Artificial Intelligence

1809 Submissions

[19] viXra:1809.0535 [pdf] submitted on 2018-09-27 02:00:51

Procrastinative Reinforcement Learning

Authors: Joy Chopra, Sandipan Haldar
Comments: 1 Page.

We propose using procrastination to prepare the agent for emergency situations and to enable it to learn to finish work in shorter horizons. This can be done by regulating the discount factor or by making the agent explore for most of the episode, and taking exploitationary actions near the end. We will finish the rest of this paper very soon.
Category: Artificial Intelligence

[18] viXra:1809.0534 [pdf] submitted on 2018-09-27 02:08:06

Rebellious Reinforcement Learning

Authors: Joy Chopra
Comments: 1 Page.

Actor critic methods have shown good performance in reinforcement learning domain. We propose using rebellious policy i.e. taking action with minimum Q value to enhance exploration and let the critic understand why other actions are good or may be not?! We will now propose the rest of the paper. NO we will not.
Category: Artificial Intelligence

[17] viXra:1809.0510 [pdf] submitted on 2018-09-24 09:05:05

Ai Create 100,000 New Tunes

Authors: George Rajna
Comments: 46 Pages.

"It will be interesting to see if this collection is used to train future generations of computer models," Sturm says. [27] Now, a team of A*STAR researchers and colleagues has developed a detector that can successfully pick out where human actions will occur in videos, in almost real-time. [26] A team of researchers affiliated with several institutions in Germany and the U.S. has developed a deep learning algorithm that can be used for motion capture of animals of any kind. [25] In 2016, when we inaugurated our new IBM Research lab in Johannesburg, we took on this challenge and are reporting our first promising results at Health Day at the KDD Data Science Conference in London this month. [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]
Category: Artificial Intelligence

[16] viXra:1809.0507 [pdf] submitted on 2018-09-24 10:09:22

Chip Up AI Performance

Authors: George Rajna
Comments: 48 Pages.

Princeton researchers, in collaboration with Analog Devices Inc., have fabricated a chip that markedly boosts the performance and efficiency of neural networks—computer algorithms modeled on the workings of the human brain. [28] "It will be interesting to see if this collection is used to train future generations of computer models," Sturm says. [27] Now, a team of A*STAR researchers and colleagues has developed a detector that can successfully pick out where human actions will occur in videos, in almost real-time. [26] A team of researchers affiliated with several institutions in Germany and the U.S. has developed a deep learning algorithm that can be used for motion capture of animals of any kind. [25] In 2016, when we inaugurated our new IBM Research lab in Johannesburg, we took on this challenge and are reporting our first promising results at Health Day at the KDD Data Science Conference in London this month. [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]
Category: Artificial Intelligence

[15] viXra:1809.0506 [pdf] submitted on 2018-09-24 10:28:49

Sensor Surface on Robot Skin

Authors: George Rajna
Comments: 36 Pages.

Robots will be able to conduct a wide variety of tasks as well as humans if they can be given tactile sensing capabilities. [25] A new type of artificial-intelligence-driven chemistry could r evolutionise the way molecules are discovered, scientists claim. [24] Tired of writing your own boring code for new software? Finally, there's an AI that can do it for you. [23] Welcome to Move Mirror, where you move in front of your webcam. [22] Understanding how a robot will react under different conditions is essential to guaranteeing its safe operation. [21] 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] 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

[14] viXra:1809.0499 [pdf] submitted on 2018-09-25 04:53:00

AI Improve Drug Combination

Authors: George Rajna
Comments: 44 Pages.

A new auto-commentary published in SLAS Technology looks at how an emerging area of artificial intelligence, specifically the analysis of small systems-of-interest specific datasets, can be used to improve drug development and personalized medicine. [25] And if that isn't surprising enough, try this one: in many cases, doctors have no idea what side effects might arise from adding another drug to a patient's personal pharmacy. [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]
Category: Artificial Intelligence

[13] viXra:1809.0473 [pdf] submitted on 2018-09-22 08:31:02

Neural Networks Identify Neutrinoless Double Beta Decay

Authors: George Rajna
Comments: 85 Pages.

