Artificial Intelligence

1810 Submissions

[28] viXra:1810.0521 [pdf] submitted on 2018-10-31 08:51:54

Deep Learning Glaucoma

Authors: George Rajna
Comments: 52 Pages.

As part of a team of scientists from IBM and New York University, my colleagues and I are looking at new ways AI could be used to help ophthalmologists and optometrists further utilize eye images, and potentially help to speed the process for detecting glaucoma in images. [31] A team of EPFL scientists has now written a machine-learning program that can predict, in record time, how atoms will respond to an applied magnetic field. [30] Researchers from the University of Luxembourg, Technische Universität Berlin, and the Fritz Haber Institute of the Max Planck Society have combined machine learning and quantum mechanics to predict the dynamics and atomic interactions in molecules. [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]
Category: Artificial Intelligence

[27] viXra:1810.0519 [pdf] submitted on 2018-10-31 09:34:04

AI in Social Media and News

Authors: George Rajna
Comments: 49 Pages.

The technology could help identify biases in social media posts and news articles, the better to judge the information's validity. [29] Researchers find AI-generated reviews and comments pose a significant threat to consumers, but machine learning can help detect the fakes. [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

[26] viXra:1810.0499 [pdf] submitted on 2018-10-31 06:01:34

AI Recognize Galaxies

Authors: George Rajna
Comments: 36 Pages.

Researchers have taught an artificial intelligence program used to recognise faces on Facebook to identify galaxies in deep space. [22] Now, researchers at Stanford University have devised a new type of artificially intelligent camera system that can classify images faster and more energy efficiently, and that could one day be built small enough to be embedded in the devices themselves, something that is not possible today. [21] Today, deep neural networks with different architectures, such as convolutional, recurrent and autoencoder networks, are becoming an increasingly popular area of research. [20] 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]
Category: Artificial Intelligence

[25] viXra:1810.0488 [pdf] submitted on 2018-10-29 12:20:08

AI and NMR Spectroscopy

Authors: George Rajna
Comments: 51 Pages.

A team of EPFL scientists has now written a machine-learning program that can predict, in record time, how atoms will respond to an applied magnetic field. [30] Researchers from the University of Luxembourg, Technische Universität Berlin, and the Fritz Haber Institute of the Max Planck Society have combined machine learning and quantum mechanics to predict the dynamics and atomic interactions in molecules. [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]
Category: Artificial Intelligence

[24] viXra:1810.0450 [pdf] submitted on 2018-10-26 07:27:57

Machine Learning Quantum Magnetometer

Authors: George Rajna
Comments: 48 Pages.

Researchers from the Moscow Institute of Physics and Technology (MIPT), Aalto University in Finland, and ETH Zurich have demonstrated a prototype device that uses quantum effects and machine learning to measure magnetic fields more accurately than its classical analogues. [26] Researchers at the University of California San Diego have developed an approach that uses machine learning to identify and predict which genes make infectious bacteria resistant to antibiotics. [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 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]
Category: Artificial Intelligence

[23] viXra:1810.0433 [pdf] submitted on 2018-10-25 09:10:32

Machine Learning Antibiotic Resistance

Authors: George Rajna
Comments: 44 Pages.

Researchers at the University of California San Diego have developed an approach that uses machine learning to identify and predict which genes make infectious bacteria resistant to antibiotics. [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 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]
Category: Artificial Intelligence

[22] viXra:1810.0431 [pdf] submitted on 2018-10-25 09:28:30

AI Help Seniors Stay Safe

Authors: George Rajna
Comments: 34 Pages.

An autonomous intelligence system is helping seniors stay safe both at home and in care facilities, thanks to a collaboration between University of Alberta computing scientists and software technology company Spxtrm AI. [22] Now, researchers at Stanford University have devised a new type of artificially intelligent camera system that can classify images faster and more energy efficiently, and that could one day be built small enough to be embedded in the devices themselves, something that is not possible today. [21] Today, deep neural networks with different architectures, such as convolutional, recurrent and autoencoder networks, are becoming an increasingly popular area of research. [20] 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]
Category: Artificial Intelligence

[21] viXra:1810.0414 [pdf] submitted on 2018-10-24 08:25:24

AI to Create Fragrances

Authors: George Rajna
Comments: 41 Pages.

