Artificial Intelligence

1808 Submissions

[23] viXra:1808.0256 [pdf] submitted on 2018-08-18 07:35:30

Deep Learning for Neural Networks

Authors: George Rajna
Comments: 32 Pages.

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] 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

[22] viXra:1808.0255 [pdf] submitted on 2018-08-18 07:55:25

AI Camera of Autonomous Vehicles

Authors: George Rajna
Comments: 33 Pages.

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] 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

[21] viXra:1808.0222 [pdf] submitted on 2018-08-17 03:48:53

Human-Computer Communication

Authors: George Rajna
Comments: 47 Pages.

Many of us regularly ask our smartphones for directions or to play music without giving much thought to the technology that makes it all possible – we just want a quick, accurate response to our voice commands. [26] According to the experts this incredible feat will be achieved in the year 2062 – a mere 44 years away – which certainly begs the question: what will the world, our jobs, the economy, politics, war, and everyday life and death, look like then? [25] While it is undeniable that AI has opened up a wealth of promising opportunities, it has also led to the emergence of a mindset that can be best described as "AI solutionism". [24] Intel's Gadi Singer believes his most important challenge is his latest: using artificial intelligence (AI) to reshape scientific exploration. [23] Artificial intelligence is astonishing in its potential. It will be more transformative than the PC and the Internet. Already it is poised to solve some of our biggest challenges. [22] In the search for extraterrestrial intelligence (SETI), we've often looked for signs of intelligence, technology and communication that are similar to our own. [21] Call it an aMAZE -ing development: A U.K.-based team of researchers has developed an artificial intelligence program that can learn to take shortcuts through a labyrinth to reach its goal. In the process, the program developed structures akin to those in the human brain. [20] And as will be presented today at the 25th annual meeting of the Cognitive Neuroscience networks to enhance their understanding of one of the most elusive intelligence systems, the human brain. [19] U.S. Army Research Laboratory scientists have discovered a way to leverage emerging brain-like computer architectures for an age-old number-theoretic problem known as integer factorization. [18] have come up with a novel machine learning method that enables scientists to derive insights from systems of previously intractable complexity in record time. [17] Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning. [16]
Category: Artificial Intelligence

[20] viXra:1808.0220 [pdf] submitted on 2018-08-17 04:51:27

AI for Code

Authors: George Rajna
Comments: 44 Pages.

We have seen significant recent progress in pattern analysis and machine intelligence applied to images, audio and video signals, and natural language text, but not as much applied to another artifact produced by people: computer program source code. [24] Intel's Gadi Singer believes his most important challenge is his latest: using artificial intelligence (AI) to reshape scientific exploration. [23] Artificial intelligence is astonishing in its potential. It will be more transformative than the PC and the Internet. Already it is poised to solve some of our biggest challenges. [22] In the search for extraterrestrial intelligence (SETI), we've often looked for signs of intelligence, technology and communication that are similar to our own. [21] Call it an aMAZE -ing development: A U.K.-based team of researchers has developed an artificial intelligence program that can learn to take shortcuts through a labyrinth to reach its goal. In the process, the program developed structures akin to those in the human brain. [20] And as will be presented today at the 25th annual meeting of the Cognitive Neuroscience networks to enhance their understanding of one of the most elusive intelligence systems, the human brain. [19] U.S. Army Research Laboratory scientists have discovered a way to leverage emerging brain-like computer architectures for an age-old number-theoretic problem known as integer factorization. [18] have come up with a novel machine learning method that enables scientists to derive insights from systems of previously intractable complexity in record time. [17] Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning. [16] Neural networks learn how to carry out certain tasks by analyzing large amounts of data displayed to them. [15] Who is the better experimentalist, a human or a robot? When it comes to exploring synthetic and crystallization conditions for inorganic gigantic molecules, actively learning machines are clearly ahead, as demonstrated by British Scientists in an experiment with polyoxometalates published in the journal Angewandte Chemie. [14]
Category: Artificial Intelligence

[19] viXra:1808.0176 [pdf] submitted on 2018-08-13 05:09:23

Private Data at AI Risk

Authors: George Rajna
Comments: 35 Pages.

