images accumulated in the field of materials science. [13] With the help of artificial intelligence, chemists from the University of Basel in Switzerland have computed the characteristics of about two million crystals made up of four chemical elements. The researchers were able to identify 90 previously unknown thermodynamically stable crystals that can be regarded as new materials. [12] The artificial intelligence system's ability to set itself up quickly every morning and compensate for any overnight fluctuations would make this fragile technology much more useful for field measurements, said co-lead researcher Dr Michael Hush from UNSW ADFA. [11] Quantum physicist Mario Krenn and his colleagues in the group of Anton Zeilinger from the Faculty of Physics at the University of Vienna and the Austrian Academy of Sciences have developed an algorithm which designs new useful quantum experiments. As the computer does not rely on human intuition, it finds novel unfamiliar solutions. [10] Researchers at the University of Chicago's Institute for Molecular Engineering and the University of Konstanz have demonstrated the ability to generate a quantum logic operation, or rotation of the qubit, that-surprisingly—is intrinsically resilient to noise as well as to variations in the strength or duration of the control. Their achievement is based on a geometric concept known as the Berry phase and is implemented through entirely optical means within a single electronic spin in diamond. [9] New research demonstrates that particles at the quantum level can in fact be seen as behaving something like billiard balls rolling along a table, and not merely as the probabilistic smears that the standard interpretation of quantum mechanics suggests. But there's a catch-the tracks the particles follow do not always behave as one would expect from "realistic" trajectories, but often in a fashion that has been termed "surrealistic." [8] Quantum entanglement—which occurs when two or more particles are correlated in such a way that they can influence each other even across large distances—is not an all-or-nothing phenomenon, but occurs in various degrees. The more a quantum state is entangled with its partner, the better the states will perform in quantum information applications. Unfortunately, quantifying entanglement is a difficult process involving complex optimization problems that give even physicists headaches. [7] A trio of physicists in Europe has come up with an idea that they believe would allow a person to actually witness entanglement. Valentina Caprara Vivoli, with the University of Geneva, Pavel Sekatski, with the University of Innsbruck and Nicolas Sangouard, with the University of Basel, have together written a paper describing a scenario where a human subject would be able to witness an instance of entanglement—they have uploaded it to the arXiv server for review by others. [6] The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the relativistic quantum theory.
Category: Artificial Intelligence

[269] viXra:1704.0022 [pdf] submitted on 2017-04-03 08:49:50

Visualizing Scientific Big Data

Authors: George Rajna
Comments: 32 Pages.

Humans are visual creatures: our brain processes images 60,000 times faster than text, and 90 percent of information sent to the brain is visual. Visualization is becoming increasingly useful in the era of big data, in which we are generating so much data at such high rates that we cannot keep up with making sense of it all. In particular, visual analytics—a research discipline that combines automated data analysis with interactive visualizations—has emerged as a promising approach to dealing with this information overload. [18] Neural networks are commonly used today to analyze complex data – for instance to find clues to illnesses in genetic information. Ultimately, though, no one knows how these networks actually work exactly. [17] Hey Siri, how's my hair?" Your smartphone may soon be able to give you an honest answer, thanks to a new machine learning algorithm designed by U of T Engineering researchers Parham Aarabi and Wenzhi Guo. [16] Researchers at Lancaster University's Data Science Institute have developed a software system that can for the first time rapidly self-assemble into the most efficient form without needing humans to tell it what to do. [15] Physicists have shown that quantum effects have the potential to significantly improve a variety of interactive learning tasks in machine learning. [14] A Chinese team of physicists have trained a quantum computer to recognise handwritten characters, the first demonstration of " quantum artificial intelligence ". Physicists have long claimed that quantum computers have the potential to dramatically outperform the most powerful conventional processors. The secret sauce at work here is the strange quantum phenomenon of superposition, where a quantum object can exist in two states at the same time. [13] One of biology's biggest mysteries-how a sliced up flatworm can regenerate into new organisms-has been solved independently by a computer. The discovery marks the first time that a computer has come up with a new scientific theory without direct human help. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11]
Category: Artificial Intelligence

[268] viXra:1704.0021 [pdf] submitted on 2017-04-03 09:15:46

Electronic Synapses Artificial Brain

Authors: George Rajna
Comments: 33 Pages.

Researchers from the CNRS, Thales, and the Universities of Bordeaux, Paris-Sud, and Evry have created an artificial synapse capable of learning autonomously. They were also able to model the device, which is essential for developing more complex circuits. [19] Humans are visual creatures: our brain processes images 60,000 times faster than text, and 90 percent of information sent to the brain is visual. Visualization is becoming increasingly useful in the era of big data, in which we are generating so much data at such high rates that we cannot keep up with making sense of it all. In particular, visual analytics—a research discipline that combines automated data analysis with interactive visualizations—has emerged as a promising approach to dealing with this information overload. [18] Neural networks are commonly used today to analyze complex data – for instance to find clues to illnesses in genetic information. Ultimately, though, no one knows how these networks actually work exactly. [17] Hey Siri, how's my hair?" Your smartphone may soon be able to give you an honest answer, thanks to a new machine learning algorithm designed by U of T Engineering researchers Parham Aarabi and Wenzhi Guo. [16] Researchers at Lancaster University's Data Science Institute have developed a software system that can for the first time rapidly self-assemble into the most efficient form without needing humans to tell it what to do. [15] Physicists have shown that quantum effects have the potential to significantly improve a variety of interactive learning tasks in machine learning. [14] A Chinese team of physicists have trained a quantum computer to recognise handwritten characters, the first demonstration of " quantum artificial intelligence ". Physicists have long claimed that quantum computers have the potential to dramatically outperform the most powerful conventional processors. The secret sauce at work here is the strange quantum phenomenon of superposition, where a quantum object can exist in two states at the same time. [13] One of biology's biggest mysteries-how a sliced up flatworm can regenerate into new organisms-has been solved independently by a computer. The discovery marks the first time that a computer has come up with a new scientific theory without direct human help. [12]
Category: Artificial Intelligence

[267] viXra:1703.0233 [pdf] submitted on 2017-03-24 10:36:41

Parallel Computation and Brain Function

Authors: George Rajna
Comments: 26 Pages.

Unlike experimental neuroscientists who deal with real-life neurons, computational neuroscientists use model simulations to investigate how the brain functions. [15] A pair of physicists with ETH Zurich has developed a way to use an artificial neural network to characterize the wave function of a quantum many-body system. [14] A team of researchers at Google's DeepMind Technologies has been working on a means to increase the capabilities of computers by combining aspects of data processing and artificial intelligence and have come up with what they are calling a differentiable neural computer (DNC.) In their paper published in the journal Nature, they describe the work they are doing and where they believe it is headed. To make the work more accessible to the public team members, Alexander Graves and Greg Wayne have posted an explanatory page on the DeepMind website. [13] Nobody understands why deep neural networks are so good at solving complex problems. Now physicists say the secret is buried in the laws of physics. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[266] viXra:1703.0063 [pdf] submitted on 2017-03-07 09:36:39

Human Readable Feature Generation for Natural Language Corpora

Authors: Tomasz Dryjanski
Comments: 4 Pages.

This paper proposes an alternative to the Paragraph Vector algorithm, generating fixed-length vectors of human-readable features for natural language corpora. It extends word2vec retaining its other advantages like speed and accuracy, hence its proposed name is doc2feat. Extracted features are presented as lists of words with their proximity to the particular feature, allowing interpretation and manual annotation. By parameter tuning focus can be made on grammatical aspects of the corpus language, making it useful for linguistic applications. The algorithm can run on variable-length pieces of texts, and provides insight into what features are relevant for text classification or sentiment analysis. The corpus does not have to, and in specific cases should not be, preprocessed with stemming or stop-words removal.
Category: Artificial Intelligence

[265] viXra:1703.0056 [pdf] submitted on 2017-03-06 13:57:49

Quantum Machine Learning to Infinite Dimensions

Authors: George Rajna
Comments: 27 Pages.

Physicists have developed a quantum machine learning algorithm that can handle infinite dimensions—that is, it works with continuous variables (which have an infinite number of possible values on a closed interval) instead of the typically used discrete variables (which have only a finite number of values). [15] Physicists have shown that quantum effects have the potential to significantly improve a variety of interactive learning tasks in machine learning. [14] A Chinese team of physicists have trained a quantum computer to recognise handwritten characters, the first demonstration of " quantum artificial intelligence ". Physicists have long claimed that quantum computers have the potential to dramatically outperform the most powerful conventional processors. The secret sauce at work here is the strange quantum phenomenon of superposition, where a quantum object can exist in two states at the same time. [13] One of biology's biggest mysteries-how a sliced up flatworm can regenerate into new organisms-has been solved independently by a computer. The discovery marks the first time that a computer has come up with a new scientific theory without direct human help. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[264] viXra:1703.0013 [pdf] submitted on 2017-03-02 05:44:53

Controlling a Robot Using a Wearable Device (MYO)

Authors: Mithileysh Sathiyanarayanan, Tobias Mulling, Bushra Nazir
Comments: 6 Pages. IJEDR, 2015, Vol 3, Issue 3

There is a huge demand for military robots in almost all the countries which comes under the field of human computer interaction and artificial intelligence. There are many different ways of operating a robot: self controlled, automatic controlled etc. Also, gesture controlled operation mode is on the rise. This acted as our motivation to develop a gesture controlled robot using MYO armband. The word ‘MYO’ has created a buzz in the technological world by its astonishing features and its utility in various fields. Its introduction as a armband that can wrap around our arm to control robots with our movements and gestures has opened new wide doors of its experimentation with robotics. This independently working gesture recognition system does not rely on any external sensors (motion capturing system) as it has its sensors embedded in itself which recognizes the gesture commands and acts accordingly. This armband can be worn by soldiers to operate robots to fight against enemies. This work in progress paper illustrates an existing robot designed by us, which can be controlled by hand gestures using a wearable device called as MYO. We would like to investigate more on this and implement, such that the robot can be interfaced with a MYO armband for a successful control.
Category: Artificial Intelligence

[263] viXra:1703.0012 [pdf] submitted on 2017-03-02 05:49:01

Leap Motion Device for Gesture Controlling an Unmanned Ground Vehicle (Robot)

Authors: Mithileysh Sathiyanarayanan, Tobias Mulling, Bushra Nazir
Comments: 10 Pages. IJEDR, 2016, Vol 4, Issue 4

A new scope of human-computer interaction utilizes the algorithms of computer vision and image processing for detecting the gesture, understanding its objective and making it meaningful for the computer to understand and then interact with the humans. The recent introduction of "Leap Motion" is a big revolution in the field of gesture control technology. Using gesture control mode in the field of robotics is also on the rise. This acted as our motivation to develop a gesture controlled robot using a Leap Motion Device that can sense human hands above it and to keep a track of them and aid in navigation. This independently working gesture recognition system does not rely on any external sensors (motion capturing system) as it has its sensors embedded in itself which recognizes the gesture commands and acts accordingly. The soldiers need not wear any physical device on their body (unlike Kinect and/or MYO Armband) to operate robots to fight against enemies. This work in progress paper illustrates an existing robot designed by us, which can be controlled by hand gestures using a non-wearable (touchless) device called as Leap Motion.
Category: Artificial Intelligence

[262] viXra:1702.0297 [pdf] submitted on 2017-02-23 18:45:36

Some General Results On Overfitting In Machine Learning

Authors: Antony Van der Mude
Comments: 13 Pages.

Overfitting has always been a problem in machine learning. Recently a related phenomenon called “oversearching” has been analyzed. This paper takes a theoretical approach using a very general methodology covering most learning paradigms in current use. Overfitting is defined in terms of the “expressive accuracy” of a model for the data, rather than “predictive accuracy”. The results show that even if the learner can identify a set of best models, overfitting will cause it to bounce from one model to another. Overfitting is ameliorated by having the learner bound the search space, and bounding is equivalent to using an accuracy (or bias) more restrictive than the problem accuracy. Also, Ramsey’s Theorem shows that every data sequence has an situation where either consistent overfitting or underfitting is unavoidable. We show that oversearching is simply overfitting where the resource used to express a model is the search space itself rather than a more common resource such as a program that executes the model. We show that the smallest data sequence guessing a model defines a canonical resource. There is an equivalence in the limit between any two resources to express the same model space, but it may not be effectively computable.
Category: Artificial Intelligence

[261] viXra:1702.0275 [pdf] submitted on 2017-02-22 09:32:33

Quantum Artificial Biomimetics

Authors: George Rajna
Comments: 26 Pages.

Quantum biomimetics consists of reproducing in quantum systems certain properties exclusive to living organisms. Researchers at University of the Basque Country have imitated natural selection, learning and memory in a new study. The mechanisms developed could give quantum computation a boost and facilitate the learning process in machines. [14] A Chinese team of physicists have trained a quantum computer to recognise handwritten characters, the first demonstration of " quantum artificial intelligence ". Physicists have long claimed that quantum computers have the potential to dramatically outperform the most powerful conventional processors. The secret sauce at work here is the strange quantum phenomenon of superposition, where a quantum object can exist in two states at the same time. [13] One of biology's biggest mysteries-how a sliced up flatworm can regenerate into new organisms-has been solved independently by a computer. The discovery marks the first time that a computer has come up with a new scientific theory without direct human help. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[260] viXra:1702.0232 [pdf] submitted on 2017-02-18 07:11:56

New Materials from Small Data

Authors: George Rajna
Comments: 22 Pages.

Finding new functional materials is always tricky. But searching for very specific properties among a relatively small family of known materials is even more difficult. [14] Machine learning algorithms are designed to improve as they encounter more data, making them a versatile technology for understanding large sets of photos such as those accessible from Google Images. Elizabeth Holm, professor of materials science and engineering at Carnegie Mellon University, is leveraging this technology to better understand the enormous number of research images accumulated in the field of materials science. [13] With the help of artificial intelligence, chemists from the University of Basel in Switzerland have computed the characteristics of about two million crystals made up of four chemical elements. The researchers were able to identify 90 previously unknown thermodynamically stable crystals that can be regarded as new materials. [12] The artificial intelligence system's ability to set itself up quickly every morning and compensate for any overnight fluctuations would make this fragile technology much more useful for field measurements, said co-lead researcher Dr Michael Hush from UNSW ADFA. [11] Quantum physicist Mario Krenn and his colleagues in the group of Anton Zeilinger from the Faculty of Physics at the University of Vienna and the Austrian Academy of Sciences have developed an algorithm which designs new useful quantum experiments. As the computer does not rely on human intuition, it finds novel unfamiliar solutions. [10] Researchers at the University of Chicago's Institute for Molecular Engineering and the University of Konstanz have demonstrated the ability to generate a quantum logic operation, or rotation of the qubit, that-surprisingly—is intrinsically resilient to noise as well as to variations in the strength or duration of the control. Their achievement is based on a geometric concept known as the Berry phase and is implemented through entirely optical means within a single electronic spin in diamond. [9] New research demonstrates that particles at the quantum level can in fact be seen as behaving something like billiard balls rolling along a table, and not merely as the probabilistic smears that the standard interpretation of quantum mechanics suggests. But there's a catch-the tracks the particles follow do not always behave as one would expect from "realistic" trajectories, but often in a fashion that has been termed "surrealistic." [8] Quantum entanglement—which occurs when two or more particles are correlated in such a way that they can influence each other even across large distances—is not an all-or-nothing phenomenon, but occurs in various degrees. The more a quantum state is entangled with its partner, the better the states will perform in quantum information applications. Unfortunately, quantifying entanglement is a difficult process involving complex optimization problems that give even physicists headaches. [7] A trio of physicists in Europe has come up with an idea that they believe would allow a person to actually witness entanglement. Valentina Caprara Vivoli, with the University of Geneva, Pavel Sekatski, with the University of Innsbruck and Nicolas Sangouard, with the University of Basel, have together written a paper describing a scenario where a human subject would be able to witness an instance of entanglement—they have uploaded it to the arXiv server for review by others. [6] The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the relativistic quantum theory.
Category: Artificial Intelligence

[259] viXra:1702.0229 [pdf] submitted on 2017-02-18 02:21:36

A.i. Music Duet

Authors: George Rajna
Comments: 27 Pages.