With this in mind, my team at IBM Research, together with Symrise, one of the top global producers of flavors and fragrances, created an AI system that can learn about formulas, raw materials, historical success data and industry trends. [25] The New York Times contacted IBM Research in late September asking for our help to use AI in a clever way to create art for the coming special section on AI. [24] Granting human rights to a computer would degrade human dignity. [23] IBM researchers are developing a new computer architecture, better equipped to handle increased data loads from artificial intelligence. [22] A computer built to mimic the brain's neural networks produces similar results to that of the best brain-simulation supercomputer software currently used for neural-signaling research, finds a new study published in the open-access journal Frontiers in Neuroscience. [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

[20] viXra:1810.0413 [pdf] submitted on 2018-10-24 09:11:15

Search Engines Entropy

Authors: George Rajna
Comments: 34 Pages.

Search engine entropy is thus important not only for the efficiency of search engines and those using them to find relevant information as well as to the success of the companies and other bodies running such systems, but also to those who run websites hoping to be found and visited following a search. [20] "We've experimentally confirmed the connection between information in the classical case and the quantum case," Murch said, "and we're seeing this new effect of information loss." [19] It's well-known that when a quantum system is continuously measured, it freezes, i.e., it stops changing, which is due to a phenomenon called the quantum Zeno effect. [18] Physicists have extended one of the most prominent fluctuation theorems of classical stochastic thermodynamics, the Jarzynski equality, to quantum field theory. [17] In 1993, physicist Lucien Hardy proposed an experiment showing that there is a small probability (around 6-9%) of observing a particle and its antiparticle interacting with each other without annihilating—something that is impossible in classical physics. [16] Scientists at the University of Geneva (UNIGE), Switzerland, recently reengineered their data processing, demonstrating that 16 million atoms were entangled in a one-centimetre crystal. [15] The fact that it is possible to retrieve this lost information reveals new insight into the fundamental nature of quantum measurements, mainly by supporting the idea that quantum measurements contain both quantum and classical components. [14] Researchers blur the line between classical and quantum physics by connecting chaos and entanglement. [13] Yale University scientists have reached a milestone in their efforts to extend the durability and dependability of quantum information. [12] Using lasers to make data storage faster than ever. [11] Some three-dimensional materials can exhibit exotic properties that only exist in "lower" dimensions.
Category: Artificial Intelligence

[19] viXra:1810.0377 [pdf] submitted on 2018-10-22 07:35:12

Algorithm Predict LED Materials

Authors: George Rajna
Comments: 52 Pages.

Researchers from the University of Houston have devised a new machine learning algorithm that is efficient enough to run on a personal computer and predict the properties of more than 100,000 compounds in search of those most likely to be efficient phosphors for LED lighting. [30] Researchers from the University of Luxembourg, Technische Universität Berlin, and the Fritz Haber Institute of the Max Planck Society have combined machine learning and quantum mechanics to predict the dynamics and atomic interactions in molecules. [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]
Category: Artificial Intelligence

[18] viXra:1810.0376 [pdf] submitted on 2018-10-22 08:06:06

AI and Human Creativity

Authors: George Rajna
Comments: 49 Pages.

The New York Times contacted IBM Research in late September asking for our help to use AI in a clever way to create art for the coming special section on AI. [24] Granting human rights to a computer would degrade human dignity. [23] IBM researchers are developing a new computer architecture, better equipped to handle increased data loads from artificial intelligence. [22] A computer built to mimic the brain's neural networks produces similar results to that of the best brain-simulation supercomputer software currently used for neural-signaling research, finds a new study published in the open-access journal Frontiers in Neuroscience. [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]
Category: Artificial Intelligence

[17] viXra:1810.0361 [pdf] submitted on 2018-10-23 01:45:49

AI Carry out Experiments

Authors: George Rajna
Comments: 40 Pages.

