Artificial Intelligence

1803 Submissions

[16] viXra:1803.0751 [pdf] submitted on 2018-03-31 04:17:45

Galenism: A Methodology for the Key Unification of Von Neumann Machines and Hierarchical Databases

Authors: Pallabi Chakraborty, Bhargav Bhushanam
Comments: 7 Pages.

The implications of psychoacoustic methodologies have been far-reaching and pervasive. In this work, we disprove the simulation of active networks. We examine how fiber-optic cables can be applied to the evaluation of DNS.
Category: Artificial Intelligence

[15] viXra:1803.0728 [pdf] submitted on 2018-03-30 06:52:39

Artificial Intelligence in Chemical Synthesis

Authors: George Rajna
Comments: 29 Pages.

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] 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]
Category: Artificial Intelligence

[14] viXra:1803.0699 [pdf] submitted on 2018-03-29 01:48:31

Universal Forecasting Scheme {Version 4}

Authors: Ramesh Chandra Bagadi
Comments: 3 Pages.

In this research investigation, the author has presented a Novel Method of Forecasting.
Category: Artificial Intelligence

[13] viXra:1803.0696 [pdf] submitted on 2018-03-29 06:19:03

Teaching Machine in Physical Systems

Authors: George Rajna
Comments: 27 Pages.

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] 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]
Category: Artificial Intelligence

[12] viXra:1803.0695 [pdf] submitted on 2018-03-28 04:26:14

Brain's Potential for Quantum Computation

Authors: George Rajna
Comments: 32 Pages.

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] 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]
Category: Artificial Intelligence

[11] viXra:1803.0675 [pdf] submitted on 2018-03-26 06:58:13

A Survey on Reasoning on Building Information Models Based on IFC

Authors: Hassan Sleiman
Comments: 17 Pages.

Building Information Models (BIM) are computer models that act as a main source of building information and integrate several aspects of engineering and architectural design, including building utilisation. They aim at enhancing the efficiency and the effectiveness of the projects during design, construction, and maintenance. Artificial Intelligence, which is used to automate tasks that would require intelligence, has found its way into BIM by applying reasoners, among other techniques. A reasoner is a piece of software that makes the implicit and hidden knowledge as explicit by using logical inferring techniques. Reasoners are applied on BIM to help take enhanced decisions and to assess the construction projects. The importance of BIM in both construction and information technology sectors has motivated many researchers to work on surveys that attempt to provide the current state of BIM, but unfortunately, none of these surveys has focused on reasoning on BIM. In this article we survey the research proposals and toolkits that rely on using reasoning systems on BIM, and we classify them into a two-level schema based on what they are intended for. According to our survey, reasoning is mainly used for solving design problems, and is especially applied for code consistency checking, with an emphasis on the semantic web technologies. Furthermore, user-friendliness is still a gap in this field and case-based reasoning, which was often applied in the past efforts, is still hardly applied for reasoning on BIM. The survey shows that this research area is active and that the research results are progressively being integrated into commercial toolkits.
Category: Artificial Intelligence

[10] viXra:1803.0652 [pdf] submitted on 2018-03-26 00:17:46

AI to Understand Human Brain

Authors: George Rajna
Comments: 30 Pages.

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] 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

[9] viXra:1803.0627 [pdf] submitted on 2018-03-24 08:48:04

Brain-Like Computers

Authors: George Rajna
Comments: 29 Pages.

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] 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

[8] viXra:1803.0089 [pdf] submitted on 2018-03-07 02:50:41

Universal Forecasting Scheme : Two Methods {Version 1}

Authors: Ramesh Chandra Bagadi
Comments: 3 Pages.

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

[7] viXra:1803.0083 [pdf] submitted on 2018-03-06 11:49:00

Machine Learning Guide Science

Authors: George Rajna
Comments: 25 Pages.