An artificial intelligence experiment has emerged of the most enjoyable kind: It is called "A.I. Duet." [16] Researchers have created a large, open source database to support the development of robot activities based on natural language input. [15] A pair of physicists with ETH Zurich has developed a way to use an artificial neural network to characterize the wave function of a quantum many-body system. [14] A team of researchers at Google's DeepMind Technologies has been working on a means to increase the capabilities of computers by combining aspects of data processing and artificial intelligence and have come up with what they are calling a differentiable neural computer (DNC.) In their paper published in the journal Nature, they describe the work they are doing and where they believe it is headed. To make the work more accessible to the public team members, Alexander Graves and Greg Wayne have posted an explanatory page on the DeepMind website. [13] Nobody understands why deep neural networks are so good at solving complex problems. Now physicists say the secret is buried in the laws of physics. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer.
Category: Artificial Intelligence

[258] viXra:1702.0143 [pdf] submitted on 2017-02-12 10:31:13

Human Motion and Language

Authors: George Rajna
Comments: 26 Pages.

Researchers have created a large, open source database to support the development of robot activities based on natural language input. [15] A pair of physicists with ETH Zurich has developed a way to use an artificial neural network to characterize the wave function of a quantum many-body system. [14] A team of researchers at Google's DeepMind Technologies has been working on a means to increase the capabilities of computers by combining aspects of data processing and artificial intelligence and have come up with what they are calling a differentiable neural computer (DNC.) In their paper published in the journal Nature, they describe the work they are doing and where they believe it is headed. To make the work more accessible to the public team members, Alexander Graves and Greg Wayne have posted an explanatory page on the DeepMind website. [13] Nobody understands why deep neural networks are so good at solving complex problems. Now physicists say the secret is buried in the laws of physics. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[257] viXra:1702.0130 [pdf] submitted on 2017-02-10 09:42:04

Artificial Neural Network

Authors: George Rajna
Comments: 25 Pages.

A pair of physicists with ETH Zurich has developed a way to use an artificial neural network to characterize the wave function of a quantum many-body system. [14] A team of researchers at Google's DeepMind Technologies has been working on a means to increase the capabilities of computers by combining aspects of data processing and artificial intelligence and have come up with what they are calling a differentiable neural computer (DNC.) In their paper published in the journal Nature, they describe the work they are doing and where they believe it is headed. To make the work more accessible to the public team members, Alexander Graves and Greg Wayne have posted an explanatory page on the DeepMind website. [13] Nobody understands why deep neural networks are so good at solving complex problems. Now physicists say the secret is buried in the laws of physics. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[256] viXra:1702.0094 [pdf] submitted on 2017-02-07 10:15:12

Complex Neutrosophic Soft Set

Authors: Said Broumi, Assia Bakali, Mohamed Talea, Florentin Smarandache, Mumtaz Ali, Ganeshsree Selvachandran
Comments: 6 Pages.

In this paper, we propose the complex neutrosophic soft set model, which is a hybrid of complex fuzzy sets,neutrosophic sets and soft sets. The basic set theoretic operations and some concepts related to the structure of this model are introduced, and illustrated. An example related to a decision making problem involving uncertain and subjective information is presented, to demonstrate the utility of this model.
Category: Artificial Intelligence

[255] viXra:1702.0010 [pdf] submitted on 2017-02-01 08:18:01

Visualize Complex Learning Processes

Authors: George Rajna
Comments: 29 Pages.

Neural networks are commonly used today to analyze complex data – for instance to find clues to illnesses in genetic information. Ultimately, though, no one knows how these networks actually work exactly. [17] Hey Siri, how's my hair?" Your smartphone may soon be able to give you an honest answer, thanks to a new machine learning algorithm designed by U of T Engineering researchers Parham Aarabi and Wenzhi Guo. [16] Researchers at Lancaster University's Data Science Institute have developed a software system that can for the first time rapidly self-assemble into the most efficient form without needing humans to tell it what to do. [15] Physicists have shown that quantum effects have the potential to significantly improve a variety of interactive learning tasks in machine learning. [14] A Chinese team of physicists have trained a quantum computer to recognise handwritten characters, the first demonstration of " quantum artificial intelligence ". Physicists have long claimed that quantum computers have the potential to dramatically outperform the most powerful conventional processors. The secret sauce at work here is the strange quantum phenomenon of superposition, where a quantum object can exist in two states at the same time. [13] One of biology's biggest mysteries-how a sliced up flatworm can regenerate into new organisms-has been solved independently by a computer. The discovery marks the first time that a computer has come up with a new scientific theory without direct human help. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10]
Category: Artificial Intelligence

[254] viXra:1702.0008 [pdf] submitted on 2017-02-01 08:56:06

First Passage Under Restart

Authors: George Rajna
Comments: 31 Pages.

Discovering the ways in which many seemingly diverse phenomena are related is one of the overarching goals of scientific inquiry, since universality often allows an insight in one area to be extended to many other areas. [18] Neural networks are commonly used today to analyze complex data – for instance to find clues to illnesses in genetic information. Ultimately, though, no one knows how these networks actually work exactly. [17] Hey Siri, how's my hair?" Your smartphone may soon be able to give you an honest answer, thanks to a new machine learning algorithm designed by U of T Engineering researchers Parham Aarabi and Wenzhi Guo. [16] Researchers at Lancaster University's Data Science Institute have developed a software system that can for the first time rapidly self-assemble into the most efficient form without needing humans to tell it what to do. [15] Physicists have shown that quantum effects have the potential to significantly improve a variety of interactive learning tasks in machine learning. [14] A Chinese team of physicists have trained a quantum computer to recognise handwritten characters, the first demonstration of “quantum artificial intelligence”. Physicists have long claimed that quantum computers have the potential to dramatically outperform the most powerful conventional processors. The secret sauce at work here is the strange quantum phenomenon of superposition, where a quantum object can exist in two states at the same time. [13] One of biology's biggest mysteries - how a sliced up flatworm can regenerate into new organisms - has been solved independently by a computer. The discovery marks the first time that a computer has come up with a new scientific theory without direct human help. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron’s spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[253] viXra:1701.0574 [pdf] submitted on 2017-01-22 21:33:04

The Relationship Between Agents and Link-Level Acknowledgements Using Mugwump

Authors: Thomas Lambert
Comments: 8 Pages.

In recent years, much research has been devoted to the improvement of architecture; unfortunately, few have explored the emulation of the World Wide Web. In fact, few biologists would disagree with the deployment of evolutionary programming. While this discussion is never a confirmed intent, it is derived from known results. Mugwump, our new framework for hash tables [28], is the solution to all of these challenges.
Category: Artificial Intelligence

[252] viXra:1701.0559 [pdf] submitted on 2017-01-21 11:05:33

AI Systems See the World as Humans

Authors: George Rajna
Comments: 38 Pages.

A Northwestern University team developed a new computational model that performs at human levels on a standard intelligence test. This work is an important step toward making artificial intelligence systems that see and understand the world as humans do. [25] Neuroscience and artificial intelligence experts from Rice University and Baylor College of Medicine have taken inspiration from the human brain in creating a new "deep learning" method that enables computers to learn about the visual world largely on their own, much as human babies do. [24]
Category: Artificial Intelligence

[251] viXra:1701.0530 [pdf] submitted on 2017-01-17 20:03:50

Intelligence of Crowd.

Authors: Michail Zak
Comments: 17 Pages.

A new class of dynamical systems with a preset type of interference of probabilities is introduced. It is obtained from the extension of the Madelung equation by replacing the quantum potential with a specially selected feedback from the Liouville equation. It has been proved that these systems are different from both Newtonian and quantum systems, but they can be useful for modeling spontaneous collective novelty phenomena when emerging outputs are qualitatively different from the weighted sum of individual inputs. Formation of language and fast decision-making process as potential applications of the probability interference is discussed.
Category: Artificial Intelligence

[250] viXra:1701.0516 [pdf] submitted on 2017-01-16 14:14:27

Optimal Control Via Self-Generated Stochasticity.

Authors: Michail Zak
Comments: 19 Pages.

Stochastic approach to maximization of a functional constrained by governing equation of a controlled system is introduced and discussed. The idea of the proposed algorithm is the following: represent the functional to be maximized as a limit of a probability density governed by the appropriately selected Liouville equation. Then the corresponding ODE become stochastic, and that sample of the solution which has the largest value will have the highest probability to appear in ODE simulation. Application to optimal control is discussed. Two limitations of optimal control theory - local maxima and possible instability of the optimal solutions - are removed. Special attention is paid to robot motion planning.
Category: Artificial Intelligence

[249] viXra:1612.0403 [pdf] submitted on 2016-12-30 06:29:12

Applications of Machine Learning in Estimating the Minimum Distance of Approach of an NEO

Authors: Jayant Mehra
Comments: 15 Pages, 6 Figures, 5 Tables

Although the current detection techniques have been able to calculate the minimum distance to which a Near Earth Object (NEO) can approach Earth for thousands of NEOs, there are millions of yet undiscovered NEOs which could pose a threat to Planet Earth. An NEO is considered highly dangerous if the minimum distance between it and the centre of the Earth is less than 0.03 AU. However, only a handful NEOs have been detected prior to entering this danger zone. The immense task of asteroid hunting by conventional techniques is further complicated by a high number of false positives and false negatives. In this report, machine learning algorithms are written to predict the minimum distance upto which an NEO can approach the planet and classify NEOs as whether they are in the danger zone or no based on their physical characteristics. In section 4 of the study, an Artificial Neural Network based on the backpropagation algorithm and a Logistic Classification based on Unconstrained Minimisation using the fminunc function are employed to classify NEOs with an accuracy of 92% and 90% respectively. In section 5 of the report, the Levenberg - Marquardt Algorithm based on an Artificial Neural Network is employed to calculate the minimum distance with a regression R value of 0.79 (Value of 1 being the maximum). All the algorithmic systems developed have low false positive and false negative rates
Category: Artificial Intelligence

[248] viXra:1612.0344 [pdf] submitted on 2016-12-26 10:03:47

Advance Artificial Super-Intelligence

Authors: Miguel A. Sanchez-Rey
Comments: 3 Pages.

From FL to AL.
Category: Artificial Intelligence

[247] viXra:1612.0314 [pdf] submitted on 2016-12-21 07:33:22

Spintronics-Based Artificial Intelligence

Authors: George Rajna
Comments: 45 Pages.

Researchers at Tohoku University have, for the first time, successfully demonstrated the basic operation of spintronics-based artificial intelligence. [27] The neural structure we use to store and process information in verbal working memory is more complex than previously understood, finds a new study by researchers at New York University. [26] Surviving breast cancer changed the course of Regina Barzilay's research. The experience showed her, in stark relief, that oncologists and their patients lack tools for data-driven decision making. [25] New research, led by the University of Southampton, has demonstrated that a nanoscale device, called a memristor, could be used to power artificial systems that can mimic the human brain. [24] Scientists at Helmholtz-Zentrum Dresden-Rossendorf conducted electricity through DNA-based nanowires by placing gold-plated nanoparticles on them. In this way it could become possible to develop circuits based on genetic material. [23] Researchers at the Nanoscale Transport Physics Laboratory from the School of Physics at the University of the Witwatersrand have found a technique to improve carbon superlattices for quantum electronic device applications. [22] The researchers have found that these previously underestimated interactions can play a significant role in preventing heat dissipation in microelectronic devices. [21] LCLS works like an extraordinary strobe light: Its ultrabright X-rays take snapshots of materials with atomic resolution and capture motions as fast as a few femtoseconds, or millionths of a billionth of a second. For comparison, one femtosecond is to a second what seven minutes is to the age of the universe. [20] A 'nonlinear' effect that seemingly turns materials transparent is seen for the first time in X-rays at SLAC's LCLS. [19] Leiden physicists have manipulated light with large artificial atoms, so-called quantum dots. Before, this has only been accomplished with actual atoms. It is an important step toward light-based quantum technology. [18] In a tiny quantum prison, electrons behave quite differently as compared to their counterparts in free space. They can only occupy discrete energy levels.
Category: Artificial Intelligence

[246] viXra:1612.0288 [pdf] submitted on 2016-12-18 09:03:13

Neuroscience and Artificial Intelligence

Authors: George Rajna
Comments: 37 Pages.

Neuroscience and artificial intelligence experts from Rice University and Baylor College of Medicine have taken inspiration from the human brain in creating a new "deep learning" method that enables computers to learn about the visual world largely on their own, much as human babies do. [24]
Category: Artificial Intelligence

[245] viXra:1612.0242 [pdf] submitted on 2016-12-14 09:45:02

Doctor of Philosophy Thesis in Military Informatics (Openphd) :Lethal Autonomy of Weapons is Designed And/or Recessive

Authors: Nyagudi Musandu Nyagudi
Comments: 1 Page. By way of Prior Publications, Practice and Contribution

My original contribution to knowledge is : Any weapon that exhibits intended and/or untended lethal autonomy in targeting and interdiction – does so by way of design and/or recessive flaw(s) in its systems of control – any such weapon is capable of war-fighting and other battle-space interaction in a manner that its Human Commander does not anticipate. A lethal autonomous weapons is therefore independently capable of exhibiting positive or negative recessive norms of targeting in its perceptions of Discrimination between Civilian and Military Objects, Proportionality of Methods and Outcomes, Feasible Precaution before interdiction and its underlying Concepts of Humanity. This marks the completion of an Open PhD ( #openphd ) project done in sui generis form.
Category: Artificial Intelligence

[244] viXra:1612.0214 [pdf] submitted on 2016-12-12 10:53:08

Memory Architecture for AI

Authors: George Rajna
Comments: 44 Pages.