There's plenty of speculation about what artificial intelligence, or AI, will look like in the future, but researchers from The Australian National University (ANU) are already harnessing its power. [25] The New York Times contacted IBM Research in late September asking for our help to use AI in a clever way to create art for the coming special section on AI. [24] Granting human rights to a computer would degrade human dignity. [23] IBM researchers are developing a new computer architecture, better equipped to handle increased data loads from artificial intelligence. [22] A computer built to mimic the brain's neural networks produces similar results to that of the best brain-simulation supercomputer software currently used for neural-signaling research, finds a new study published in the open-access journal Frontiers in Neuroscience. [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]
Category: Artificial Intelligence

[16] viXra:1810.0347 [pdf] submitted on 2018-10-21 19:58:21

The Teleonomic Purpose of the Human Species (a Secular Discussion, Regarding Artificial General Intelligence)

Authors: Jordan Micah Bennett
Comments: 8 Pages.

This work concerns a hypothesis regarding a teleonomic description, regarding the non-trivial purpose of the human species. Teleonomy is a recent concept (with contributions from Richard Dawkins) that entails purpose in the context of objectivity/science, rather than in the context of subjectivity/deities. Teleonomy ought not to be confused for the teleological argument, which is a religious/subjective concept contrary to teleonomy, a scientific/objective concept. As such, this work concerns principles in entropy. This hypothesis was originally proposed on Research Gate in 2015.
Category: Artificial Intelligence

[15] viXra:1810.0345 [pdf] replaced on 2018-10-22 10:38:55

Cosmological Natural Selection AI

Authors: Jordan Micah Bennett
Comments: Author webpage: folioverse.appspot.com

Notably, this short paper concerns a non-serious thought experiment/statement, in the scope of a serious hypothesis of mine regarding the scientific purpose of the human species, in tandem with Cosmological Natural Selection I (CNS I). This thus may be considered as an aside wrt the aforesaid serious hypothesis, however, separately including thinking in relation to CNS I.
Category: Artificial Intelligence

[14] viXra:1810.0302 [pdf] submitted on 2018-10-20 04:10:30

Interactions in Molecules Using AI

Authors: George Rajna
Comments: 50 Pages.

Researchers from the University of Luxembourg, Technische Universität Berlin, and the Fritz Haber Institute of the Max Planck Society have combined machine learning and quantum mechanics to predict the dynamics and atomic interactions in molecules. [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

[13] viXra:1810.0246 [pdf] submitted on 2018-10-15 07:27:43

Quantum Computers with Machine Learning

Authors: George Rajna
Comments: 41 Pages.

But researchers at Purdue University are working on a solution, combining quantum algorithms with classical computing on small-scale quantum computers to speed up database accessibility. [23] Researchers at the University of Twente, working with colleagues at the Technical Universities of Delft and Eindhoven, have successfully developed a new and interesting building block. [22] Researchers at the Institut d'Optique Graduate School at the CNRS and Université Paris-Saclay in France have used a laser-based technique to rearrange cold atoms one-by-one into fully ordered 3D patterns. [21] Reduced entropy in a three-dimensional lattice of super-cooled, laser-trapped atoms could help speed progress toward creating quantum computers. [20] Under certain conditions, an atom can cause other atoms to emit a flash of light. At TU Wien (Vienna), this quantum effect has now been measured. [19] A recent discovery by William & Mary and University of Michigan researchers transforms our understanding of one of the most important laws of modern physics. [18] Now, a team of physicists from The University of Queensland and the NÉEL Institute has shown that, as far as quantum physics is concerned, the chicken and the egg can both come first. [17] In 1993, physicist Lucien Hardy proposed an experiment showing that there is a small probability (around 6-9%) of observing a particle and its antiparticle interacting with each other without annihilating—something that is impossible in classical physics. [16] Scientists at the University of Geneva (UNIGE), Switzerland, recently reengineered their data processing, demonstrating that 16 million atoms were entangled in a one-centimetre crystal. [15]
Category: Artificial Intelligence

[12] viXra:1810.0245 [pdf] submitted on 2018-10-15 07:46:43

AI of Single Molecules in Cells

Authors: George Rajna
Comments: 42 Pages.