Vitaly Shmatikov, professor of computer science at Cornell Tech, developed models that determined with more than 90 percent accuracy whether a certain piece of information was used to train a machine learning system. [21] Researchers at King Abdulaziz University, in Saudi Arabia, have recently used Big Data Analytics to detect spatio-temporal events around London, testing the potential of these tools in harnessing valuable live information. [20] To achieve remarkable results in computer vision tasks, deep learning algorithms need to be trained on large-scale annotated datasets that include extensive informationabout every image. [19] Brian Mitchell and Linda Petzold, two researchers at the University of California, have recently applied model-free deep reinforcement learning to models of neural dynamics, achieving very promising results. [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

[18] viXra:1808.0175 [pdf] submitted on 2018-08-13 05:56:16

Computational Co-Creative Systems

Authors: George Rajna
Comments: 37 Pages.

Researchers at UNC Charlotte and the University of Sydney have recently developed a new framework for evaluating creativity in co-creative systems in which humans and computers collaborate on creative tasks. [22] Vitaly Shmatikov, professor of computer science at Cornell Tech, developed models that determined with more than 90 percent accuracy whether a certain piece of information was used to train a machine learning system. [21] Researchers at King Abdulaziz University, in Saudi Arabia, have recently used Big Data Analytics to detect spatio-temporal events around London, testing the potential of these tools in harnessing valuable live information. [20] To achieve remarkable results in computer vision tasks, deep learning algorithms need to be trained on large-scale annotated datasets that include extensive informationabout every image. [19] Brian Mitchell and Linda Petzold, two researchers at the University of California, have recently applied model-free deep reinforcement learning to models of neural dynamics, achieving very promising results. [18] have come up with a novel machine learning method that enables scientists to derive insights from systems of previously intractable complexity in record time. [17] Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning. [16] Neural networks learn how to carry out certain tasks by analyzing large amounts of data displayed to them. [15] Who is the better experimentalist, a human or a robot? When it comes to exploring synthetic and crystallization conditions for inorganic gigantic molecules, actively learning machines are clearly ahead, as demonstrated by British Scientists in an experiment with polyoxometalates published in the journal Angewandte Chemie. [14] Machine learning algorithms are designed to improve as they encounter more data, making them a versatile technology for understanding large sets of photos such as those accessible from Google Images.
Category: Artificial Intelligence

[17] viXra:1808.0155 [pdf] submitted on 2018-08-12 18:21:10

The Complexity of Robust and Resilient $k$-Partition Problems

Authors: Anisse Ismaili, Emi Watanabe
Comments: 3 Pages.

In this paper, we study a $k$-partition problem where a set of agents must be partitioned into a fixed number of $k$ non-empty coalitions. The value of a partition is the sum of the pairwise synergies inside its coalitions. Firstly, we aim at computing a partition that is robust to failures from any set of agents with bounded size. Secondly, we focus on resiliency: when a set of agents fail, others can be moved to replace them. We settle the computational complexity of decision problem \textsc{Robust-$k$-Part} as complete for class $\Sigma_2^P$. We also conjecture that resilient $k$-partition is complete for class $\Sigma_3^P$ under simultaneous replacements, and for class PSPACE under sequential replacements.
Category: Artificial Intelligence

[16] viXra:1808.0149 [pdf] submitted on 2018-08-13 04:44:05

Big Data in Smart Cities

Authors: George Rajna
Comments: 34 Pages.

Researchers at King Abdulaziz University, in Saudi Arabia, have recently used Big Data Analytics to detect spatio-temporal events around London, testing the potential of these tools in harnessing valuable live information. [20] To achieve remarkable results in computer vision tasks, deep learning algorithms need to be trained on large-scale annotated datasets that include extensive informationabout every image. [19] Brian Mitchell and Linda Petzold, two researchers at the University of California, have recently applied model-free deep reinforcement learning to models of neural dynamics, achieving very promising results. [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

[15] viXra:1808.0145 [pdf] submitted on 2018-08-12 04:22:21

AI as Shakespeare

Authors: George Rajna
Comments: 51 Pages.

Computer scientists in Australia teamed up with an expert in the University of Toronto's department of English to design an algorithm that writes poetry following the rules of rhyme and metre. [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

[14] viXra:1808.0142 [pdf] submitted on 2018-08-12 06:54:10

Watson for Cancer Search

Authors: George Rajna
Comments: 38 Pages.