The neural structure we use to store and process information in verbal working memory is more complex than previously understood, finds a new study by researchers at New York University. [26] Surviving breast cancer changed the course of Regina Barzilay's research. The experience showed her, in stark relief, that oncologists and their patients lack tools for data-driven decision making. [25] New research, led by the University of Southampton, has demonstrated that a nanoscale device, called a memristor, could be used to power artificial systems that can mimic the human brain. [24] Scientists at Helmholtz-Zentrum Dresden-Rossendorf conducted electricity through DNA-based nanowires by placing gold-plated nanoparticles on them. In this way it could become possible to develop circuits based on genetic material. [23] Researchers at the Nanoscale Transport Physics Laboratory from the School of Physics at the University of the Witwatersrand have found a technique to improve carbon superlattices for quantum electronic device applications. [22] The researchers have found that these previously underestimated interactions can play a significant role in preventing heat dissipation in microelectronic devices. [21] LCLS works like an extraordinary strobe light: Its ultrabright X-rays take snapshots of materials with atomic resolution and capture motions as fast as a few femtoseconds, or millionths of a billionth of a second. For comparison, one femtosecond is to a second what seven minutes is to the age of the universe. [20] A 'nonlinear' effect that seemingly turns materials transparent is seen for the first time in X-rays at SLAC's LCLS. [19] Leiden physicists have manipulated light with large artificial atoms, so-called quantum dots. Before, this has only been accomplished with actual atoms. It is an important step toward light-based quantum technology. [18] In a tiny quantum prison, electrons behave quite differently as compared to their counterparts in free space. They can only occupy discrete energy levels, much like the electrons in an atom-for this reason, such electron prisons are often called "artificial atoms". [17]
Category: Artificial Intelligence

[243] viXra:1612.0130 [pdf] submitted on 2016-12-08 05:48:31

Machine Learning of 2-D Materials

Authors: George Rajna
Comments: 22 Pages.

Machine learning, a field focused on training computers to recognize patterns in data and make new predictions, is helping doctors more accurately diagnose diseases and stock analysts forecast the rise and fall of financial markets. And now materials scientists have pioneered another important application for machine learning—helping to accelerate the discovery and development of new materials. [14] Machine learning algorithms are designed to improve as they encounter more data, making them a versatile technology for understanding large sets of photos such as those accessible from Google Images. Elizabeth Holm, professor of materials science and engineering at Carnegie Mellon University, is leveraging this technology to better understand the enormous number of research images accumulated in the field of materials science. [13] With the help of artificial intelligence, chemists from the University of Basel in Switzerland have computed the characteristics of about two million crystals made up of four chemical elements. The researchers were able to identify 90 previously unknown thermodynamically stable crystals that can be regarded as new materials. [12] The artificial intelligence system's ability to set itself up quickly every morning and compensate for any overnight fluctuations would make this fragile technology much more useful for field measurements, said co-lead researcher Dr Michael Hush from UNSW ADFA. [11] Quantum physicist Mario Krenn and his colleagues in the group of Anton Zeilinger from the Faculty of Physics at the University of Vienna and the Austrian Academy of Sciences have developed an algorithm which designs new useful quantum experiments. As the computer does not rely on human intuition, it finds novel unfamiliar solutions. [10] Researchers at the University of Chicago's Institute for Molecular Engineering and the University of Konstanz have demonstrated the ability to generate a quantum logic operation, or rotation of the qubit, that-surprisingly—is intrinsically resilient to noise as well as to variations in the strength or duration of the control. Their achievement is based on a geometric concept known as the Berry phase and is implemented through entirely optical means within a single electronic spin in diamond.
Category: Artificial Intelligence

[242] viXra:1612.0030 [pdf] submitted on 2016-12-02 12:28:36

Machine Learning Breakthroughs

Authors: George Rajna
Comments: 47 Pages.

Machine Learning Breakthroughs As machine learning breakthroughs abound, researchers look to democratize benefits. [27] Machine-learning system spontaneously reproduces aspects of human neurology. [26] Surviving breast cancer changed the course of Regina Barzilay's research. The experience showed her, in stark relief, that oncologists and their patients lack tools for data-driven decision making. [25] New research, led by the University of Southampton, has demonstrated that a nanoscale device, called a memristor, could be used to power artificial systems that can mimic the human brain. [24] Scientists at Helmholtz-Zentrum Dresden-Rossendorf conducted electricity through DNA-based nanowires by placing gold-plated nanoparticles on them. In this way it could become possible to develop circuits based on genetic material. [23] Researchers at the Nanoscale Transport Physics Laboratory from the School of Physics at the University of the Witwatersrand have found a technique to improve carbon superlattices for quantum electronic device applications. [22] The researchers have found that these previously underestimated interactions can play a significant role in preventing heat dissipation in microelectronic devices. [21] LCLS works like an extraordinary strobe light: Its ultrabright X-rays take snapshots of materials with atomic resolution and capture motions as fast as a few femtoseconds, or millionths of a billionth of a second. For comparison, one femtosecond is to a second what seven minutes is to the age of the universe. [20] A 'nonlinear' effect that seemingly turns materials transparent is seen for the first time in X-rays at SLAC's LCLS. [19] Leiden physicists have manipulated light with large artificial atoms, so-called quantum dots. Before, this has only been accomplished with actual atoms. It is an important step toward light-based quantum technology. [18] In a tiny quantum prison, electrons behave quite differently as compared to their counterparts in free space. They can only occupy discrete energy levels, much like the electrons in an atom-for this reason, such electron prisons are often called "artificial atoms". [17]
Category: Artificial Intelligence

[241] viXra:1612.0022 [pdf] submitted on 2016-12-02 07:17:06

Machine-Learning and Human Neurology

Authors: George Rajna
Comments: 44 Pages.

Machine-learning system spontaneously reproduces aspects of human neurology. [26] Surviving breast cancer changed the course of Regina Barzilay's research. The experience showed her, in stark relief, that oncologists and their patients lack tools for data-driven decision making. [25] New research, led by the University of Southampton, has demonstrated that a nanoscale device, called a memristor, could be used to power artificial systems that can mimic the human brain. [24] Scientists at Helmholtz-Zentrum Dresden-Rossendorf conducted electricity through DNA-based nanowires by placing gold-plated nanoparticles on them. In this way it could become possible to develop circuits based on genetic material. [23] Researchers at the Nanoscale Transport Physics Laboratory from the School of Physics at the University of the Witwatersrand have found a technique to improve carbon superlattices for quantum electronic device applications. [22] The researchers have found that these previously underestimated interactions can play a significant role in preventing heat dissipation in microelectronic devices. [21] LCLS works like an extraordinary strobe light: Its ultrabright X-rays take snapshots of materials with atomic resolution and capture motions as fast as a few femtoseconds, or millionths of a billionth of a second. For comparison, one femtosecond is to a second what seven minutes is to the age of the universe. [20] A 'nonlinear' effect that seemingly turns materials transparent is seen for the first time in X-rays at SLAC's LCLS. [19] Leiden physicists have manipulated light with large artificial atoms, so-called quantum dots. Before, this has only been accomplished with actual atoms. It is an important step toward light-based quantum technology. [18] In a tiny quantum prison, electrons behave quite differently as compared to their counterparts in free space. They can only occupy discrete energy levels, much like the electrons in an atom-for this reason, such electron prisons are often called "artificial atoms". [17]
Category: Artificial Intelligence

[240] viXra:1612.0009 [pdf] submitted on 2016-12-01 12:44:05

Computer Learns by Watching Video

Authors: George Rajna
Comments: 28 Pages.

In recent years, computers have gotten remarkably good at recognizing speech and images: Think of the dictation software on most cellphones, or the algorithms that automatically identify people in photos posted to Facebook. [15] Physicists have shown that quantum effects have the potential to significantly improve a variety of interactive learning tasks in machine learning. [14] A Chinese team of physicists have trained a quantum computer to recognise handwritten characters, the first demonstration of " quantum artificial intelligence ". Physicists have long claimed that quantum computers have the potential to dramatically outperform the most powerful conventional processors. The secret sauce at work here is the strange quantum phenomenon of superposition, where a quantum object can exist in two states at the same time. [13] One of biology's biggest mysteries-how a sliced up flatworm can regenerate into new organisms-has been solved independently by a computer. The discovery marks the first time that a computer has come up with a new scientific theory without direct human help. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[239] viXra:1611.0335 [pdf] submitted on 2016-11-24 10:44:26

Kannada Spell Checker with Sandhi Splitter

Authors: Akshatha A N, Chandana G Upadhyaya, Rajashekara Murthy S
Comments: Number of pages is 7

Spelling errors are introduced in text either during typing, or when the user does not know the correct phoneme or grapheme. If a language contains complex words like sandhi where two or more morphemes join based on some rules, spell checking becomes very tedious. In such situations, having a spell checker with sandhi splitter which alerts the user by flagging the errors and providing suggestions is very useful. A novel algorithm of sandhi splitting is proposed in this paper. The sandhi splitter can split about 7000 most common sandhi words in Kannada language used as test samples. The sandhi splitter was integrated with a Kannada spell checker and a mechanism for generating suggestions was added. A comprehensive, platform independent, standalone spell checker with sandhi splitter application software was thus developed and tested extensively for its efficiency and correctness. A comparative analysis of this spell checker with sandhi splitter was made and results concluded that the Kannada spell checker with sandhi splitter has an improved performance. It is twice as fast, 200 times more space efficient, and it is 90% accurate in case of complex nouns and 50% accurate for complex verbs. Such a spell checker with sandhi splitter will be of foremost significance in machine translation systems, voice processing, etc. This is the first sandhi splitter in Kannada and the advantage of the novel algorithm is that, it can be extended to all Indian languages.
Category: Artificial Intelligence

[238] viXra:1611.0316 [pdf] submitted on 2016-11-23 08:10:08

Minds for Machine Intelligence

Authors: George Rajna
Comments: 42 Pages.

Surviving breast cancer changed the course of Regina Barzilay's research. The experience showed her, in stark relief, that oncologists and their patients lack tools for data-driven decision making. [25] New research, led by the University of Southampton, has demonstrated that a nanoscale device, called a memristor, could be used to power artificial systems that can mimic the human brain. [24] Scientists at Helmholtz-Zentrum Dresden-Rossendorf conducted electricity through DNA-based nanowires by placing gold-plated nanoparticles on them. In this way it could become possible to develop circuits based on genetic material. [23] Researchers at the Nanoscale Transport Physics Laboratory from the School of Physics at the University of the Witwatersrand have found a technique to improve carbon superlattices for quantum electronic device applications. [22] The researchers have found that these previously underestimated interactions can play a significant role in preventing heat dissipation in microelectronic devices. [21] LCLS works like an extraordinary strobe light: Its ultrabright X-rays take snapshots of materials with atomic resolution and capture motions as fast as a few femtoseconds, or millionths of a billionth of a second. For comparison, one femtosecond is to a second what seven minutes is to the age of the universe. [20] A 'nonlinear' effect that seemingly turns materials transparent is seen for the first time in X-rays at SLAC's LCLS. [19] Leiden physicists have manipulated light with large artificial atoms, so-called quantum dots. Before, this has only been accomplished with actual atoms. It is an important step toward light-based quantum technology. [18] In a tiny quantum prison, electrons behave quite differently as compared to their counterparts in free space. They can only occupy discrete energy levels, much like the electrons in an atom-for this reason, such electron prisons are often called "artificial atoms". [17] When two atoms are placed in a small chamber enclosed by mirrors, they can simultaneously absorb a single photon. [16] Optical quantum technologies are based on the interactions of atoms and photons at the single-particle level, and so require sources of single photons.
Category: Artificial Intelligence

[237] viXra:1611.0314 [pdf] submitted on 2016-11-23 08:47:27

New AI Algorithm Learns Beyond its Training

Authors: George Rajna
Comments: 27 Pages.

Researchers at Lancaster University's Data Science Institute have developed a software system that can for the first time rapidly self-assemble into the most efficient form without needing humans to tell it what to do. [15] Physicists have shown that quantum effects have the potential to significantly improve a variety of interactive learning tasks in machine learning. [14] A Chinese team of physicists have trained a quantum computer to recognise handwritten characters, the first demonstration of " quantum artificial intelligence ". Physicists have long claimed that quantum computers have the potential to dramatically outperform the most powerful conventional processors. The secret sauce at work here is the strange quantum phenomenon of superposition, where a quantum object can exist in two states at the same time. [13] One of biology's biggest mysteries-how a sliced up flatworm can regenerate into new organisms-has been solved independently by a computer. The discovery marks the first time that a computer has come up with a new scientific theory without direct human help. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[236] viXra:1611.0260 [pdf] submitted on 2016-11-17 11:18:04

Deng Entropy in Hyper Power Set and Super Power Set

Authors: Bingyi Kang, Yong Deng
Comments: 18 Pages.

Deng entropy has been proposed to handle the uncertainty degree of belief function in Dempster-Shafer framework very recently. In this paper, two new belief entropies based on the frame of Deng entropy for hyper-power sets and super-power sets are respectively proposed to measure the uncertainty degree of more uncertain and more flexible information. Directly, the new entropies based on the frame of Deng entropy in hyper-power sets and super-power sets can be used in the application of DSmT.
Category: Artificial Intelligence

[235] viXra:1611.0211 [pdf] submitted on 2016-11-14 04:10:17

A Variable Order Hidden Markov Model with Dependence Jumps

Authors: Anastasios Petropoulos, Stelios Xanthopoulos, Sotirios P. Chatzis
Comments: 14 Pages.

Hidden Markov models (HMMs) are a popular approach for modeling sequential data, typically based on the assumption of a first- or moderate-order Markov chain. However, in many real-world scenarios the modeled data entail temporal dynamics the patterns of which change over time. In this paper, we address this problem by proposing a novel HMM formulation, treating temporal dependencies as latent variables over which inference is performed. Specifically, we introduce a hierarchical graphical model comprising two hidden layers: on the first layer, we postulate a chain of latent observation-emitting states, the temporal dependencies between which may change over time; on the second layer, we postulate a latent first-order Markov chain modeling the evolution of temporal dynamics (dependence jumps) pertaining to the first-layer latent process. As a result of this construction, our method allows for effectively modeling non-homogeneous observed data, where the patterns of the entailed temporal dynamics may change over time. We devise efficient training and inference algorithms for our model, following the expectation-maximization paradigm. We demonstrate the efficacy and usefulness of our approach considering several real-world datasets. As we show, our model allows for increased modeling and predictive performance compared to the alternative methods, while offering a good trade-off between the resulting increases in predictive performance and computational complexity.
Category: Artificial Intelligence

[234] viXra:1611.0181 [pdf] submitted on 2016-11-12 07:13:04

Finding Patterns in Corrupted Data

Authors: George Rajna
Comments: 29 Pages.