A research team centered at Osaka University, in collaboration with RIKEN, has developed a system that can overcome these difficulties by automatically searching for, focusing on, imaging, and tracking single molecules within living cells. [24] But researchers at Purdue University are working on a solution, combining quantum algorithms with classical computing on small-scale quantum computers to speed up database accessibility. [23] Researchers at the University of Twente, working with colleagues at the Technical Universities of Delft and Eindhoven, have successfully developed a new and interesting building block. [22] Researchers at the Institut d'Optique Graduate School at the CNRS and Université Paris-Saclay in France have used a laser-based technique to rearrange cold atoms one-by-one into fully ordered 3D patterns. [21] Reduced entropy in a three-dimensional lattice of super-cooled, laser-trapped atoms could help speed progress toward creating quantum computers. [20] Under certain conditions, an atom can cause other atoms to emit a flash of light. At TU Wien (Vienna), this quantum effect has now been measured. [19] A recent discovery by William & Mary and University of Michigan researchers transforms our understanding of one of the most important laws of modern physics. [18] Now, a team of physicists from The University of Queensland and the NÉEL Institute has shown that, as far as quantum physics is concerned, the chicken and the egg can both come first. [17] In 1993, physicist Lucien Hardy proposed an experiment showing that there is a small probability (around 6-9%) of observing a particle and its antiparticle interacting with each other without annihilating—something that is impossible in classical physics. [16]
Category: Artificial Intelligence

[11] viXra:1810.0243 [pdf] submitted on 2018-10-15 10:03:13

Analog Information AI System

Authors: George Rajna
Comments: 43 Pages.

A NIMS research group has invented an ionic device, termed an ionic decision-maker, capable of quickly making its own decisions based on previous experience using changes in ionic/molecular concentrations. [25] A research team centered at Osaka University, in collaboration with RIKEN, has developed a system that can overcome these difficulties by automatically searching for, focusing on, imaging, and tracking single molecules within living cells. [24] But researchers at Purdue University are working on a solution, combining quantum algorithms with classical computing on small-scale quantum computers to speed up database accessibility. [23] Researchers at the University of Twente, working with colleagues at the Technical Universities of Delft and Eindhoven, have successfully developed a new and interesting building block. [22] Researchers at the Institut d'Optique Graduate School at the CNRS and Université Paris-Saclay in France have used a laser-based technique to rearrange cold atoms one-by-one into fully ordered 3D patterns. [21] Reduced entropy in a three-dimensional lattice of super-cooled, laser-trapped atoms could help speed progress toward creating quantum computers. [20] Under certain conditions, an atom can cause other atoms to emit a flash of light. At TU Wien (Vienna), this quantum effect has now been measured. [19] A recent discovery by William & Mary and University of Michigan researchers transforms our understanding of one of the most important laws of modern physics. [18] Now, a team of physicists from The University of Queensland and the NÉEL Institute has shown that, as far as quantum physics is concerned, the chicken and the egg can both come first. [17]
Category: Artificial Intelligence

[10] viXra:1810.0139 [pdf] submitted on 2018-10-09 21:37:41

Supersymmetric Artificial Neural Network

Authors: Jordan Micah Bennett
Comments: 12 Pages. Author Email: jordanmicahbennett@gmail.com Author Website: folioverse.appspot.com