The work will help to improve the sensitivity of detection for the PandaX-III neutrinoless double beta decay experiment, and deepen our knowledge of the nature of neutrinos. [45] The interactions of quarks and gluons are computed using lattice quantum chromodynamics (QCD)—a computer-friendly version of the mathematical framework that describes these strong-force interactions. [44] The building blocks of matter in our universe were formed in the first 10 microseconds of its existence, according to the currently accepted scientific picture. [43] In a recent experiment at the University of Nebraska–Lincoln, plasma electrons in the paths of intense laser light pulses were almost instantly accelerated close to the speed of light. [42] Plasma particle accelerators more powerful than existing machines could help probe some of the outstanding mysteries of our universe, as well as make leaps forward in cancer treatment and security scanning—all in a package that's around a thousandth of the size of current accelerators. [41] The Department of Energy's SLAC National Accelerator Laboratory has started to assemble a new facility for revolutionary accelerator technologies that could make future accelerators 100 to 1,000 times smaller and boost their capabilities. [40]
Category: Artificial Intelligence

[12] viXra:1809.0437 [pdf] submitted on 2018-09-19 10:43:44

Image Analysis with Deep Learning

Authors: George Rajna
Comments: 47 Pages.

IBM researchers are applying deep learning to discover ways to overcome some of the technical challenges that AI can face when analyzing X-rays and other medical images. [27] Now, a team of A*STAR researchers and colleagues has developed a detector that can successfully pick out where human actions will occur in videos, in almost real-time. [26] A team of researchers affiliated with several institutions in Germany and the U.S. has developed a deep learning algorithm that can be used for motion capture of animals of any kind. [25] In 2016, when we inaugurated our new IBM Research lab in Johannesburg, we took on this challenge and are reporting our first promising results at Health Day at the KDD Data Science Conference in London this month. [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]
Category: Artificial Intelligence

[11] viXra:1809.0364 [pdf] replaced on 2018-09-28 10:07:11

Idealistic Neural Networks

Authors: Tofara Moyo
Comments: 2 Pages.

I describe an Artificial Neural Network, where we have mapped words to individual neurons instead of having them as variables to be fed into a network. The process of changing training cases will be equivalent to a Dropout procedure where we replace some (or all) of the words/neurons in the previous training case with new ones. Each neuron/word then takes in as input, all the b weights of the other neurons, and weights them all with its personal a weight. To learn this network uses the backpropagation algorithm after calculating an error from the output of an output neuron that will be a traditional neuron. This network then has a unique topology and functions with no inputs. We will use coordinate gradient decent to learn where we alternate between training the a weights of the words and the b weights. The Idealistic Neural Network, is an extremely shallow network that can represent non-linearity complexity in a linear outfit.
Category: Artificial Intelligence

[10] viXra:1809.0357 [pdf] submitted on 2018-09-17 12:53:02

Machine Learning Human Cell

Authors: George Rajna
Comments: 34 Pages.

Scientists at the Allen Institute have used machine learning to train computers to see parts of the cell the human eye cannot easily distinguish. [21] Small angle X-ray scattering (SAXS) is one of a number of biophysical techniques used for determining the structural characteristics of biomolecules. [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] 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

[9] viXra:1809.0354 [pdf] submitted on 2018-09-17 13:17:17

AI can Tell if Restaurant Review Fake

Authors: George Rajna
Comments: 47 Pages.

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] 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

[8] viXra:1809.0258 [pdf] submitted on 2018-09-12 10:29:29

AI-Based Robots and Drones

Authors: George Rajna
Comments: 32 Pages.

What if a parent could feel safe allowing a drone to walk their child to the bus stop? [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]
Category: Artificial Intelligence

[7] viXra:1809.0242 [pdf] submitted on 2018-09-11 12:59:41

Deep-See Images with AI

Authors: George Rajna
Comments: 46 Pages.

The evaluation of very large amounts of data is becoming increasingly relevant in ocean research. [27] An LMU study now shows that new algorithms allow interactions in the atmosphere to be modeled more rapidly without loss of reliability. [26] Progress on new artificial intelligence (AI) technology could make monitoring at water treatment plants cheaper and easier and help safeguard public health. [25] And if that isn't surprising enough, try this one: in many cases, doctors have no idea what side effects might arise from adding another drug to a patient's personal pharmacy. [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]
Category: Artificial Intelligence

[6] viXra:1809.0212 [pdf] submitted on 2018-09-10 09:58:43

AI Climate Computation

Authors: George Rajna
Comments: 45 Pages.