A team, including researchers from MIT's Computer Science and Artificial Intelligence Laboratory, has created a new set of algorithms that can efficiently fit probability distributions to high-dimensional data. [16] Researchers at Lancaster University's Data Science Institute have developed a software system that can for the first time rapidly self-assemble into the most efficient form without needing humans to tell it what to do. [15] Physicists have shown that quantum effects have the potential to significantly improve a variety of interactive learning tasks in machine learning. [14] A Chinese team of physicists have trained a quantum computer to recognise handwritten characters, the first demonstration of " quantum artificial intelligence ". Physicists have long claimed that quantum computers have the potential to dramatically outperform the most powerful conventional processors. The secret sauce at work here is the strange quantum phenomenon of superposition, where a quantum object can exist in two states at the same time. [13] One of biology's biggest mysteries-how a sliced up flatworm can regenerate into new organisms-has been solved independently by a computer. The discovery marks the first time that a computer has come up with a new scientific theory without direct human help. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[233] viXra:1611.0177 [pdf] submitted on 2016-11-12 04:50:24

Machines Learn by Simply Observing

Authors: George Rajna
Comments: 28 Pages.

It is now possible for machines to learn how natural or artificial systems work by simply observing them, without being told what to look for, according to researchers at the University of Sheffield. [16] Researchers at Lancaster University's Data Science Institute have developed a software system that can for the first time rapidly self-assemble into the most efficient form without needing humans to tell it what to do. [15] Physicists have shown that quantum effects have the potential to significantly improve a variety of interactive learning tasks in machine learning. [14] A Chinese team of physicists have trained a quantum computer to recognise handwritten characters, the first demonstration of " quantum artificial intelligence ". Physicists have long claimed that quantum computers have the potential to dramatically outperform the most powerful conventional processors. The secret sauce at work here is the strange quantum phenomenon of superposition, where a quantum object can exist in two states at the same time. [13] One of biology's biggest mysteries-how a sliced up flatworm can regenerate into new organisms-has been solved independently by a computer. The discovery marks the first time that a computer has come up with a new scientific theory without direct human help. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[232] viXra:1611.0174 [pdf] submitted on 2016-11-12 05:46:51

Social Emotions Test for Artificial Intelligence

Authors: George Rajna
Comments: 29 Pages.

New evidence from brain studies, including cognitive psychology and neurophysiology research, shows that the emotional assessment of every object, subject, action or event plays an important role in human mental processes. And that means that if we want to create human-like artificial intelligence, we must make it emotionally responsive. But how do we know that such intelligence actually experiences real, human-like emotions? [17] It is now possible for machines to learn how natural or artificial systems work by simply observing them, without being told what to look for, according to researchers at the University of Sheffield. [16] Researchers at Lancaster University's Data Science Institute have developed a software system that can for the first time rapidly self-assemble into the most efficient form without needing humans to tell it what to do. [15] Physicists have shown that quantum effects have the potential to significantly improve a variety of interactive learning tasks in machine learning. [14] A Chinese team of physicists have trained a quantum computer to recognise handwritten characters, the first demonstration of “quantum artificial intelligence”. Physicists have long claimed that quantum computers have the potential to dramatically outperform the most powerful conventional processors. The secret sauce at work here is the strange quantum phenomenon of superposition, where a quantum object can exist in two states at the same time. [13] One of biology's biggest mysteries - how a sliced up flatworm can regenerate into new organisms - has been solved independently by a computer. The discovery marks the first time that a computer has come up with a new scientific theory without direct human help. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron’s spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[231] viXra:1611.0173 [pdf] submitted on 2016-11-12 06:46:28

AI System Surfs Web to Improve its Performance

Authors: George Rajna
Comments: 31 Pages.

Of the vast wealth of information unlocked by the Internet, most is plain text. The data necessary to answer myriad questions—about, say, the correlations between the industrial use of certain chemicals and incidents of disease, or between patterns of news coverage and voter-poll results—may all be online. But extracting it from plain text and organizing it for quantitative analysis may be prohibitively time consuming. [18] New evidence from brain studies, including cognitive psychology and neurophysiology research, shows that the emotional assessment of every object, subject, action or event plays an important role in human mental processes. And that means that if we want to create human-like artificial intelligence, we must make it emotionally responsive. But how do we know that such intelligence actually experiences real, human-like emotions? [17] It is now possible for machines to learn how natural or artificial systems work by simply observing them, without being told what to look for, according to researchers at the University of Sheffield. [16] Researchers at Lancaster University's Data Science Institute have developed a software system that can for the first time rapidly self-assemble into the most efficient form without needing humans to tell it what to do. [15] Physicists have shown that quantum effects have the potential to significantly improve a variety of interactive learning tasks in machine learning. [14] A Chinese team of physicists have trained a quantum computer to recognise handwritten characters, the first demonstration of “quantum artificial intelligence”. Physicists have long claimed that quantum computers have the potential to dramatically outperform the most powerful conventional processors. The secret sauce at work here is the strange quantum phenomenon of superposition, where a quantum object can exist in two states at the same time. [13] One of biology's biggest mysteries - how a sliced up flatworm can regenerate into new organisms - has been solved independently by a computer. The discovery marks the first time that a computer has come up with a new scientific theory without direct human help. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron’s spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[230] viXra:1611.0169 [pdf] submitted on 2016-11-12 04:07:06

Brain-Inspired Device

Authors: George Rajna
Comments: 39 Pages.

New research, led by the University of Southampton, has demonstrated that a nanoscale device, called a memristor, could be used to power artificial systems that can mimic the human brain. [24] Scientists at Helmholtz-Zentrum Dresden-Rossendorf conducted electricity through DNA-based nanowires by placing gold-plated nanoparticles on them. In this way it could become possible to develop circuits based on genetic material. [23] Researchers at the Nanoscale Transport Physics Laboratory from the School of Physics at the University of the Witwatersrand have found a technique to improve carbon superlattices for quantum electronic device applications. [22] The researchers have found that these previously underestimated interactions can play a significant role in preventing heat dissipation in microelectronic devices. [21] LCLS works like an extraordinary strobe light: Its ultrabright X-rays take snapshots of materials with atomic resolution and capture motions as fast as a few femtoseconds, or millionths of a billionth of a second. For comparison, one femtosecond is to a second what seven minutes is to the age of the universe. [20] A ‘nonlinear’ effect that seemingly turns materials transparent is seen for the first time in X-rays at SLAC’s LCLS. [19] Leiden physicists have manipulated light with large artificial atoms, so-called quantum dots. Before, this has only been accomplished with actual atoms. It is an important step toward light-based quantum technology. [18] In a tiny quantum prison, electrons behave quite differently as compared to their counterparts in free space. They can only occupy discrete energy levels, much like the electrons in an atom - for this reason, such electron prisons are often called "artificial atoms". [17] When two atoms are placed in a small chamber enclosed by mirrors, they can simultaneously absorb a single photon. [16] Optical quantum technologies are based on the interactions of atoms and photons at the single-particle level, and so require sources of single photons that are highly indistinguishable – that is, as identical as possible. Current single-photon sources using semiconductor quantum dots inserted into photonic structures produce photons that are ultrabright but have limited indistinguishability due to charge noise, which results in a fluctuating electric field. [14] A method to produce significant amounts of semiconducting nanoparticles for light-emitting displays, sensors, solar panels and biomedical applications has gained momentum with a demonstration by researchers at the Department of Energy's Oak Ridge National Laboratory. [13] A source of single photons that meets three important criteria for use in quantum-information systems has been unveiled in China by an international team of physicists. Based on a quantum dot, the device is an efficient source of photons that emerge as solo particles that are indistinguishable from each other. The researchers are now trying to use the source to create a quantum computer based on "boson sampling". [11] With the help of a semiconductor quantum dot, physicists at the University of Basel have developed a new type of light source that emits single photons. For the first time, the researchers have managed to create a stream of identical photons. [10] Optical photons would be ideal carriers to transfer quantum information over large distances. Researchers envisage a network where information is processed in certain nodes and transferred between them via photons. [9] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer using Quantum Information. In August 2013, the achievement of "fully deterministic" quantum teleportation, using a hybrid technique, was reported. On 29 May 2014, scientists announced a reliable way of transferring data by quantum teleportation. Quantum teleportation of data had been done before but with highly unreliable methods. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron’s spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer with the help of Quantum Information.
Category: Artificial Intelligence

[229] viXra:1611.0095 [pdf] submitted on 2016-11-08 03:33:30

Quantitative Prediction of Electoral Vote for United States Presidential Election in 2016

Authors: Gang Xu
Comments: 8 Pages. This work was originally completed by October 22, 2016. The manuscript draft was prepared on November 7, 2016.

In this paper I am reporting the quantitative prediction of the electoral vote for United States presidential election in 2016. This quantitative prediction was based on the Google Trends (GT) data that is publicly available on the internet. A simple heuristic statistical model is applied to analyzing the GT data. This is intended to be an experiment for exploring the plausible dependency between the GT data and the electoral vote result of US presidential elections. The model's performance has also been tested by comparing the predicted results and the actual electoral votes in 2004, 2008 and 2012. For the year 2016, the Google Trends data projects that Mr. Trump will win the white house in landslide. This paper serves as a document to put this exploratory experiment in real test, since the actual election result can be compared to the prediction after tomorrow (November 8, 2016).
Category: Artificial Intelligence

[228] viXra:1611.0086 [pdf] submitted on 2016-11-07 06:27:49

Neuromorphic Processor

Authors: George Rajna
Comments: 22 Pages.

Toshiba advances deep learning with extremely low power neuromorphic processor. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[227] viXra:1611.0025 [pdf] submitted on 2016-11-02 08:20:12

Machine Learning for Cancer Treatment

Authors: George Rajna
Comments: 28 Pages.

Physicians have long used visual judgment of medical images to determine the course of cancer treatment. A new program package from Fraunhofer researchers reveals changes in images and facilitates this task using deep learning. The experts will demonstrate this software in Chicago from November 27 to December 2 at RSNA, the world's largest radiology meeting. [16] Researchers at Lancaster University's Data Science Institute have developed a software system that can for the first time rapidly self-assemble into the most efficient form without needing humans to tell it what to do. [15] Physicists have shown that quantum effects have the potential to significantly improve a variety of interactive learning tasks in machine learning. [14] A Chinese team of physicists have trained a quantum computer to recognise handwritten characters, the first demonstration of “quantum artificial intelligence”. Physicists have long claimed that quantum computers have the potential to dramatically outperform the most powerful conventional processors. The secret sauce at work here is the strange quantum phenomenon of superposition, where a quantum object can exist in two states at the same time. [13] One of biology's biggest mysteries - how a sliced up flatworm can regenerate into new organisms - has been solved independently by a computer. The discovery marks the first time that a computer has come up with a new scientific theory without direct human help. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron’s spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[226] viXra:1611.0022 [pdf] submitted on 2016-11-02 06:49:11

Transforming, Self-Learning Software

Authors: George Rajna
Comments: 27 Pages.

Researchers at Lancaster University's Data Science Institute have developed a software system that can for the first time rapidly self-assemble into the most efficient form without needing humans to tell it what to do. [15] Physicists have shown that quantum effects have the potential to significantly improve a variety of interactive learning tasks in machine learning. [14] A Chinese team of physicists have trained a quantum computer to recognise handwritten characters, the first demonstration of " quantum artificial intelligence ". Physicists have long claimed that quantum computers have the potential to dramatically outperform the most powerful conventional processors. The secret sauce at work here is the strange quantum phenomenon of superposition, where a quantum object can exist in two states at the same time. [13] One of biology's biggest mysteries-how a sliced up flatworm can regenerate into new organisms-has been solved independently by a computer. The discovery marks the first time that a computer has come up with a new scientific theory without direct human help. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[225] viXra:1610.0360 [pdf] submitted on 2016-10-30 02:33:09

Machine-Learning Decision Rationales

Authors: George Rajna
Comments: 28 Pages.

Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have devised a way to train neural networks so that they provide not only predictions and classifications but rationales for their decisions. [15] Physicists have shown that quantum effects have the potential to significantly improve a variety of interactive learning tasks in machine learning. [14] A Chinese team of physicists have trained a quantum computer to recognise handwritten characters, the first demonstration of " quantum artificial intelligence ". Physicists have long claimed that quantum computers have the potential to dramatically outperform the most powerful conventional processors. The secret sauce at work here is the strange quantum phenomenon of superposition, where a quantum object can exist in two states at the same time. [13] One of biology's biggest mysteries-how a sliced up flatworm can regenerate into new organisms-has been solved independently by a computer. The discovery marks the first time that a computer has come up with a new scientific theory without direct human help. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[224] viXra:1610.0359 [pdf] submitted on 2016-10-30 04:02:16

Machine Learning Understand Materials

Authors: George Rajna
Comments: 21 Pages.

Machine learning algorithms are designed to improve as they encounter more data, making them a versatile technology for understanding large sets of photos such as those accessible from Google Images. Elizabeth Holm, professor of materials science and engineering at Carnegie Mellon University, is leveraging this technology to better understand the enormous number of research images accumulated in the field of materials science. [13] With the help of artificial intelligence, chemists from the University of Basel in Switzerland have computed the characteristics of about two million crystals made up of four chemical elements. The researchers were able to identify 90 previously unknown thermodynamically stable crystals that can be regarded as new materials. [12] The artificial intelligence system's ability to set itself up quickly every morning and compensate for any overnight fluctuations would make this fragile technology much more useful for field measurements, said co-lead researcher Dr Michael Hush from UNSW ADFA. [11] Quantum physicist Mario Krenn and his colleagues in the group of Anton Zeilinger from the Faculty of Physics at the University of Vienna and the Austrian Academy of Sciences have developed an algorithm which designs new useful quantum experiments. As the computer does not rely on human intuition, it finds novel unfamiliar solutions. [10] Researchers at the University of Chicago's Institute for Molecular Engineering and the University of Konstanz have demonstrated the ability to generate a quantum logic operation, or rotation of the qubit, that-surprisingly—is intrinsically resilient to noise as well as to variations in the strength or duration of the control. Their achievement is based on a geometric concept known as the Berry phase and is implemented through entirely optical means within a single electronic spin in diamond. [9] New research demonstrates that particles at the quantum level can in fact be seen as behaving something like billiard balls rolling along a table, and not merely as the probabilistic smears that the standard interpretation of quantum mechanics suggests. But there's a catch-the tracks the particles follow do not always behave as one would expect from "realistic" trajectories, but often in a fashion that has been termed "surrealistic." [8] Quantum entanglement—which occurs when two or more particles are correlated in such a way that they can influence each other even across large distances—is not an all-or-nothing phenomenon, but occurs in various degrees. The more a quantum state is entangled with its partner, the better the states will perform in quantum information applications. Unfortunately, quantifying entanglement is a difficult process involving complex optimization problems that give even physicists headaches. [7] A trio of physicists in Europe has come up with an idea that they believe would allow a person to actually witness entanglement. Valentina Caprara Vivoli, with the University of Geneva, Pavel Sekatski, with the University of Innsbruck and Nicolas Sangouard, with the University of Basel, have together written a paper describing a scenario where a human subject would be able to witness an instance of entanglement—they have uploaded it to the arXiv server for review by others. [6] The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the relativistic quantum theory.
Category: Artificial Intelligence

[223] viXra:1610.0336 [pdf] submitted on 2016-10-27 21:31:21

Fuzzy Evidential Influence Diagram Evaluation Algorithm

Authors: Haoyang Zheng, Yong Deng
Comments: 38 Pages.