Babies are great examples of some non-trivial basis for artificial general intelligence; babies are significant examples of biological baseis that are reasonably usable to inspire smart algorithms. The “Supersymmetric Artificial Neural Network” in deep learning (denoted φ(x, θ, θ)⊤w), espouses the importance of considering biological constraints in the aim of developing general machine learning models, pertinently, where babies' brains are observed to be pre-equipped with particular "physics priors", constituting specifically, the ability for babies to intuitively know laws of physics, while learning by reinforcement. It is palpable that the phrasing “intuitively know laws of physics” above, should not be confused for nobel laureate or physics undergrad aligned babies that for example, write or understand physics papers/exams; instead, the aforesaid phrasing simply conveys that babies' brains are pre-baked with ways to naturally exercise physics based expectations w.r.t. interactions with objects in their world, as indicated by Aimee Stahl and Lisa Feigenson. Outstandingly, the importance of recognizing underlying causal physics laws in learning models (although not via supermanifolds, as encoded in the “Supersymmetric Artificial Neural Network”), has recently been both demonstrated and separately echoed by Deepmind (See “Neuroscience-Inspired Artificial Intelligence“) and of late, distinctly emphasized by Yoshua Bengio (See the “Consciousness Prior”). Physics based object detectors like "Uetorch" use something called pooling to gain translation invariance over objects, so that the model learns regardless of where the object in the image is positioned, while instead, reinforcement models like "AtariQLearner" exclude pooling, because "AtariQLearner" requires translation variance, in order for Q learning to apply on the changing positions of the objects in pixels. Babies seem to be able to do both these activities. That said, an example of models that can deliver both translation invariance and variance at the same time, i.e. disentangled factors of variation, are called manifold learning frameworks (Bengio et al. ...). Given that cognitive science may be used to constrain machine learning models (similar to how firms like Deepmind often use cognitive science as a boundary on the deep learning models they produce) The " Supersymmetric Artificial Neural Network” is a uniquely disentanglable model that is constrained by cognitive science, in the direction of supermanifolds (See “Supersymmetric methods ... at brain scale”, Perez et al.), instead of state of the art manifold work by other authors. (Such as manifold work by Bengio et al., Lecun et al. or Michael Bronstein et al.) As such, the "Supersymmetric Artificial Neural Network" is yet another way to represent richer values in the weights of the model; because supersymmetric values can allow for more information to be captured about the input space. For example, supersymmetric systems can capture potential-partner signals, which are beyond the feature space of magnitude and phase signals learnt in typical real valued neural nets and deep complex neural networks respectively. Looking at the progression of ‘solution geometries’; going from SO(n) representation (such as Perceptron like models) to SU(n) representation (such as UnitaryRNNs) has guaranteed richer and richer representations in weight space of the artificial neural network, and hence better and better hypotheses were generatable. The Supersymmetric Artificial Neural Network explores a natural step forward, namely SU(m|n) representation. These supersymmetric biological brain representations (Perez et al.) can be represented by supercharge compatible special unitary notation SU(m|n), or φ(x, θ, `θ)Tw parameterized by θ, `θ, which are supersymmetric directions, unlike θ seen in the typical non-supersymmetric deep learning model. Notably, Supersymmetric values can encode or represent more information than the typical deep learning model, in terms of “partner potential” signals for example.
Category: Artificial Intelligence

[9] viXra:1810.0110 [pdf] submitted on 2018-10-07 12:53:48

Machine Learning Heart Picture

Authors: George Rajna
Comments: 37 Pages.

To meet that demand, IBM researchers in Australia are using POWER9 systems, with Nvidia Tesla V100 graphics processing units (GPUs), to perform hemodynamic simulations for vFFR-based diagnosis within one to two minutes. [23] IBM researchers are developing a new computer architecture, better equipped to handle increased data loads from artificial intelligence. [22] A computer built to mimic the brain's neural networks produces similar results to that of the best brain-simulation supercomputer software currently used for neural-signaling research, finds a new study published in the open-access journal Frontiers in Neuroscience. [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]
Category: Artificial Intelligence

[8] viXra:1810.0100 [pdf] submitted on 2018-10-08 05:03:15

Cooperation for Vehicular Delay Tolerant Network

Authors: Adnan Muhammad
Comments: 8 Pages.

This article reviews the literature related to Vehicular Delay Tolerant Network with focus on Cooperation. It starts by examining definitions of some of the fields of research in VDTN. An overview of VDTN with cooperative networks is presented
Category: Artificial Intelligence

[7] viXra:1810.0097 [pdf] submitted on 2018-10-06 07:55:21

AI Person Under the Law

Authors: George Rajna
Comments: 38 Pages.

Granting human rights to a computer would degrade human dignity. [23] IBM researchers are developing a new computer architecture, better equipped to handle increased data loads from artificial intelligence. [22] A computer built to mimic the brain's neural networks produces similar results to that of the best brain-simulation supercomputer software currently used for neural-signaling research, finds a new study published in the open-access journal Frontiers in Neuroscience. [21]
Category: Artificial Intelligence

[6] viXra:1810.0094 [pdf] submitted on 2018-10-06 14:03:52

Regression in Wireless Sensor Networks

Authors: Muhammad Kashif Ghumman, Tauseef Jamal
Comments: 8 Pages. DCIS0710

In WSN, the main purpose of regression is to locate the nodes by prediction on the basis of readings. This article explains the concept of regression according to WSN perspective and on the basic of these concepts the clustering of nodes through multi-linear regression originates by combing the ideas of locating the nodes through regression and how to utilize nodes parameters in multilinear regression formula.
Category: Artificial Intelligence

[5] viXra:1810.0060 [pdf] submitted on 2018-10-06 04:55:16

Universal Forecasting Scheme-New

Authors: Ramesh Chandra Bagadi
Comments: 4 Pages.