An LMU study now shows that new algorithms allow interactions in the atmosphere to be modeled more rapidly without loss of reliability. [26] Progress on new artificial intelligence (AI) technology could make monitoring at water treatment plants cheaper and easier and help safeguard public health. [25] And if that isn't surprising enough, try this one: in many cases, doctors have no idea what side effects might arise from adding another drug to a patient's personal pharmacy. [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]
Category: Artificial Intelligence

[5] viXra:1809.0190 [pdf] replaced on 2018-09-11 13:51:22

Thoughts About Thinking

Authors: Lev I. Verkhovsky
Comments: 12 Pages. The article in Russian

A geometric model illustrating the basic mechanisms of thinking -- logical and intuitive -- is proposed. The thinking of man and the problems of creating artificial intelligence are discussed. Although the article was published in the Russian popular science journal «Chemistry and Life» in 1989 No. 7, according to the author, it is not obsolete. In Russian.
Category: Artificial Intelligence

[4] viXra:1809.0136 [pdf] submitted on 2018-09-06 06:57:56

Machine Learning Material Spectra

Authors: George Rajna
Comments: 41 Pages.

Use of big data analysis techniques has been attracting attention in materials science applications, and researchers at The University of Tokyo Institute of Industrial Science realized that such techniques could be used to interpret much larger numbers of spectra than traditional approaches. [25] Researchers have mathematically proven that a powerful classical machine learning algorithm should work on quantum computers. [24] Researchers at Oregon State University have used deep learning to decipher which ribonucleic acids have the potential to encode proteins. [23] A new method allows researchers to systematically identify specialized proteins that unpack DNA inside the nucleus of a cell, making the usually dense DNA more accessible for gene expression and other functions. [22] Bacterial systems are some of the simplest and most effective platforms for the expression of recombinant proteins. [21] Now, in a new paper published in Nature Structural & Molecular Biology, Mayo researchers have determined how one DNA repair protein gets to the site of DNA damage. [20] A microscopic thread of DNA evidence in a public genealogy database led California authorities to declare this spring they had caught the Golden State Killer, the rapist and murderer who had eluded authorities for decades. [19] Researchers at Delft University of Technology, in collaboration with colleagues at the Autonomous University of Madrid, have created an artificial DNA blueprint for the replication of DNA in a cell-like structure. [18] An LMU team now reveals the inner workings of a molecular motor made of proteins which packs and unpacks DNA. [17] Chemist Ivan Huc finds the inspiration for his work in the molecular principles that underlie biological systems. [16] What makes particles self-assemble into complex biological structures? [15]
Category: Artificial Intelligence

[3] viXra:1809.0101 [pdf] submitted on 2018-09-06 03:14:06

Machine Learning Predicts Metabolism

Authors: George Rajna
Comments: 33 Pages.

Machine learning algorithms that can predict yeast metabolism from its protein content have been developed by scientists at the Francis Crick Institute. [21] 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] 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

[2] viXra:1809.0033 [pdf] submitted on 2018-09-03 06:34:04

A Novel Representation Of A Natural Number, A Set Of Natural Numbers And One Step Growth Of Any Natural Number Represented By Primality Trees (Version 2)

Authors: Ramesh Chandra Bagadi
Comments: 2 Pages.

In this research investigation the author has presented a novel representation of any natural number as a Primality Tree. Also, the author has presented a novel representation of a given set of any natural numbers. Furthermore, finally, the author has presented the novel representation of One step growth of any number and also any set of natural numbers as a Primality Tree.
Category: Artificial Intelligence

[1] viXra:1809.0007 [pdf] submitted on 2018-09-01 03:59:47

AI Meets Your Shopping Experience

Authors: George Rajna
Comments: 47 Pages.

This shift from reactive to predictive marketing could change the way you shop, bringing you suggestions you perhaps never even considered, all possible because of AI-related opportunities for both retailers and their customers. [27] Now, a team of A*STAR researchers and colleagues has developed a detector that can successfully pick out where human actions will occur in videos, in almost real-time. [26] A team of researchers affiliated with several institutions in Germany and the U.S. has developed a deep learning algorithm that can be used for motion capture of animals of any kind. [25] In 2016, when we inaugurated our new IBM Research lab in Johannesburg, we took on this challenge and are reporting our first promising results at Health Day at the KDD Data Science Conference in London this month. [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]
Category: Artificial Intelligence