Fuzzy influence diagrams (FIDs) are one of the graphical models that combines the qualitative and quantitative analysis to solve decision-making problems. However, FIDs use an incomprehensive evaluation criteria to score nodes in complex systems, so that many different nodes got the same score, which can not reflect their differences. Based on fuzzy set and Dempster-Shafer (D-S) evidence theory, this paper changes the traditional evaluation system and modifies corresponding algorithm, in order that the influence diagram can more effectively reflect the true situation of the system, and get more practical results. Numerical examples and the real application in supply chain financial system are used to show the efficiency of the proposed influence diagram model.
Category: Artificial Intelligence

[222] viXra:1610.0314 [pdf] submitted on 2016-10-26 05:16:26

Artificial Intelligence Replaces Judges and Lawyers

Authors: George Rajna
Comments: 20 Pages.

An artificial intelligence method developed by University College London computer scientists and associates has predicted the judicial decisions of the European Court of Human Rights (ECtHR) with 79% accuracy, according to a paper published Monday, Oct. 24 in PeerJ Computer Science. [12] The artificial intelligence system's ability to set itself up quickly every morning and compensate for any overnight fluctuations would make this fragile technology much more useful for field measurements, said co-lead researcher Dr Michael Hush from UNSW ADFA. [11] Quantum physicist Mario Krenn and his colleagues in the group of Anton Zeilinger from the Faculty of Physics at the University of Vienna and the Austrian Academy of Sciences have developed an algorithm which designs new useful quantum experiments. As the computer does not rely on human intuition, it finds novel unfamiliar solutions. [10] Researchers at the University of Chicago's Institute for Molecular Engineering and the University of Konstanz have demonstrated the ability to generate a quantum logic operation, or rotation of the qubit, that-surprisingly—is intrinsically resilient to noise as well as to variations in the strength or duration of the control. Their achievement is based on a geometric concept known as the Berry phase and is implemented through entirely optical means within a single electronic spin in diamond. [9] New research demonstrates that particles at the quantum level can in fact be seen as behaving something like billiard balls rolling along a table, and not merely as the probabilistic smears that the standard interpretation of quantum mechanics suggests. But there's a catch-the tracks the particles follow do not always behave as one would expect from "realistic" trajectories, but often in a fashion that has been termed "surrealistic." [8] Quantum entanglement—which occurs when two or more particles are correlated in such a way that they can influence each other even across large distances—is not an all-or-nothing phenomenon, but occurs in various degrees. The more a quantum state is entangled with its partner, the better the states will perform in quantum information applications. Unfortunately, quantifying entanglement is a difficult process involving complex optimization problems that give even physicists headaches. [7] A trio of physicists in Europe has come up with an idea that they believe would allow a person to actually witness entanglement. Valentina Caprara Vivoli, with the University of Geneva, Pavel Sekatski, with the University of Innsbruck and Nicolas Sangouard, with the University of Basel, have together written a paper describing a scenario where a human subject would be able to witness an instance of entanglement—they have uploaded it to the arXiv server for review by others. [6] The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the relativistic quantum theory.
Category: Artificial Intelligence

[221] viXra:1610.0281 [pdf] submitted on 2016-10-24 04:05:52

An Information Volume Measure

Authors: Yong Deng
Comments: 8 Pages.

How to measure the volume of uncertainty information is an open issue. Shannon entropy is used to represent the uncertainty degree of a probability distribution. Given a generalized probability distribution which means that the probability is not only assigned to the basis event space but also the power set of event space. At this time, a so called meta probability space is constructed. A new measure, named as Deng entropy, is presented. The results show that, compared with existing method, Deng entropy is not only better from the aspect of mathematic form, but also has the significant physical meaning.
Category: Artificial Intelligence

[220] viXra:1610.0249 [pdf] submitted on 2016-10-21 11:35:59

New Data Algorithms

Authors: George Rajna
Comments: 28 Pages.

Last year, MIT researchers presented a system that automated a crucial step in big-data analysis: the selection of a "feature set," or aspects of the data that are useful for making predictions. The researchers entered the system in several data science contests, where it outperformed most of the human competitors and took only hours instead of months to perform its analyses. [15] Physicists have shown that quantum effects have the potential to significantly improve a variety of interactive learning tasks in machine learning. [14] A Chinese team of physicists have trained a quantum computer to recognise handwritten characters, the first demonstration of " quantum artificial intelligence ". Physicists have long claimed that quantum computers have the potential to dramatically outperform the most powerful conventional processors. The secret sauce at work here is the strange quantum phenomenon of superposition, where a quantum object can exist in two states at the same time. [13] One of biology's biggest mysteries-how a sliced up flatworm can regenerate into new organisms-has been solved independently by a computer. The discovery marks the first time that a computer has come up with a new scientific theory without direct human help. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[219] viXra:1610.0169 [pdf] submitted on 2016-10-15 16:49:11

Band Gap Estimation Using Machine Learning Techniques

Authors: Anantha Natarajan S, R Varadhan, Ezhilvel ME
Comments: 3 Pages.

The purpose of this study is to build machine learning models to predict the band gap of binary compounds, using its known properties like molecular weight, electronegativity, atomic fraction and the group of the constituent elements in the periodic table. Regression techniques like Linear, Ridge regression and Random Forest were used to build the model. This model can be used by students and researchers in experiments involving unknown band gaps or new compounds.
Category: Artificial Intelligence

[218] viXra:1610.0142 [pdf] submitted on 2016-10-13 14:04:33

Google DeepMind Neural Networks

Authors: George Rajna
Comments: 24 Pages.

A team of researchers at Google's DeepMind Technologies has been working on a means to increase the capabilities of computers by combining aspects of data processing and artificial intelligence and have come up with what they are calling a differentiable neural computer (DNC.) In their paper published in the journal Nature, they describe the work they are doing and where they believe it is headed. To make the work more accessible to the public team members, Alexander Graves and Greg Wayne have posted an explanatory page on the DeepMind website. [13] Nobody understands why deep neural networks are so good at solving complex problems. Now physicists say the secret is buried in the laws of physics. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[217] viXra:1610.0110 [pdf] submitted on 2016-10-10 12:21:47

Neuro-Inspired Analog Computer

Authors: George Rajna
Comments: 23 Pages.

Researchers have developed a neuro-inspired analog computer that has the ability to train itself to become better at whatever tasks it performs. [13] A small, Santa Fe, New Mexico-based company called Knowm claims it will soon begin commercializing a state-of-the-art technique for building computing chips that learn. Other companies, including HP HPQ-3.45% and IBM IBM-2.10% , have already invested in developing these so-called brain-based chips, but Knowm says it has just achieved a major technological breakthrough that it should be able to push into production hopefully within a few years. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[216] viXra:1610.0074 [pdf] submitted on 2016-10-07 00:22:44

Belief Reliability Analysis and Its Application

Authors: Haoyang Zheng, Likang Yin, Tian Bian, Yong Deng
Comments: 24 Pages.

In reliability analysis, Fault Tree Analysis based on evidential networks is an important research topic. However, the existing EN approaches still remain two issues: one is the final results are expressed with interval numbers, which has a relatively high uncertainty to make a final decision. The other is the combination rule is not used to fuse uncertain information. These issues will greatly decrease the efficiency of EN to handle uncertain information. To address these open issues, a new methodology, called Belief Reliability Analysis, is presented in this paper. The combination methods to deal with series system, parallel system, series-parallel system as well as parallel-series system are proposed for reliability evaluation. Numerical examples and the real application in servo-actuation system are used to show the efficiency of the proposed Belief Reliability Analysis methodology.
Category: Artificial Intelligence

[215] viXra:1610.0029 [pdf] submitted on 2016-10-04 04:31:32

Associative Broadcast Neural Network

Authors: Aleksei Morozov
Comments: 3 Pages.

Associative broadcast neural network (ABNN) is an artificial neural network inspired by a hypothesis of broadcasting of neuron's output pattern in a biological neural network. Neuron has wire connections and ether connections. Ether connections are electrical. Wire connections provide a recognition functionality. Ether connections provide an association functionality.
Category: Artificial Intelligence

[214] viXra:1610.0028 [pdf] submitted on 2016-10-03 13:53:10

A New Belief Entropy: Possible Generalization of Deng Entropy, Tsallis Entropy and Shannon Entropy

Authors: Bingyi Kang, Yong Deng
Comments: 15 Pages.

Shannon entropy is the mathematical foundation of information theory, Tsallis entropy is the roots of nonextensive statistical mechanics, Deng entropy was proposed to measure the uncertainty degree of belief function very recently. In this paper, A new entropy H was proposed to generalize Deng entropy, Tsallis entropy and Shannon entropy. The new entropy H can be degenerated to Deng entropy, Tsallis entropy, and Shannon entropy under different conditions, and also can maintains the mathematical properity of Deng entropy, Tsallis entropy and Shannon entropy.
Category: Artificial Intelligence

[213] viXra:1609.0311 [pdf] submitted on 2016-09-21 07:22:05

Artificial Intelligence Discover New Materials

Authors: George Rajna
Comments: 20 Pages.

With the help of artificial intelligence, chemists from the University of Basel in Switzerland have computed the characteristics of about two million crystals made up of four chemical elements. The researchers were able to identify 90 previously unknown thermodynamically stable crystals that can be regarded as new materials. [12] The artificial intelligence system's ability to set itself up quickly every morning and compensate for any overnight fluctuations would make this fragile technology much more useful for field measurements, said co-lead researcher Dr Michael Hush from UNSW ADFA. [11] Quantum physicist Mario Krenn and his colleagues in the group of Anton Zeilinger from the Faculty of Physics at the University of Vienna and the Austrian Academy of Sciences have developed an algorithm which designs new useful quantum experiments. As the computer does not rely on human intuition, it finds novel unfamiliar solutions. [10] Researchers at the University of Chicago's Institute for Molecular Engineering and the University of Konstanz have demonstrated the ability to generate a quantum logic operation, or rotation of the qubit, that-surprisingly—is intrinsically resilient to noise as well as to variations in the strength or duration of the control. Their achievement is based on a geometric concept known as the Berry phase and is implemented through entirely optical means within a single electronic spin in diamond. [9] New research demonstrates that particles at the quantum level can in fact be seen as behaving something like billiard balls rolling along a table, and not merely as the probabilistic smears that the standard interpretation of quantum mechanics suggests. But there's a catch-the tracks the particles follow do not always behave as one would expect from "realistic" trajectories, but often in a fashion that has been termed "surrealistic." [8] Quantum entanglement—which occurs when two or more particles are correlated in such a way that they can influence each other even across large distances—is not an all-or-nothing phenomenon, but occurs in various degrees. The more a quantum state is entangled with its partner, the better the states will perform in quantum information applications. Unfortunately, quantifying entanglement is a difficult process involving complex optimization problems that give even physicists headaches. [7] A trio of physicists in Europe has come up with an idea that they believe would allow a person to actually witness entanglement. Valentina Caprara Vivoli, with the University of Geneva, Pavel Sekatski, with the University of Innsbruck and Nicolas Sangouard, with the University of Basel, have together written a paper describing a scenario where a human subject would be able to witness an instance of entanglement—they have uploaded it to the arXiv server for review by others. [6] The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the relativistic quantum theory.
Category: Artificial Intelligence

[212] viXra:1609.0238 [pdf] submitted on 2016-09-15 19:25:33

Revision on Fuzzy Artificial Potential Field for Humanoid Robot Path Planning in Unknown Environment

Authors: Mahdi Fakoor, Amirreza Kosari, Mohsen Jafarzadeh
Comments: 10 Pages.

Path planning in a completely known environment has been experienced various ways. However, in real world, most humanoid robots work in unknown environments. Robots' path planning by artificial potential field and fuzzy artificial potential field methods are very popular in the field of robotics navigation. However, by default humanoid robots lack range sensors; thus, traditional artificial potential field approaches needs to adopt themselves to these limitations. This paper investigates two different approaches for path planning of a humanoid robot in an unknown environment using fuzzy artificial potential (FAP) method. In the first approach, the direction of the moving robot is derived from fuzzified artificial potential field whereas in the second one, the direction of the robot is extracted from some linguistic rules that are inspired from artificial potential field. These two introduced trajectory design approaches are validated though some software and hardware in the loop simulations and the experimental results demonstrate the superiority of the proposed approaches in humanoid robot real-time trajectory planning problems.
Category: Artificial Intelligence

[211] viXra:1609.0134 [pdf] submitted on 2016-09-10 11:19:00

War Algorithm Accountability

Authors: Dustin A. Lewis, Gabriella Blum, Naz K. Modirzadeh
Comments: 244 Pages.

Compendium on Accountability issues as pertains to Lethal Autonomous Weapons
Category: Artificial Intelligence

[210] viXra:1609.0133 [pdf] submitted on 2016-09-10 12:00:18

Five Hundred Deep Learning Papers, Graphviz and Python

Authors: Daniele Ettore Ciriello
Comments: 13 Pages.

I invested days creating a graph with PyGraphviz to repre- sent the evolutionary process of deep learning’s state of the art for the last twenty-five years. Through this paper I want to show you how and what I obtained.
Category: Artificial Intelligence

[209] viXra:1609.0126 [pdf] submitted on 2016-09-10 04:54:56

Deep Neural Networks

Authors: George Rajna
Comments: 23 Pages.

Nobody understands why deep neural networks are so good at solving complex problems. Now physicists say the secret is buried in the laws of physics. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the Relativistic Quantum Theory and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[208] viXra:1608.0291 [pdf] submitted on 2016-08-24 01:48:59

A Comparison of the Generalized minC Combination and the Hybrid DSm Combination Rules

Authors: Milan Daniel
Comments: 18 Pages.

A generalization of the minC combination to DSm hyper-power sets is presented. Both the special formulas for static fusion or dynamic fusion without non-existential constraints and the quite general formulas for dynamic fusion with non-existential constraints are included. Examples of the minC combination on several different hybrid DSm models are presented. A comparison of the generalized minC combination with the hybrid DSm rule is discussed and explained on examples.
Category: Artificial Intelligence

[207] viXra:1608.0224 [pdf] submitted on 2016-08-20 12:45:56

Search for Dynamical Origin of Social Networks

Authors: Michail Zak
Comments: 33 Pages.

