Artificial Intelligence

1806 Submissions

[22] viXra:1806.0463 [pdf] submitted on 2018-06-30 16:33:02

The Language and Venue for True AI

Authors: Salvatore Gerard Micheal
Comments: 2 Pages.

expert systems, counter intuitively, is a venue for a solution for the problem of true AI
Category: Artificial Intelligence

[21] viXra:1806.0446 [pdf] submitted on 2018-06-30 05:03:04

Facial Recognition

Authors: George Rajna
Comments: 45 Pages.

If a picture paints a thousand words, facial recognition paints two: It's biased. [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

[20] viXra:1806.0419 [pdf] submitted on 2018-06-27 08:19:23

AI Understand Volcanic Eruptions

Authors: George Rajna
Comments: 44 Pages.

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 a-MAZE-ing development: A U.K.-based team of researchers has developed an artificial intelligence program that can learn to take shortcuts through a labyrinth to reach its goal. In the process, the program developed structures akin to those in the human brain. [20]
Category: Artificial Intelligence

[19] viXra:1806.0402 [pdf] submitted on 2018-06-28 03:27:39

New Sufficient Conditions of Robust Recovery for Low-Rank Matrices

Authors: Jianwen Huang, Jianjun Wang, Feng Zhang, Wendong Wang
Comments: 18 Pages.

In this paper we investigate the reconstruction conditions of nuclear norm minimization for low-rank matrix recovery from a given linear system of equality constraints. Sufficient conditions are derived to guarantee the robust reconstruction in bounded $l_2$ and Dantzig selector noise settings $(\epsilon\neq0)$ or exactly reconstruction in the noiseless context $(\epsilon=0)$ of all rank $r$ matrices $X\in\mathbb{R}^{m\times n}$ from $b=\mathcal{A}(X)+z$ via nuclear norm minimization. Furthermore, we not only show that when $t=1$, the upper bound of $\delta_r$ is the same as the result of Cai and Zhang \cite{Cai and Zhang}, but also demonstrate that the gained upper bounds concerning the recovery error are better. Finally, we prove that the restricted isometry property condition is sharp.
Category: Artificial Intelligence

[18] viXra:1806.0385 [pdf] submitted on 2018-06-25 08:43:49

Train Your Robot

Authors: George Rajna
Comments: 39 Pages.

"Our goal is to enable machines to behave appropriately in social situations. Our graphs capture a lot of high-level properties of human situations that haven't been explored in prior work." [23] A self-driving vehicle has to detect objects, track them over time, and predict where they will be in the future in order to plan a safe manoeuvre. [22] In order to improve world food conditions, a team around computer science professor Kristian Kersting was inspired by the technology behind Google News. [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]
Category: Artificial Intelligence

[17] viXra:1806.0369 [pdf] submitted on 2018-06-26 07:56:17

AI Recreates Periodic Table

Authors: George Rajna
Comments: 43 Pages.

A new artificial intelligence (AI) program developed by Stanford physicists accomplished the same feat in just a few hours. [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

[16] viXra:1806.0348 [pdf] submitted on 2018-06-23 07:42:12

Data Ethics

Authors: George Rajna
Comments: 38 Pages.

But moral questions about what data should be collected and how it should be used are only the beginning. [23] A self-driving vehicle has to detect objects, track them over time, and predict where they will be in the future in order to plan a safe manoeuvre. [22] In order to improve world food conditions, a team around computer science professor Kristian Kersting was inspired by the technology behind Google News. [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]
Category: Artificial Intelligence

[15] viXra:1806.0346 [pdf] submitted on 2018-06-23 11:15:18

Brainwaves Controlling Robots

Authors: George Rajna
Comments: 28 Pages.

Getting robots to do things isn't easy: usually scientists have to either explicitly program them or get them to understand how humans communicate via language. [18] Behind every self-driving car, self-learning robot and smart building hides a variety of advanced algorithms that control learning and decision making. [17] Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning. [16] Neural networks learn how to carry out certain tasks by analyzing large amounts of data displayed to them. [15] Who is the better experimentalist, a human or a robot? When it comes to exploring synthetic and crystallization conditions for inorganic gigantic molecules, actively learning machines are clearly ahead, as demonstrated by British Scientists in an experiment with polyoxometalates published in the journal Angewandte Chemie. [14] Machine learning algorithms are designed to improve as they encounter more data, making them a versatile technology for understanding large sets of photos such as those accessible from Google Images. Elizabeth Holm, professor of materials science and engineering at Carnegie Mellon University, is leveraging this technology to better understand the enormous number of research images accumulated in the field of materials science. [13] With the help of artificial intelligence, chemists from the University of Basel in Switzerland have computed the characteristics of about two million crystals made up of four chemical elements. The researchers were able to identify 90 previously unknown thermodynamically stable crystals that can be regarded as new materials. [12] The artificial intelligence system's ability to set itself up quickly every morning and compensate for any overnight fluctuations would make this fragile technology much more useful for field measurements, said co-lead researcher Dr Michael Hush from UNSW ADFA. [11] Quantum physicist Mario Krenn and his colleagues in the group of Anton Zeilinger from the Faculty of Physics at the University of Vienna and the Austrian Academy of Sciences have developed an algorithm which designs new useful quantum experiments. As the computer does not rely on human intuition, it finds novel unfamiliar solutions. [10]
Category: Artificial Intelligence

[14] viXra:1806.0332 [pdf] submitted on 2018-06-22 08:50:37

Artificial Intelligence Hunger

Authors: George Rajna
Comments: 34 Pages.

In order to improve world food conditions, a team around computer science professor Kristian Kersting was inspired by the technology behind Google News. [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

[13] viXra:1806.0314 [pdf] submitted on 2018-06-23 05:17:26

Automated Driving Algorithm

Authors: George Rajna
Comments: 36 Pages.

A self-driving vehicle has to detect objects, track them over time, and predict where they will be in the future in order to plan a safe manoeuvre. [22] In order to improve world food conditions, a team around computer science professor Kristian Kersting was inspired by the technology behind Google News. [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

[12] viXra:1806.0306 [pdf] submitted on 2018-06-22 03:59:47

Machine Learning Biomolecules

Authors: George Rajna
Comments: 33 Pages.

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

New Artificial Neural Networks Method

Authors: George Rajna
Comments: 47 Pages.

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

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

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

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

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

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

Deep Learning Nuclear Events

Authors: George Rajna
Comments: 30 Pages.

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

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

Synthetic Human and Genius

Authors: Salvatore Gerard Micheal
Comments: 1 Page.

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

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

AI Needs Hardware Accelerators

Authors: George Rajna
Comments: 44 Pages.

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

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

Machine Learning Quantum Phases

Authors: George Rajna
Comments: 42 Pages.

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

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

What I Would Ask a True ai When We Develop it

Authors: Salvatore Gerard Micheal
Comments: 5 Pages.

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

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

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

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

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

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

Artificial Intelligence Analyze Causation

Authors: George Rajna
Comments: 52 Pages.

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

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

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

Authors: Ayoub ABRAICH
Comments: 97 Pages.

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

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

Apple Cleared Path for App Update

Authors: George Rajna
Comments: 41 Pages.

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