Artificial Intelligence

1703 Submissions

[6] viXra:1703.0233 [pdf] submitted on 2017-03-24 10:36:41

Parallel Computation and Brain Function

Authors: George Rajna
Comments: 26 Pages.

Unlike experimental neuroscientists who deal with real-life neurons, computational neuroscientists use model simulations to investigate how the brain functions. [15] A pair of physicists with ETH Zurich has developed a way to use an artificial neural network to characterize the wave function of a quantum many-body system. [14] A team of researchers at Google's DeepMind Technologies has been working on a means to increase the capabilities of computers by combining aspects of data processing and artificial intelligence and have come up with what they are calling a differentiable neural computer (DNC.) In their paper published in the journal Nature, they describe the work they are doing and where they believe it is headed. To make the work more accessible to the public team members, Alexander Graves and Greg Wayne have posted an explanatory page on the DeepMind website. [13] Nobody understands why deep neural networks are so good at solving complex problems. Now physicists say the secret is buried in the laws of physics. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. 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 and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[5] viXra:1703.0063 [pdf] submitted on 2017-03-07 09:36:39

Human Readable Feature Generation for Natural Language Corpora

Authors: Tomasz Dryjanski
Comments: 4 Pages.

This paper proposes an alternative to the Paragraph Vector algorithm, generating fixed-length vectors of human-readable features for natural language corpora. It extends word2vec retaining its other advantages like speed and accuracy, hence its proposed name is doc2feat. Extracted features are presented as lists of words with their proximity to the particular feature, allowing interpretation and manual annotation. By parameter tuning focus can be made on grammatical aspects of the corpus language, making it useful for linguistic applications. The algorithm can run on variable-length pieces of texts, and provides insight into what features are relevant for text classification or sentiment analysis. The corpus does not have to, and in specific cases should not be, preprocessed with stemming or stop-words removal.
Category: Artificial Intelligence

[4] viXra:1703.0056 [pdf] submitted on 2017-03-06 13:57:49

Quantum Machine Learning to Infinite Dimensions

Authors: George Rajna
Comments: 27 Pages.

Physicists have developed a quantum machine learning algorithm that can handle infinite dimensions—that is, it works with continuous variables (which have an infinite number of possible values on a closed interval) instead of the typically used discrete variables (which have only a finite number of values). [15] Physicists have shown that quantum effects have the potential to significantly improve a variety of interactive learning tasks in machine learning. [14] A Chinese team of physicists have trained a quantum computer to recognise handwritten characters, the first demonstration of " quantum artificial intelligence ". Physicists have long claimed that quantum computers have the potential to dramatically outperform the most powerful conventional processors. The secret sauce at work here is the strange quantum phenomenon of superposition, where a quantum object can exist in two states at the same time. [13] One of biology's biggest mysteries-how a sliced up flatworm can regenerate into new organisms-has been solved independently by a computer. The discovery marks the first time that a computer has come up with a new scientific theory without direct human help. [12] A team of researchers working at the University of California (and one from Stony Brook University) has for the first time created a neural-network chip that was built using just memristors. In their paper published in the journal Nature, the team describes how they built their chip and what capabilities it has. [11] A team of researchers used a promising new material to build more functional memristors, bringing us closer to brain-like computing. Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons. [10] Cambridge Quantum Computing Limited (CQCL) has built a new Fastest Operating System aimed at running the futuristic superfast quantum computers. [9] IBM scientists today unveiled two critical advances towards the realization of a practical quantum computer. For the first time, they showed the ability to detect and measure both kinds of quantum errors simultaneously, as well as demonstrated a new, square quantum bit circuit design that is the only physical architecture that could successfully scale to larger dimensions. [8] Physicists at the Universities of Bonn and Cambridge have succeeded in linking two completely different quantum systems to one another. In doing so, they have taken an important step forward on the way to a quantum computer. To accomplish their feat the researchers used a method that seems to function as well in the quantum world as it does for us people: teamwork. The results have now been published in the "Physical Review Letters". [7] While physicists are continually looking for ways to unify the theory of relativity, which describes large-scale phenomena, with quantum theory, which describes small-scale phenomena, computer scientists are searching for technologies to build the quantum computer. 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 and making possible to build the Quantum Computer.
Category: Artificial Intelligence

[3] viXra:1703.0055 [pdf] replaced on 2017-03-08 16:33:20

Application of a Shallow Neural Network to Short-Term Stock Trading

Authors: Abhinav Madahar, Yuze Ma, Kunal Patel
Comments: 4 Pages.

Machine learning is increasingly prevalent in stock market trading. Though neural networks have seen success in computer vision and natural language processing, they have not been as useful in stock market trading. To demonstrate the applicability of a neural network in stock trading, we made a single-layer neural network that recommends buying or selling shares of a stock by comparing the highest high of 10 consecutive days with that of the next 10 days, a process repeated for the stock’s year-long historical data. A chi-squared analysis found that the neural network can accurately and appropriately decide whether to buy or sell shares for a given stock, showing that a neural network can make simple decisions about the stock market.
Category: Artificial Intelligence

[2] viXra:1703.0013 [pdf] submitted on 2017-03-02 05:44:53

Controlling a Robot Using a Wearable Device (MYO)

Authors: Mithileysh Sathiyanarayanan, Tobias Mulling, Bushra Nazir
Comments: 6 Pages. IJEDR, 2015, Vol 3, Issue 3

There is a huge demand for military robots in almost all the countries which comes under the field of human computer interaction and artificial intelligence. There are many different ways of operating a robot: self controlled, automatic controlled etc. Also, gesture controlled operation mode is on the rise. This acted as our motivation to develop a gesture controlled robot using MYO armband. The word ‘MYO’ has created a buzz in the technological world by its astonishing features and its utility in various fields. Its introduction as a armband that can wrap around our arm to control robots with our movements and gestures has opened new wide doors of its experimentation with robotics. This independently working gesture recognition system does not rely on any external sensors (motion capturing system) as it has its sensors embedded in itself which recognizes the gesture commands and acts accordingly. This armband can be worn by soldiers to operate robots to fight against enemies. This work in progress paper illustrates an existing robot designed by us, which can be controlled by hand gestures using a wearable device called as MYO. We would like to investigate more on this and implement, such that the robot can be interfaced with a MYO armband for a successful control.
Category: Artificial Intelligence

[1] viXra:1703.0012 [pdf] submitted on 2017-03-02 05:49:01

Leap Motion Device for Gesture Controlling an Unmanned Ground Vehicle (Robot)

Authors: Mithileysh Sathiyanarayanan, Tobias Mulling, Bushra Nazir
Comments: 10 Pages. IJEDR, 2016, Vol 4, Issue 4

A new scope of human-computer interaction utilizes the algorithms of computer vision and image processing for detecting the gesture, understanding its objective and making it meaningful for the computer to understand and then interact with the humans. The recent introduction of "Leap Motion" is a big revolution in the field of gesture control technology. Using gesture control mode in the field of robotics is also on the rise. This acted as our motivation to develop a gesture controlled robot using a Leap Motion Device that can sense human hands above it and to keep a track of them and aid in navigation. This independently working gesture recognition system does not rely on any external sensors (motion capturing system) as it has its sensors embedded in itself which recognizes the gesture commands and acts accordingly. The soldiers need not wear any physical device on their body (unlike Kinect and/or MYO Armband) to operate robots to fight against enemies. This work in progress paper illustrates an existing robot designed by us, which can be controlled by hand gestures using a non-wearable (touchless) device called as Leap Motion.
Category: Artificial Intelligence