The challenge of this work is to re-define the concept of intelligent agent as a building block of social networks by presenting it as a physical particle with additional non-Newtonian properties. The proposed model of an intelligent agent described by a system of ODE coupled with their Liouville equation has been introduced and discussed. Following the Madelung equation that belongs to this class, non-Newtonian properties such as superposition, entanglement, and probability interference typical for quantum systems have been described. Special attention was paid to the capability to violate the second law of thermodynamics, which makes these systems neither Newtonian, nor quantum. It has been shown that the proposed model can be linked to mathematical models of livings as well as to models of AI. The model is presented in two modifications. The first one is illustrated by the discovery of a stochastic attractor approached by the social network; as an application, it was demonstrated that any statistics can be represented by an attractor of the solution to the corresponding system of ODE coupled with its Liouville equation. It was emphasized that evolution to the attractor reveals possible micro-mechanisms driving random events to the final distribution of the corresponding statistical law. Special attention is concentrated upon the power law and its dynamical interpretation: it is demonstrated that the underlying micro- dynamics supports a “violent reputation” of the power-law statistics. The second modification of the model of social network associated with a decision-making process and applied to solution of NP-complete problems known as being unsolvable neither by classical nor by quantum algorithms. The approach is illustrated by solving a search in unsorted database in polynomial time by resonance between external force representing the address of a required item and the response representing the location of this item.
Category: Artificial Intelligence

[206] viXra:1608.0187 [pdf] submitted on 2016-08-18 14:02:36

Neuromorphic Architecture

Authors: George Rajna
Comments: 28 Pages.

In the future, level-tuned neurons may help enable neuromorphic computing systems to perform tasks that traditional computers cannot, such as learning from their environment, pattern recognition, and knowledge extraction from big data sources. [19] IBM scientists have created randomly spiking neurons using phase-change materials to store and process data. This demonstration marks a significant step forward in the development of energy-efficient, ultra-dense integrated neuromorphic technologies for applications in cognitive computing. [18] An ion trap with four segmented blade electrodes used to trap a linear chain of atomic ions for quantum information processing. Each ion is addressed optically for individual control and readout using the high optical access of the trap. [17] To date, researchers have realised qubits in the form of individual electrons (aktuell.ruhr-uni-bochum.de/pm2012/pm00090.html.en). However, this led to interferences and rendered the information carriers difficult to programme and read. The group has solved this problem by utilising electron holes as qubits, rather than electrons. [16] Physicists from MIPT and the Russian Quantum Center have developed an easier method to create a universal quantum computer using multilevel quantum systems (qudits), each one of which is able to work with multiple "conventional" quantum elements – qubits. [15] Precise atom implants in silicon provide a first step toward practical quantum computers. [14] A method to produce significant amounts of semiconducting nanoparticles for light-emitting displays, sensors, solar panels and biomedical applications has gained momentum with a demonstration by researchers at the Department of Energy's Oak Ridge National Laboratory. [13] A source of single photons that meets three important criteria for use in quantum-information systems has been unveiled in China by an international team of physicists. Based on a quantum dot, the device is an efficient source of photons that emerge as solo particles that are indistinguishable from each other. The researchers are now trying to use the source to create a quantum computer based on "boson sampling". [11] With the help of a semiconductor quantum dot, physicists at the University of Basel have developed a new type of light source that emits single photons.
Category: Artificial Intelligence

[205] viXra:1608.0093 [pdf] submitted on 2016-08-08 22:08:41

Edge Based Grid Super-Imposition for Crowd Emotion Recognition

Authors: Amol S Patwardhan
Comments: 6 Pages.

Numerous automatic continuous emotion detection system studies have examined mostly use of videos and images containing individual person expressing emotions. This study examines the detection of spontaneous emotions in a group and crowd settings. Edge detection was used with a grid of lines superimposition to extract the features. The feature movement in terms of movement from the reference point was used to track across sequences of images from the color channel. Additionally the video data capturing was done on spontaneous emotions invoked by watching sports events from group of participants. The method was view and occlusion independent and the results were not affected by presence of multiple people chaotically expressing various emotions. The edge thresholds of 0.2 and grid thresholds of 20 showed the best accuracy results. The overall accuracy of the group emotion classifier was 70.9%.
Category: Artificial Intelligence

[204] viXra:1608.0092 [pdf] submitted on 2016-08-08 22:15:06

Human Activity Recognition Using Temporal Frame Decision Rule Extraction

Authors: Amol Patwardhan
Comments: 4 Pages.

Activities of humans and their recognition has many practical and real world applications such as safety, security, surveillance, humanoid assistive robotics and intelligent simulation systems. Numerous human action and emotion recognition systems included analysis of position and geometric features and gesture based co-ordinates to detect actions. There exits additional data and information in the movement and motion based features and temporal and time-sequential series of image and video frames which can be leveraged to detect and extract a certain actions, postures, gestures and expressions. This paper uses dynamic, temporal, time-scale dependent data to compare with decision rules and templates for activity recognition. The human shape boundaries and silhouette is extracted using geometric co-ordinate and centroid model across multiple frames. The extracted shape boundary is transformed to binary state using eigen space mapping and parameter dependent canonical transformation in 3D space dimension. The image blob data frames are down sampled using activity templates to a single candidate reference frame. This candidate frame was compared with the decision rule driven model to associate with an activity class label. The decision rule driven and activity templates method produced 64% recognition accuracy indicating that the method was feasible for recognizing human activities.
Category: Artificial Intelligence

[203] viXra:1608.0076 [pdf] submitted on 2016-08-08 02:57:31

Watson Doctor

Authors: George Rajna
Comments: 19 Pages.

Watson correctly diagnoses woman after doctors were stumped. [12] The artificial intelligence system's ability to set itself up quickly every morning and compensate for any overnight fluctuations would make this fragile technology much more useful for field measurements, said co-lead researcher Dr Michael Hush from UNSW ADFA. [11] Quantum physicist Mario Krenn and his colleagues in the group of Anton Zeilinger from the Faculty of Physics at the University of Vienna and the Austrian Academy of Sciences have developed an algorithm which designs new useful quantum experiments. As the computer does not rely on human intuition, it finds novel unfamiliar solutions. [10] Researchers at the University of Chicago's Institute for Molecular Engineering and the University of Konstanz have demonstrated the ability to generate a quantum logic operation, or rotation of the qubit, that-surprisingly—is intrinsically resilient to noise as well as to variations in the strength or duration of the control. Their achievement is based on a geometric concept known as the Berry phase and is implemented through entirely optical means within a single electronic spin in diamond. [9] New research demonstrates that particles at the quantum level can in fact be seen as behaving something like billiard balls rolling along a table, and not merely as the probabilistic smears that the standard interpretation of quantum mechanics suggests. But there's a catch-the tracks the particles follow do not always behave as one would expect from "realistic" trajectories, but often in a fashion that has been termed "surrealistic." [8] Quantum entanglement—which occurs when two or more particles are correlated in such a way that they can influence each other even across large distances—is not an all-or-nothing phenomenon, but occurs in various degrees. The more a quantum state is entangled with its partner, the better the states will perform in quantum information applications. Unfortunately, quantifying entanglement is a difficult process involving complex optimization problems that give even physicists headaches. [7] A trio of physicists in Europe has come up with an idea that they believe would allow a person to actually witness entanglement. Valentina Caprara Vivoli, with the University of Geneva, Pavel Sekatski, with the University of Innsbruck and Nicolas Sangouard, with the University of Basel, have together written a paper describing a scenario where a human subject would be able to witness an instance of entanglement—they have uploaded it to the arXiv server for review by others. [6] The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the relativistic quantum theory.
Category: Artificial Intelligence

[202] viXra:1608.0041 [pdf] submitted on 2016-08-04 08:00:47

Combining Infinity Number Of Neural Networks Into One

Authors: Bo Tian
Comments: 17 Pages.

One of the important aspects of a neural network is its generalization property, which is measured by its ability to make correct prediction on unseen samples. One option to improve generalization is to combine results from multiple networks, which is unfortunately a time-consuming process. In this paper, a new approach is presented to combine infinity number of neural networks in analytic way to produce a small, fast and reliable neural network.
Category: Artificial Intelligence

[201] viXra:1607.0484 [pdf] submitted on 2016-07-25 21:28:15

Active Appearance Model Construction: Implementation notes

Authors: Nikzad Babaii Rizvandi, Wilfried Philips, Aleksandra Pizurica
Comments: 7 Pages.

Active Appearance Model (AAM) is a powerful object modeling technique and one of the best available ones in computer vision and computer graphics. This approach is however quite complex and various parts of its implementation were addressed separately by different researchers in several recent works. In this paper, we present systematically a full implementation of the AAM model with pseudo codes for the crucial steps in the construction of this model.
Category: Artificial Intelligence

[200] viXra:1607.0483 [pdf] submitted on 2016-07-25 21:29:15

Active Appearance Model (Aam) from Theory to Implementation

Authors: Nikzad Babaii Rizvandi, Aleksandra Pizˇurica, Wilfried Philips
Comments: 4 Pages.

Active Appearance Model (AAM) is a kind of deformable shape descriptors which is widely used in computer vision and computer graphics. This approach utilizes statistical model obtained from some images in training set and gray-value information of the texture to fit on the boundaries of a new image. In this paper, we describe a brief implementation, apply the method on hand object and finally discuss its performance in compare to Active Shape Model(ASM). Our experiments shows this method is more sensitive to the initialization and slower than ASM.
Category: Artificial Intelligence

[199] viXra:1607.0459 [pdf] submitted on 2016-07-24 21:30:52

Pattern Recognition and Learning in Bistable Cam Networks

Authors: Vladimir Chinarov, Martin Dudziak, Yuri Kyrpach
Comments: 12 Pages.

The present study concerns the problem of learning, pattern recognition and computational abilities of a homogeneous network composed from coupled bistable units. New possibilities for pattern recognition may be realized due to the developed technique that permits a reconstruction of a dynamical system using the distributions of its attractors. In both cases the updating procedure for the coupling matrix uses the minimization of least-mean-square errors between the applied and desired patterns.
Category: Artificial Intelligence

[198] viXra:1607.0073 [pdf] submitted on 2016-07-07 02:49:14

Indian Buffet Process Deep Generative Models

Authors: Sotirios P. Chatzis
Comments: 16 Pages.

Deep generative models (DGMs) have brought about a major breakthrough, as well as renewed interest, in generative latent variable models. However, an issue current DGM formulations do not address concerns the data-driven inference of the number of latent features needed to represent the observed data. Traditional linear formulations allow for addressing this issue by resorting to tools from the field of nonparametric statistics: Indeed, nonparametric linear latent variable models, obtained by appropriate imposition of Indian Buffet Process (IBP) priors, have been extensively studied by the machine learning community; inference for such models can been performed either via exact sampling or via approximate variational techniques. Based on this inspiration, in this paper we examine whether similar ideas from the field of Bayesian nonparametrics can be utilized in the context of modern DGMs in order to address the latent variable dimensionality inference problem. To this end, we propose a novel DGM formulation, based on the imposition of an IBP prior. We devise an efficient Black-Box Variational inference algorithm for our model, and exhibit its efficacy in a number of semi-supervised classification experiments. In all cases, we use popular benchmark datasets, and compare to state-of-the-art DGMs.
Category: Artificial Intelligence

[197] viXra:1607.0014 [pdf] submitted on 2016-07-01 14:54:48

Interval-Valued Neutrosophic Oversets, Neutrosophic Undersets, and Neutrosophic Offsets

Authors: Florentin Smarandache
Comments: 4 Pages.

We have proposed since 1995 the existence of degrees of membership of an element with respect to a neutrosophic set to also be partially or totally above 1 (overmembership), and partially or totally below 0 (undermembership) in order to better describe our world problems [published in 2007].
Category: Artificial Intelligence

[196] viXra:1606.0343 [pdf] submitted on 2016-06-30 08:36:33

Neutrosophic Overset, Neutrosophic Underset, and Neutrosophic Offset. Similarly for Neutrosophic Over-/Under-/Off- Logic, Probability, and Statistics

Authors: Florentin Smarandache
Comments: 168 Pages.

Neutrosophic Over-/Under-/Off-Set and -Logic were defined for the first time by Smarandache in 1995 and published in 2007. They are totally different from other sets/logics/probabilities. He extended the neutrosophic set respectively to Neutrosophic Overset {when some neutrosophic component is > 1}, Neutrosophic Underset {when some neutrosophic component is < 0}, and to Neutrosophic Offset {when some neutrosophic components are off the interval [0, 1], i.e. some neutrosophic component > 1 and other neutrosophic component < 0}. This is no surprise with respect to the classical fuzzy set/logic, intuitionistic fuzzy set/logic, or classical/imprecise probability, where the values are not allowed outside the interval [0, 1], since our real-world has numerous examples and applications of over-/under-/off-neutrosophic components. Example of Neutrosophic Offset. In a given company a full-time employer works 40 hours per week. Let’s consider the last week period. Helen worked part-time, only 30 hours, and the other 10 hours she was absent without payment; hence, her membership degree was 30/40 = 0.75 < 1. John worked full-time, 40 hours, so he had the membership degree 40/40 = 1, with respect to this company. But George worked overtime 5 hours, so his membership degree was (40+5)/40 = 45/40 = 1.125 > 1. Thus, we need to make distinction between employees who work overtime, and those who work full-time or part-time. That’s why we need to associate a degree of membership strictly greater than 1 to the overtime workers. Now, another employee, Jane, was absent without pay for the whole week, so her degree of membership was 0/40 = 0. Yet, Richard, who was also hired as a full-time, not only didn’t come to work last week at all (0 worked hours), but he produced, by accidentally starting a devastating fire, much damage to the company, which was estimated at a value half of his salary (i.e. as he would have gotten for working 20 hours that week). Therefore, his membership degree has to be less that Jane’s (since Jane produced no damage). Whence, Richard’s degree of membership, with respect to this company, was - 20/40 = - 0.50 < 0. Consequently, we need to make distinction between employees who produce damage, and those who produce profit, or produce neither damage no profit to the company. Therefore, the membership degrees > 1 and < 0 are real in our world, so we have to take them into consideration. Then, similarly, the Neutrosophic Logic/Measure/Probability/Statistics etc. were extended to respectively Neutrosophic Over-/Under-/Off-Logic, -Measure, -Probability, -Statistics etc. [Smarandache, 2007].
Category: Artificial Intelligence

[195] viXra:1606.0341 [pdf] submitted on 2016-06-30 08:42:46

Operators on Single-Valued Neutrosophic Oversets, Neutrosophic Undersets, and Neutrosophic Offsets

Authors: Florentin Smarandache
Comments: 5 Pages.

We have defined Neutrosophic Over-/Under-/Off-Set and Logic for the first time in 1995 and published in 2007. During 1995-2016 we presented them to various national and international conferences and seminars. These new notions are totally different from other sets/logics/probabilities. We extended the neutrosophic set respectively to Neutrosophic Overset {when some neutrosophic component is > 1}, to Neutrosophic Underset {when some neutrosophic component is < 0}, and to Neutrosophic Offset {when some neutrosophic components are off the interval [0, 1], i.e. some neutrosophic component > 1 and other neutrosophic component < 0}. This is no surprise since our real-world has numerous examples and applications of over-/under-/off-neutrosophic components.
Category: Artificial Intelligence

[194] viXra:1606.0272 [pdf] submitted on 2016-06-25 19:29:46

Self-Controlled Dynamics

Authors: Michail Zak
Comments: 26 Pages.