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] 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] Researchers at the University of Chicago's Institute for Molecular Engineering and the University of Konstanz have demonstrated the ability to generate a quantum logic operation, or rotation of the qubit, that - surprisingly—is intrinsically resilient to noise as well as to variations in the strength or duration of the control. Their achievement is based on a geometric concept known as the Berry phase and is implemented through entirely optical means within a single electronic spin in diamond. [9] New research demonstrates that particles at the quantum level can in fact be seen as behaving something like billiard balls rolling along a table, and not merely as the probabilistic smears that the standard interpretation of quantum mechanics suggests. But there's a catch - the tracks the particles follow do not always behave as one would expect from "realistic" trajectories, but often in a fashion that has been termed "surrealistic." [8] Quantum entanglement—which occurs when two or more particles are correlated in such a way that they can influence each other even across large distances—is not an all-or-nothing phenomenon, but occurs in various degrees. The more a quantum state is entangled with its partner, the better the states will perform in quantum information applications. Unfortunately, quantifying entanglement is a difficult process involving complex optimization problems that give even physicists headaches. [7] A trio of physicists in Europe has come up with an idea that they believe would allow a person to actually witness entanglement. Valentina Caprara Vivoli, with the University of Geneva, Pavel Sekatski, with the University of Innsbruck and Nicolas Sangouard, with the University of Basel, have together written a paper describing a scenario where a human subject would be able to witness an instance of entanglement—they have uploaded it to the arXiv server for review by others. [6] The accelerating electrons explain not only the Maxwell Equations and the Special Relativity, but the Heisenberg Uncertainty Relation, the Wave-Particle Duality and the electron’s spin also, building the Bridge between the Classical and Quantum Theories. The Planck Distribution Law of the electromagnetic oscillators explains the electron/proton mass rate and the Weak and Strong Interactions by the diffraction patterns. The Weak Interaction changes the diffraction patterns by moving the electric charge from one side to the other side of the diffraction pattern, which violates the CP and Time reversal symmetry. The diffraction patterns and the locality of the self-maintaining electromagnetic potential explains also the Quantum Entanglement, giving it as a natural part of the relativistic quantum theory.
Category: Artificial Intelligence

[6] viXra:1803.0072 [pdf] submitted on 2018-03-06 03:34:57

Universal Forecasting Scheme {Version 1}

Authors: Ramesh Chandra Bagadi
Comments: 2 Pages.

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

[5] viXra:1803.0070 [pdf] submitted on 2018-03-06 04:39:44

Universal Forecasting Scheme {Version 2}

Authors: Ramesh Chandra Bagadi
Comments: 2 Pages.

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

[4] viXra:1803.0069 [pdf] submitted on 2018-03-06 04:45:06

Universal Forecasting Scheme {Version 3}

Authors: Ramesh Chandra Bagadi
Comments: 2 Pages.

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

[3] viXra:1803.0061 [pdf] submitted on 2018-03-04 22:32:07

Cancer Detection Through Handwriting

Authors: Alaa Tarek, Shorouk Alalem, Maryam El-Fdaly, Nehal Fooda
Comments: 31 Pages.

Having a look at the medical field in the previous years and the drawbacks in it, Egypt's health level is declining year after year; due to the statistical study on the health level published by the British medical journal Lancet in 2016 Egypt ranked 124 out of 188 countries. Cancer is a major burden of disease worldwide. Each year, 10,000,000 of people are diagnosed with cancer around the world, and more than half of the patients eventually die because of it. In many countries, cancer ranks the second most common cause of death following cardiovascular diseases. With significant improvement in treatment and prevention of cardiovascular diseases, cancer has or will soon become the number one killer in many parts of the world. Nearly 90,000 people do not know they have got cancer until they arrive at Accident and Emergency wards, by that time only 36 percent will live longer than a year. So, we needed to find controlled ways to diagnose patients earlier. As no aspect of human life has escaped the impact of the information age, and perhaps in no area of life is information more critical than in health and medicine. However, computers have become available for all aspects of human endeavors. After so, we have designed a program that could detect if a person has cancer or not through your handwriting. We chose “efficiency, cost, and applicability” as the design requirements that have been tested. The program could be tested by scanning the text, searching for specific features that are related to cancer and displaying “1” or “0” according to your state. After testing the program many times, we finally reached a mean efficiency of 93.75%. So, this program saves lives, time and money.
Category: Artificial Intelligence

[2] viXra:1803.0053 [pdf] submitted on 2018-03-04 05:55:27

Tunnel Similar Modeling Notation and Spherical Viewpoint

Authors: Alexey Podorov
Comments: 11 Pages.

The article proposes a spherical model of perception, groups and levels of complexity, notation for modeling abstractness, complexity
Category: Artificial Intelligence

[1] viXra:1803.0023 [pdf] submitted on 2018-03-01 09:41:28

AI for Safer Cities

Authors: George Rajna
Comments: 50 Pages.

Computers may better predict taxi and ride sharing service demand, paving the way toward smarter, safer and more sustainable cities, according to an international team of researchers. [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