Artificial Intelligence

1805 Submissions

[16] 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

[15] 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

[14] 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

[13] 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

[12] 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

[11] 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

[10] 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

[9] 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

[8] 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

[7] 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

[6] 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

[5] 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

[4] 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

[3] 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

[2] 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

[1] 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