A new class of dynamical system described by ODE coupled with their Liouville equation has been introduced and discussed. These systems called self-controlled, or self-supervised since the role of actuators is played by the probability produced by the Liouville equation. Following the Madelung equation that belongs to this class, non-Newtonian properties such as randomness, entanglement, and probability interference typical for quantum systems have been described. Special attention was paid to the capability to violate the second law of thermodynamics, which makes these systems neither Newtonian, nor quantum. It has been shown that self-controlled dynamical systems can be linked to mathematical models of livings as well as to models of AI. The central point of this paper is the application of the self-controlled systems to NP-complete problems known as being unsolvable neither by classical nor by quantum algorithms. The approach is illustrated by solving a search in unsorted database in polynomial time by resonance between external force representing the address of a required item and the response representing location of this item.
Category: Artificial Intelligence

[193] viXra:1606.0181 [pdf] submitted on 2016-06-17 22:41:17

Universal Natural Memory Embedding -3 (AI)

Authors: Ramesh Chandra Bagadi
Comments: 18 Pages.

In this research investigation, the author has presented a theory of ‘Universal Relative Metric That Generates A Field Super-Set To The Fields Generated By Various Distinct Relative Metrics’.
Category: Artificial Intelligence

[192] viXra:1606.0155 [pdf] submitted on 2016-06-15 07:30:51

Universal Natural Memory Embedding - Part Two

Authors: Ramesh Chandra Bagadi
Comments: 14 Pages.

In this research investigation, the author has presented a theory of ‘The Universal Irreducible Any Field Generating Metric’.
Category: Artificial Intelligence

[191] viXra:1606.0146 [pdf] submitted on 2016-06-15 00:17:24

Universal Natural Memory Embedding - I

Authors: Ramesh Chandra Bagadi
Comments: 14 Pages.

In this research investigation, the author has presented a theory of ‘Universal Natural Memory Embedding’.
Category: Artificial Intelligence

[190] viXra:1605.0288 [pdf] submitted on 2016-05-29 02:24:48

Syllabic Networks: Measuring the Redundancy of Associative Syntactic Patterns

Authors: Bradly Alicea
Comments: 6 Pages. 3 figures, 1 table

The self-organization and diversity inherent in natural and artificial language can be revealed using a technique called syllabic network decomposition. The topology of such networks are determined by a series of linguistic strings which are broken apart at critical points and then linked together in a non-linear fashion. Small proof-of-concept examples are given using words from the English language. A criterion for connectedness and two statistical parameters for measuring connectedness are applied to these examples. To conclude, we will discuss some applications of this technique, ranging from improving models of speech recognition to bioinformatic analysis and recreational games.
Category: Artificial Intelligence

[189] viXra:1605.0190 [pdf] submitted on 2016-05-18 06:12:00

The Algorithm of the Thinking Machine

Authors: Dimiter Dobrev
Comments: 13 Pages. Represented at 12 of May 2016 at Faculty of Mathematics and Informatics, University of Sofia.

Throughout my life I’ve tried to answer the question "What is AI?" and write the program that is AI. I’ve already known the answer to the question "What is AI?" for 16 years now but the AI algorithm has eluded me. I’ve come up with individual fragments but something has always been missing to put the puzzle together. Finally, I gathered all the missing pieces and I can introduce you to this algorithm. That is, in this article you will find a sufficiently detailed description of the algorithm of the AI. That sounds so audacious that probably you won’t believe me. Frankly, even I do not fully believe myself. I’ll believe it only when someone writes a program executing this algorithm and when I see that this program actually works. Even if you don’t manage to believe in the importance of this article, I hope that you will like it.
Category: Artificial Intelligence

[188] viXra:1605.0178 [pdf] submitted on 2016-05-16 10:25:46

Artificial Intelligence Replaces Physicists

Authors: George Rajna
Comments: 19 Pages.

The artificial intelligence system's ability to set itself up quickly every morning and compensate for any overnight fluctuations would make this fragile technology much more useful for field measurements, said co-lead researcher Dr Michael Hush from UNSW ADFA. [11] Quantum physicist Mario Krenn and his colleagues in the group of Anton Zeilinger from the Faculty of Physics at the University of Vienna and the Austrian Academy of Sciences have developed an algorithm which designs new useful quantum experiments. As the computer does not rely on human intuition, it finds novel unfamiliar solutions. [10] Researchers at the University of Chicago's Institute for Molecular Engineering and the University of Konstanz have demonstrated the ability to generate a quantum logic operation, or rotation of the qubit, that-surprisingly—is intrinsically resilient to noise as well as to variations in the strength or duration of the control. Their achievement is based on a geometric concept known as the Berry phase and is implemented through entirely optical means within a single electronic spin in diamond. [9] New research demonstrates that particles at the quantum level can in fact be seen as behaving something like billiard balls rolling along a table, and not merely as the probabilistic smears that the standard interpretation of quantum mechanics suggests. But there's a catch-the tracks the particles follow do not always behave as one would expect from "realistic" trajectories, but often in a fashion that has been termed "surrealistic." [8] Quantum entanglement—which occurs when two or more particles are correlated in such a way that they can influence each other even across large distances—is not an all-or-nothing phenomenon, but occurs in various degrees. The more a quantum state is entangled with its partner, the better the states will perform in quantum information applications. Unfortunately, quantifying entanglement is a difficult process involving complex optimization problems that give even physicists headaches. [7] A trio of physicists in Europe has come up with an idea that they believe would allow a person to actually witness entanglement. Valentina Caprara Vivoli, with the University of Geneva, Pavel Sekatski, with the University of Innsbruck and Nicolas Sangouard, with the University of Basel, have together written a paper describing a scenario where a human subject would be able to witness an instance of entanglement—they have uploaded it to the arXiv server for review by others. [6] The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron's spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the relativistic quantum theory.
Category: Artificial Intelligence

[187] viXra:1605.0125 [pdf] submitted on 2016-05-12 08:13:31

Failure Mode and Effects Analysis Based on D Numbers and Topsis

Authors: Tian Bian, Haoyang Zheng, Likang Yin, Yong Deng, Sankaran Mahadevand
Comments: 39 Pages.

Failure mode and effects analysis (FMEA) is a widely used technique for assessing the risk of potential failure modes in designs, products, process, system or services. One of the main problems of FMEA is to deal with a variety of assessments given by FMEA team members and sequence the failure modes according to the degree of risk factors. The traditional FMEA using risk priority number (RPN) which is the product of occurrence (O), severity (S) and detection (D) of a failure to determine the risk priority ranking order of failure modes. However, it will become impractical when multiple experts give different risk assessments to one failure mode, which may be imprecise or incomplete or the weights of risk factors is inconsistent. In this paper, a new risk priority model based on D numbers, and technique for order of preference by similarity to ideal solution (TOPSIS) is proposed to evaluate the risk in FMEA. In the proposed model, the assessments given by FMEA team members are represented by D numbers, a method can effectively handle uncertain information. TOPSIS method, a novel multi-criteria decision making (MCDM) method is presented to rank the preference of failure modes respect to risk factors. Finally, an application of the failure modes of rotor blades of an aircraft turbine is provided to illustrate the efficiency of the proposed method.
Category: Artificial Intelligence

[186] viXra:1603.0378 [pdf] submitted on 2016-03-27 16:51:16

A Review of Theoretical and Practical Challenges of Trusted Autonomy in Big Data

Authors: Hussein A. Abbass, George Leu, Kathryn Merrick
Comments: 32 Pages.

Despite the advances made in artificial intelligence, software agents, and robotics, there is little we see today that we can truly call a fully autonomous system. We conjecture that the main inhibitor for advancing autonomy is lack of trust. Trusted autonomy is the scientific and engineering field to establish the foundations and ground work for developing trusted autonomous systems (robotics and software agents) that can be used in our daily life, and can be integrated with humans seamlessly, naturally and efficiently. In this paper, we review this literature to reveal opportunities for researchers and practitioners to work on topics that can create a leap forward in advancing the field of trusted autonomy. We focus the paper on the `trust' component as the uniting technology between humans and machines. Our inquiry into this topic revolves around three sub-topics: (1) reviewing and positioning the trust modelling literature for the purpose of trusted autonomy; (2) reviewing a critical subset of sensor technologies that allow a machine to sense human states; and (3) distilling some critical questions for advancing the field of trusted autonomy. The inquiry is augmented with conceptual models that we propose along the way by recompiling and reshaping the literature into forms that enables trusted autonomous systems to become a reality. The paper offers a vision for a Trusted Cyborg Swarm, an extension of our previous Cognitive Cyber Symbiosis concept, whereby humans and machines meld together in a harmonious, seamless, and coordinated manner.
Category: Artificial Intelligence

[185] viXra:1603.0335 [pdf] submitted on 2016-03-23 10:04:45

Conditional Deng Entropy, Joint Deng Entropy and Generalized Mutual Information

Authors: Haoyang Zheng, Yong Deng
Comments: 16 Pages.

Shannon entropy, conditional entropy, joint entropy and mutual information, can estimate the chaotic level of information. However, these methods could only handle certain situations. Based on Deng entropy, this paper introduces multiple new entropy to estimate entropy under multiple interactive uncertain information: conditional Deng entropy is used to calculate entropy under conditional basic belief assignment; joint Deng entropy could calculate entropy by applying joint basic belief assignment distribution; generalized mutual information is applied to estimate the uncertainty of information under knowing another information. Numerical examples are used for illustrating the function of new entropy in the end.
Category: Artificial Intelligence

Replacements of recent Submissions

[49] viXra:1805.0214 [pdf] replaced on 2018-05-31 04:56:00

AI Should Not Be an Open Source Project

Authors: Dimiter Dobrev
Comments: 9 Pages.

Who should own the Artificial Intelligence technology? It should belong to everyone, properly said not the technology per se, but the fruits that can be reaped from it. Obviously, we should not let AI end up in the hands of irresponsible persons. Likewise, nuclear technology should benefit all, however it should be kept secret and inaccessible by the public at large.
Category: Artificial Intelligence

[48] viXra:1801.0271 [pdf] replaced on 2018-01-22 21:08:14

Refutation: Neutrosophic Logic by Florentin Smarandache as Generalized Intuitionistic, Fuzzy Logic © 2018 by Colin James III All Rights Reserved.

Authors: Colin James III
Comments: 2 Pages. © 2018 by Colin James III All rights reserved.

We map the neutrosophic logical values of truth, falsity, and indeterminacy on intervals "]0,1[" and "]-0,1+[" in equations for the Meth8/VL4 apparatus. We test the summation of the those values. The result is not tautologous, meaning neutrosophic logic is refuted and hence its use as a generalization of intuitionistic, fuzzy logic is likewise unworkable.
Category: Artificial Intelligence

[47] viXra:1712.0071 [pdf] replaced on 2018-04-26 10:08:54

The IQ of Artificial Intelligence

Authors: Dimiter Dobrev
Comments: 24 Pages. Serdica Journal of Computing

All it takes to identify the computer programs which are Artificial Intelligence is to give them a test and award AI to those that pass the test. Let us say that the scores they earn at the test will be called IQ. We cannot pinpoint a minimum IQ threshold that a program has to cover in order to be AI, however, we will choose a certain value. Thus, our definition for AI will be any program the IQ of which is above the chosen value. While this idea has already been implemented in [3], here we will revisit this construct in order to introduce certain improvements.
Category: Artificial Intelligence

[46] viXra:1712.0071 [pdf] replaced on 2018-01-28 06:21:06

The Intelligence Quotient of the Artificial Intelligence

Authors: Dimiter Dobrev
Comments: 27 Pages. Bulgarian. Serdica Journal of Computing

To say which programs are AI, it's enough to run an exam and recognize for AI those programs that passed the exam. The exam grade will be called IQ. We cannot say just how big the IQ has to be in order one program to be AI, but we will choose a specific value. So our definition of AI will be any program whose IQ is above this specific value. This idea has already been realized in [3], but here we will repeat this construction by bringing some improvements.
Category: Artificial Intelligence

[45] viXra:1711.0265 [pdf] replaced on 2017-11-27 03:16:15

Revisit Fuzzy Neural Network: Bridging the Gap Between Fuzzy Logic and Deep Learning

Authors: Lixin Fan
Comments: 76 Pages.

This article aims to establish a concrete and fundamental connection between two important elds in artificial intelligence i.e. deep learning and fuzzy logic. On the one hand, we hope this article will pave the way for fuzzy logic researchers to develop convincing applications and tackle challenging problems which are of interest to machine learning community too. On the other hand, deep learning could benefit from the comparative research by re-examining many trail-and-error heuristics in the lens of fuzzy logic, and consequently, distilling the essential ingredients with rigorous foundations. Based on the new findings reported in [41] and this article, we believe the time is ripe to revisit fuzzy neural network as a crucial bridge between two schools of AI research i.e. symbolic versus connectionist [101] and eventually open the black-box of artificial neural networks.
Category: Artificial Intelligence

[44] viXra:1711.0265 [pdf] replaced on 2017-11-17 16:28:38

Revisit Fuzzy Neural Network: Bridging the Gap Between Fuzzy Logic and Deep Learning

Authors: Lixin Fan
Comments: 76 Pages.

This article aims to establish a concrete and fundamental connection between two important fields in artificial intelligence i.e. deep learning and fuzzy logic. On the one hand, we hope this article will pave the way for fuzzy logic researchers to develop convincing applications and tackle challenging problems which are of interest to machine learning community too. On the other hand, deep learning could benefit from the comparative research by re-examining many trail-and-error heuristics in the lens of fuzzy logic, and consequently, distilling the essential ingredients with rigorous foundations. Based on the new findings reported in [38] and this article, we believe the time is ripe to revisit fuzzy neural network as a crucial bridge between two schools of AI research i.e. symbolic versus connectionist [93] and eventually open the black-box of artificial neural networks.
Category: Artificial Intelligence

[43] viXra:1710.0324 [pdf] replaced on 2017-11-09 05:34:27

New Sufficient Conditions of Signal Recovery with Tight Frames Via $l_1$-Analysis

Authors: Jianwen Huang, Jianjun Wang, Feng Zhang, Wendong Wang
Comments: 18 Pages.

The paper discusses the recovery of signals in the case that signals are nearly sparse with respect to a tight frame $D$ by means of the $l_1$-analysis approach. We establish several new sufficient conditions regarding the $D$-restricted isometry property to ensure stable reconstruction of signals that are approximately sparse with respect to $D$. It is shown that if the measurement matrix $\Phi$ fulfils the condition $\delta_{ts}<t/(4-t)$ for $0<t<4/3$, then signals which are approximately sparse with respect to $D$ can be stably recovered by the $l_1$-analysis method. In the case of $D=I$, the bound is sharp, see Cai and Zhang's work \cite{Cai and Zhang 2014}. When $t=1$, the present bound improves the condition $\delta_s<0.307$ from Lin et al.'s reuslt to $\delta_s<1/3$. In addition, numerical simulations are conducted to indicate that the $l_1$-analysis method can stably reconstruct the sparse signal in terms of tight frames.
Category: Artificial Intelligence

[42] viXra:1709.0108 [pdf] replaced on 2017-09-10 08:24:10

A New Semantic Theory of Nature Language

Authors: Kun Xing
Comments: 70 Pages.