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

[4] viXra:1810.0050 [pdf] submitted on 2018-10-04 15:08:20

Refutation of Another Neutrosophic Genetic Algorithm

Authors: Colin James III
Comments: 1 Page. © Copyright 2018 by Colin James III All rights reserved. Respond to the author by email at: info@ersatz-systems dot com.

The instant neutrosophic intelligent system based on genetic algorithm is not confirmed.
Category: Artificial Intelligence

[3] viXra:1810.0042 [pdf] submitted on 2018-10-03 07:06:39

A Novel Approach for Classify Manets Attacks with a Neutrosophic Intelligent System Based on Genetic Algorithm

Authors: Haitham Elwahsh, Mona Gamal, A. A. Salama, I. M. El-Henawy
Comments: 10 Pages.

Recently designing an effective intrusion detection systems (IDS) within Mobile Ad Hoc Networks Security (MANETs) becomes a requirement because of the amount of indeterminacy and doubt exist in that environment. Neutrosophic system is a discipline that makes a mathematical formulation for the indeterminacy found in such complex situations. Neutrosophic rules compute with symbols instead of numeric values making a good base for symbolic reasoning. These symbols should be carefully designed as they form the propositions base for the neutrosophic rules (NR) in the IDS. Each attack is determined by membership, nonmembership, and indeterminacy degrees in neutrosophic system. This research proposes a MANETs attack inference by a hybrid framework of Self-Organized Features Maps (SOFM) and the genetic algorithms (GA). The hybrid utilizes the unsupervised learning capabilities of the SOFM to define the MANETs neutrosophic conditional variables. The neutrosophic variables along with the training data set are fed into the genetic algorithm to find the most fit neutrosophic rule set from a number of initial subattacks according to the fitness function. This method is designed to detect unknown attacks in MANETs. The simulation and experimental results are conducted on the KDD-99 network attacks data available in the UCI machine-learning repository for further processing in knowledge discovery. The experiments cleared the feasibility of the proposed hybrid by an average accuracy of 99.3608 % which is more accurate than other IDS found in literature.
Category: Artificial Intelligence

[2] viXra:1810.0033 [pdf] submitted on 2018-10-04 04:39:53

Brain-Inspired AI Architecture

Authors: George Rajna
Comments: 36 Pages.

IBM researchers are developing a new computer architecture, better equipped to handle increased data loads from artificial intelligence. [22] A computer built to mimic the brain's neural networks produces similar results to that of the best brain-simulation supercomputer software currently used for neural-signaling research, finds a new study published in the open-access journal Frontiers in Neuroscience. [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]
Category: Artificial Intelligence

[1] viXra:1810.0013 [pdf] submitted on 2018-10-01 07:19:49

Machine Learning Helps Photonic Applications

Authors: George Rajna
Comments: 61 Pages.

Photonic nanostructures can be used for many applications besides solar cells—for example, optical sensors for cancer markers or other biomolecules. [36] Microelectromechanical systems (MEMS) have expansive applications in biotechnology and advanced engineering with growing interest in materials science and engineering due to their potential in emerging systems. [35] Researchers at Griffith University working with Australia's Commonwealth Scientific and Industrial Research Organisation (CSIRO) have unveiled a stunningly accurate technique for scientific measurements which uses a single atom as the sensor, with sensitivity down to 100 zeptoNewtons. [34] Researchers at the Center for Quantum Nanoscience within the Institute for Basic Science (IBS) have made a major breakthrough in controlling the quantum properties of single atoms. [33] A team of researchers from several institutions in Japan has described a physical system that can be described as existing above "absolute hot" and also below absolute zero. [32] A silicon-based quantum computing device could be closer than ever due to a new experimental device that demonstrates the potential to use light as a messenger to connect quantum bits of information—known as qubits—that are not immediately adjacent to each other. [31] Researchers at the University of Bristol's Quantum Engineering Technology Labs have demonstrated a new type of silicon chip that can help building and testing quantum computers and could find their way into your mobile phone to secure information. [30] Theoretical physicists propose to use negative interference to control heat flow in quantum devices. [29] Particle physicists are studying ways to harness the power of the quantum realm to further their research. [28]
Category: Artificial Intelligence