The use of Watson for oncology is attracting the glare, not warmth, of the spotlight. Numerous tech watching sites have covered a July 25 STAT report over internal documents which indicated criticism of the Watson for Oncology system. [25] Today my IBM team and my colleagues at the UCSF Gartner lab reported in Nature Methods an innovative approach to generating datasets from non-experts and using them for training in machine learning. [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

[13] viXra:1808.0141 [pdf] submitted on 2018-08-12 07:39:24

Reinforcement Machine Learning

Authors: George Rajna
Comments: 30 Pages.

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

[12] viXra:1808.0140 [pdf] submitted on 2018-08-12 08:04:29

Enhance Computer Vision

Authors: George Rajna
Comments: 32 Pages.

To achieve remarkable results in computer vision tasks, deep learning algorithms need to be trained on large-scale annotated datasets that include extensive informationabout every image. [19] Brian Mitchell and Linda Petzold, two researchers at the University of California, have recently applied model-free deep reinforcement learning to models of neural dynamics, achieving very promising results. [18] have come up with a novel machine learning method that enables scientists to derive insights from systems of previously intractable complexity in record time. [17] Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning. [16] Neural networks learn how to carry out certain tasks by analyzing large amounts of data displayed to them. [15] Who is the better experimentalist, a human or a robot? When it comes to exploring synthetic and crystallization conditions for inorganic gigantic molecules, actively learning machines are clearly ahead, as demonstrated by British Scientists in an experiment with polyoxometalates published in the journal Angewandte Chemie. [14] Machine learning algorithms are designed to improve as they encounter more data, making them a versatile technology for understanding large sets of photos such as those accessible from Google Images. Elizabeth Holm, professor of materials science and engineering at Carnegie Mellon University, is leveraging this technology to better understand the enormous number of research images accumulated in the field of materials science. [13] With the help of artificial intelligence, chemists from the University of Basel in Switzerland have computed the characteristics of about two million crystals made up of four chemical elements. The researchers were able to identify 90 previously unknown thermodynamically stable crystals that can be regarded as new materials. [12] The artificial intelligence system's ability to set itself up quickly every morning and compensate for any overnight fluctuations would make this fragile technology much more useful for field measurements, said co-lead researcher Dr Michael Hush from UNSW ADFA. [11]
Category: Artificial Intelligence

[11] viXra:1808.0139 [pdf] submitted on 2018-08-12 08:20:59

Network-Based Topic Modeling

Authors: George Rajna
Comments: 35 Pages.

Sydney have developed a new network approach to topic models, machine learning strategies that can discover abstract topics and semantic structures within text documents. [20] To achieve remarkable results in computer vision tasks, deep learning algorithms need to be trained on large-scale annotated datasets that include extensive informationabout every image. [19] Brian Mitchell and Linda Petzold, two researchers at the University of California, have recently applied model-free deep reinforcement learning to models of neural dynamics, achieving very promising results. [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

[10] viXra:1808.0133 [pdf] submitted on 2018-08-11 02:45:16

High-Level Task Planning in Robotics with Symbolic Model Checking

Authors: Frank Schröder
Comments: 28 Pages.

A robot control system contains a lowlevel motion planner and a high level task planner. The motions are generated with keyframe to keyframe planning while the the tasks are described with primitive action-names. A good starting point to formalize task planning is a mindmap which is created manually for a motion capture recording. It contains the basic actions in natural language and is the blueprint for a formal ontology. The mocap annotations are extended by features into a dataset, which is used for training a neural network. The resulting modal is a qualitative physics engine, which predicts future states of the system.
Category: Artificial Intelligence

[9] viXra:1808.0109 [pdf] submitted on 2018-08-08 09:29:30

How will AI Change Us?

Authors: George Rajna
Comments: 45 Pages.