Formal Semantics and Distributional Semantics are two important semantic frameworks in Natural Language Processing (NLP). Cognitive Semantics belongs to the movement of Cognitive Linguistics, which is based on contemporary cognitive science. Each framework could deal with some meaning phenomena, but none of them fulfills all requirements proposed by applications. A unified semantic theory characterizing all important language phenomena has both theoretical and practical significance; however, although many attempts have been made in recent years, no existing theory has achieved this goal yet. This article introduces a new semantic theory that has the potential to characterize most of the important meaning phenomena of natural language and to fulfill most of the necessary requirements for philosophical analysis and for NLP applications. The theory is based on a unified representation of information, and constructs a kind of mathematical model called cognitive model to interpret natural language expressions in a compositional manner. It accepts the empirical assumption of Cognitive Semantics, and overcomes most shortcomings of Formal Semantics and of Distributional Semantics. The theory, however, is not a simple combination of existing theories, but an extensive generalization of classic logic and Formal Semantics. It inherits nearly all advantages of Formal Semantics, and also provides descriptive contents for objects and events as fine-gram as possible, descriptive contents which represent the results of human cognition.
Category: Artificial Intelligence

[41] viXra:1611.0211 [pdf] replaced on 2016-12-01 04:59:33

A Variable Order Hidden Markov Model with Dependence Jumps

Authors: Anastasios Petropoulos, Stelios Xanthopoulos, Sotirios P. Chatzis
Comments: 33 Pages.

Hidden Markov models (HMMs) are a popular approach for modeling sequential data, typically based on the assumption of a first- or moderate-order Markov chain. However, in many real-world scenarios the modeled data entail temporal dynamics the patterns of which change over time. In this paper, we address this problem by proposing a novel HMM formulation, treating temporal dependencies as latent variables over which inference is performed. Specifically, we introduce a hierarchical graphical model comprising two hidden layers: on the first layer, we postulate a chain of latent observation-emitting states, the temporal dependencies between which may change over time; on the second layer, we postulate a latent first-order Markov chain modeling the evolution of temporal dynamics (dependence jumps) pertaining to the first-layer latent process. As a result of this construction, our method allows for effectively modeling non-homogeneous observed data, where the patterns of the entailed temporal dynamics may change over time. We devise efficient training and inference algorithms for our model, following the expectation-maximization paradigm. We demonstrate the efficacy and usefulness of our approach considering several real-world datasets. As we show, our model allows for increased modeling and predictive performance compared to the alternative methods, while offering a good trade-off between the resulting increases in predictive performance and computational complexity.
Category: Artificial Intelligence

[40] viXra:1611.0211 [pdf] replaced on 2016-11-14 08:01:26

A Variable Order Hidden Markov Model with Dependence Jumps

Authors: Anastasios Petropoulos, Stelios Xanthopoulos, Sotirios P. Chatzis
Comments: 15 Pages.

Hidden Markov models (HMMs) are a popular approach for modeling sequential data, typically based on the assumption of a first- or moderate-order Markov chain. However, in many real-world scenarios the modeled data entail temporal dynamics the patterns of which change over time. In this paper, we address this problem by proposing a novel HMM formulation, treating temporal dependencies as latent variables over which inference is performed. Specifically, we introduce a hierarchical graphical model comprising two hidden layers: on the first layer, we postulate a chain of latent observation-emitting states, the temporal dependencies between which may change over time; on the second layer, we postulate a latent first-order Markov chain modeling the evolution of temporal dynamics (dependence jumps) pertaining to the first-layer latent process. As a result of this construction, our method allows for effectively modeling non-homogeneous observed data, where the patterns of the entailed temporal dynamics may change over time. We devise efficient training and inference algorithms for our model, following the expectation-maximization paradigm. We demonstrate the efficacy and usefulness of our approach considering several real-world datasets. As we show, our model allows for increased modeling and predictive performance compared to the alternative methods, while offering a good trade-off between the resulting increases in predictive performance and computational complexity.
Category: Artificial Intelligence

[39] viXra:1611.0211 [pdf] replaced on 2016-11-14 04:26:58

A Variable Order Hidden Markov Model with Dependence Jumps

Authors: Anastasios Petropoulos, Stelios Xanthopoulos, Sotirios P. Chatzis
Comments: 15 Pages.

Hidden Markov models (HMMs) are a popular approach for modeling sequential data, typically based on the assumption of a first- or moderate-order Markov chain. However, in many real-world scenarios the modeled data entail temporal dynamics the patterns of which change over time. In this paper, we address this problem by proposing a novel HMM formulation, treating temporal dependencies as latent variables over which inference is performed. Specifically, we introduce a hierarchical graphical model comprising two hidden layers: on the first layer, we postulate a chain of latent observation-emitting states, the temporal dependencies between which may change over time; on the second layer, we postulate a latent first-order Markov chain modeling the evolution of temporal dynamics (dependence jumps) pertaining to the first-layer latent process. As a result of this construction, our method allows for effectively modeling non-homogeneous observed data, where the patterns of the entailed temporal dynamics may change over time. We devise efficient training and inference algorithms for our model, following the expectation-maximization paradigm. We demonstrate the efficacy and usefulness of our approach considering several real-world datasets. As we show, our model allows for increased modeling and predictive performance compared to the alternative methods, while offering a good trade-off between the resulting increases in predictive performance and computational complexity.
Category: Artificial Intelligence

[38] viXra:1610.0029 [pdf] replaced on 2016-10-16 08:51:52

Associative Broadcast Neural Network

Authors: Aleksei Morozov
Comments: 5 Pages.

Associative broadcast neural network (aka Ether Neural Network) is an artificial neural network inspired by a hypothesis of broadcasting of neuron's output pattern in a biological neural network. Neuron has wire connections and ether connections. Ether connections are electrical. Wire connections provide a recognition functionality. Ether connections provide an association functionality.
Category: Artificial Intelligence

[37] viXra:1607.0073 [pdf] replaced on 2016-07-08 06:55:14

Indian Buffet Process Deep Generative Models

Authors: Sotirios P. Chatzis
Comments: 16 Pages.

Deep generative models (DGMs) have brought about a major breakthrough, as well as renewed interest, in generative latent variable models. However, an issue current DGM formulations do not address concerns the data-driven inference of the number of latent features needed to represent the observed data. Traditional linear formulations allow for addressing this issue by resorting to tools from the field of nonparametric statistics: Indeed, nonparametric linear latent variable models, obtained by appropriate imposition of Indian Buffet Process (IBP) priors, have been extensively studied by the machine learning community; inference for such models can been performed either via exact sampling or via approximate variational techniques. Based on this inspiration, in this paper we examine whether similar ideas from the field of Bayesian nonparametrics can be utilized in the context of modern DGMs in order to address the latent variable dimensionality inference problem. To this end, we propose a novel DGM formulation, based on the imposition of an IBP prior. We devise an efficient Black-Box Variational inference algorithm for our model, and exhibit its efficacy in a number of semi-supervised classification experiments. In all cases, we use popular benchmark datasets, and compare to state-of-the-art DGMs.
Category: Artificial Intelligence

[36] viXra:1607.0073 [pdf] replaced on 2016-07-08 03:07:07

Indian Buffet Process Deep Generative Models

Authors: Sotirios P. Chatzis
Comments: 16 Pages.

Deep generative models (DGMs) have brought about a major breakthrough, as well as renewed interest, in generative latent variable models. However, an issue current DGM formulations do not address concerns the data-driven inference of the number of latent features needed to represent the observed data. Traditional linear formulations allow for addressing this issue by resorting to tools from the field of nonparametric statistics: Indeed, nonparametric linear latent variable models, obtained by appropriate imposition of Indian Buffet Process (IBP) priors, have been extensively studied by the machine learning community; inference for such models can been performed either via exact sampling or via approximate variational techniques. Based on this inspiration, in this paper we examine whether similar ideas from the field of Bayesian nonparametrics can be utilized in the context of modern DGMs in order to address the latent variable dimensionality inference problem. To this end, we propose a novel DGM formulation, based on the imposition of an IBP prior. We devise an efficient Black-Box Variational inference algorithm for our model, and exhibit its efficacy in a number of semi-supervised classification experiments. In all cases, we use popular benchmark datasets, and compare to state-of-the-art DGMs.
Category: Artificial Intelligence

[35] viXra:1607.0073 [pdf] replaced on 2016-07-07 10:41:19

Indian Buffet Process Deep Generative Models

Authors: Sotirios P. Chatzis
Comments: 16 Pages.

Deep generative models (DGMs) have brought about a major breakthrough, as well as renewed interest, in generative latent variable models. However, an issue current DGM formulations do not address concerns the data-driven inference of the number of latent features needed to represent the observed data. Traditional linear formulations allow for addressing this issue by resorting to tools from the field of nonparametric statistics: Indeed, nonparametric linear latent variable models, obtained by appropriate imposition of Indian Buffet Process (IBP) priors, have been extensively studied by the machine learning community; inference for such models can been performed either via exact sampling or via approximate variational techniques. Based on this inspiration, in this paper we examine whether similar ideas from the field of Bayesian nonparametrics can be utilized in the context of modern DGMs in order to address the latent variable dimensionality inference problem. To this end, we propose a novel DGM formulation, based on the imposition of an IBP prior. We devise an efficient Black-Box Variational inference algorithm for our model, and exhibit its efficacy in a number of semi-supervised classification experiments. In all cases, we use popular benchmark datasets, and compare to state-of-the-art DGMs.
Category: Artificial Intelligence

[34] viXra:1607.0073 [pdf] replaced on 2016-07-07 08:09:10

Indian Buffet Process Deep Generative Models

Authors: Sotirios P. Chatzis
Comments: 16 Pages.

Deep generative models (DGMs) have brought about a major breakthrough, as well as renewed interest, in generative latent variable models. However, an issue current DGM formulations do not address concerns the data-driven inference of the number of latent features needed to represent the observed data. Traditional linear formulations allow for addressing this issue by resorting to tools from the field of nonparametric statistics: Indeed, nonparametric linear latent variable models, obtained by appropriate imposition of Indian Buffet Process (IBP) priors, have been extensively studied by the machine learning community; inference for such models can been performed either via exact sampling or via approximate variational techniques. Based on this inspiration, in this paper we examine whether similar ideas from the field of Bayesian nonparametrics can be utilized in the context of modern DGMs in order to address the latent variable dimensionality inference problem. To this end, we propose a novel DGM formulation, based on the imposition of an IBP prior. We devise an efficient Black-Box Variational inference algorithm for our model, and exhibit its efficacy in a number of semi-supervised classification experiments. In all cases, we use popular benchmark datasets, and compare to state-of-the-art DGMs.
Category: Artificial Intelligence

[33] viXra:1607.0073 [pdf] replaced on 2016-07-07 07:08:00

Indian Buffet Process Deep Generative Models

Authors: Sotirios P. Chatzis
Comments: 16 Pages.

Deep generative models (DGMs) have brought about a major breakthrough, as well as renewed interest, in generative latent variable models. However, an issue current DGM formulations do not address concerns the data-driven inference of the number of latent features needed to represent the observed data. Traditional linear formulations allow for addressing this issue by resorting to tools from the field of nonparametric statistics: Indeed, nonparametric linear latent variable models, obtained by appropriate imposition of Indian Buffet Process (IBP) priors, have been extensively studied by the machine learning community; inference for such models can been performed either via exact sampling or via approximate variational techniques. Based on this inspiration, in this paper we examine whether similar ideas from the field of Bayesian nonparametrics can be utilized in the context of modern DGMs in order to address the latent variable dimensionality inference problem. To this end, we propose a novel DGM formulation, based on the imposition of an IBP prior. We devise an efficient Black-Box Variational inference algorithm for our model, and exhibit its efficacy in a number of semi-supervised classification experiments. In all cases, we use popular benchmark datasets, and compare to state-of-the-art DGMs.
Category: Artificial Intelligence

[32] viXra:1606.0272 [pdf] replaced on 2016-11-24 16:47:04

Self-Controlled Dynamics

Authors: Michail Zak
Comments: 26 Pages.

A new class of dynamical system described by ODE coupled with their Liouville equation has been introduced and discussed. These systems called self-controlled, or self-supervised since the role of actuators is played by the probability produced by the Liouville equation. Following the Madelung equation that belongs to this class, non- Newtonian properties such as randomness, entanglement, and probability interference typical for quantum systems have been described. Special attention was paid to the capability to violate the second law of thermodynamics, which makes these systems neither Newtonian, nor quantum. It has been shown that self-controlled dynamical systems can be linked to mathematical models of livings as well as to models of AI. The central point of this paper is the application of the self-controlled systems to NP-complete problems known as being unsolvable neither by classical nor by quantum algorithms. The approach is illustrated by solving a search in unsorted database in polynomial time by resonance between external force representing the address of a required item and the response representing location of this item.
Category: Artificial Intelligence

[31] viXra:1606.0272 [pdf] replaced on 2016-11-24 15:46:24

Self-Controlled Dynamics

Authors: Michail Zak
Comments: 26 Pages.

A new class of dynamical system described by ODE coupled with their Liouville equation has been introduced and discussed. These systems called self-controlled, or self-supervised since the role of actuators is played by the probability produced by the Liouville equation. Following the Madelung equation that belongs to this class, non- Newtonian properties such as randomness, entanglement, and probability interference typical for quantum systems have been described. Special attention was paid to the capability to violate the second law of thermodynamics, which makes these systems neither Newtonian, nor quantum. It has been shown that self-controlled dynamical systems can be linked to mathematical models of livings as well as to models of AI. The central point of this paper is the application of the self-controlled systems to NP-complete problems known as being unsolvable neither by classical nor by quantum algorithms. The approach is illustrated by solving a search in unsorted database in polynomial time by resonance between external force representing the address of a required item and the response representing location of this item.
Category: Artificial Intelligence

[30] viXra:1605.0190 [pdf] replaced on 2016-05-20 08:36:35

The Algorithm of the Thinking Machine

Authors: Dimiter Dobrev
Comments: 15 Pages. Represented at 12 of May, 2016 at Faculty of Mathematics and Informatics, University of Sofia.

In this article we consider the questions 'What is AI?' and 'How to write a program that satisfies the definition of AI?' It deals with the basic concepts and modules that must be at the heart of this program. The most interesting concept that is discussed here is the concept of abstract signals. Each of these signals is related to the result of a particular experiment. The abstract signal is a function that at any time point returns the probability the corresponding experiment to return true.
Category: Artificial Intelligence