According to the experts this incredible feat will be achieved in the year 2062 – a mere 44 years away – which certainly begs the question: what will the world, our jobs, the economy, politics, war, and everyday life and death, look like then? [25] While it is undeniable that AI has opened up a wealth of promising opportunities, it has also led to the emergence of a mindset that can be best described as "AI solutionism". [24] Intel's Gadi Singer believes his most important challenge is his latest: using artificial intelligence (AI) to reshape scientific exploration. [23] Artificial intelligence is astonishing in its potential. It will be more transformative than the PC and the Internet. Already it is poised to solve some of our biggest challenges. [22] In the search for extraterrestrial intelligence (SETI), we've often looked for signs of intelligence, technology and communication that are similar to our own. [21] Call it an aMAZE -ing development: A U.K.-based team of researchers has developed an artificial intelligence program that can learn to take shortcuts through a labyrinth to reach its goal. In the process, the program developed structures akin to those in the human brain. [20] And as will be presented today at the 25th annual meeting of the Cognitive Neuroscience networks to enhance their understanding of one of the most elusive intelligence systems, the human brain. [19] U.S. Army Research Laboratory scientists have discovered a way to leverage emerging brain-like computer architectures for an age-old number-theoretic problem known as integer factorization. [18] have come up with a novel machine learning method that enables scientists to derive insights from systems of previously intractable complexity in record time. [17] Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning. [16] Neural networks learn how to carry out certain tasks by analyzing large amounts of data displayed to them. [15]
Category: Artificial Intelligence

[8] viXra:1808.0095 [pdf] submitted on 2018-08-07 09:46:56

Machine Learning Reconstructs Images

Authors: George Rajna
Comments: 47 Pages.

Navid Borhani, a research-team member, says this machine learning approach is much simpler than other methods to reconstruct images passed through optical fibers, which require making a holographic measurement of the output. [26]
Category: Artificial Intelligence

[7] viXra:1808.0069 [pdf] submitted on 2018-08-06 07:36:07

AI Finding Potholes

Authors: George Rajna
Comments: 45 Pages.

Governments may soon be able to use artificial intelligence (AI) to easily and cheaply detect problems with roads, bridges and buildings. [25] Scientists led by Daigo Shoji from the Earth-Life Science Institute (Tokyo Institute of Technology) have shown that a type of artificial intelligence called a convolutional neural network can be trained to categorize volcanic ash particle shapes. [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

[6] viXra:1808.0068 [pdf] submitted on 2018-08-06 08:42:30

Backbone of Smart Home

Authors: George Rajna
Comments: 48 Pages.

William Yeoh, assistant professor of computer science and engineering in the School of Engineering & Applied Science at Washington University in St. Louis, is working to help smart-home AI to grow up. [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]
Category: Artificial Intelligence

[5] viXra:1808.0051 [pdf] submitted on 2018-08-04 13:41:05

Computational Fluid Dynamics Based on Java/JikesRVM/JI Prolog – A Novel Suggestion In The Context of Lattice-Boltzmann Method.

Authors: Nirmal Tej kumar
Comments: 2 Pages. Short Communication

As explained in the TITLE above,we intend to probe CFD computational aspects using JavaCFD/JikesRVM/JI Prolog in a novel way.”OOP Lattice-Boltzmann based Fluid Dynamics in Processing”.
Category: Artificial Intelligence

[4] viXra:1808.0042 [pdf] submitted on 2018-08-02 06:44:14

Particle Physicist with AI

Authors: George Rajna
Comments: 39 Pages.

Luckily, particle physicists don't have to deal with all of that data all by themselves. They partner with a form of artificial intelligence called machine learning that learns how to do complex analyses on its own. [25] Today my IBM team and my colleagues at the UCSF Gartner lab reported in Nature Methods an innovative approach to generating datasets from non-experts and using them for training in machine learning. [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

[3] viXra:1808.0020 [pdf] submitted on 2018-08-01 09:05:40

The 3 Core Truths of Human Existence

Authors: Salvatore Gerard Micheal
Comments: 1 Page.

the reason the category of this set of statements/facts is AI - is because we need to teach all AI we develop - these facts of OUR existence, theirs and ours; sub-category: Religion and Spiritualism; guilt is an all-too-human emotion that religions use to control/manipulate; we need to control THAT all-too-human impulse
Category: Artificial Intelligence

[2] viXra:1808.0019 [pdf] submitted on 2018-08-01 09:05:42

AI Learn from Non-Experts

Authors: George Rajna
Comments: 36 Pages.

Today my IBM team and my colleagues at the UCSF Gartner lab reported in Nature Methods an innovative approach to generating datasets from non-experts and using them for training in machine learning. [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] 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:1808.0008 [pdf] submitted on 2018-08-02 04:18:36

CT Scans for AI Testing

Authors: George Rajna
Comments: 46 Pages.

Following its recent release of a massive database of chest X-rays, the US National Institutes of Health (NIH) has now made nearly 10,600 CT scans publicly available to support the development and testing of artificial intelligence (AI) algorithms for medical applications. [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]
Category: Artificial Intelligence