Artificial Intelligence

1509 Submissions

[4] viXra:1509.0163 [pdf] submitted on 2015-09-18 04:00:41

Lie Detection System with Voice Using Bidirectional Associative Memory Algorithm

Authors: Bustami; Fadlisyah; Nurdania Delemunte
Comments: 07 Pages. Figures :04 Tables : 03, IJCAT.org, Volume 2, Issue 8, August 2015

Lie detection through voice can be detected using the algorithm bidirectional associative memory. This system is a branch of sound processing that can be used to identify the type of sound lies use some verbs like go, roads and move. This study uses an algorithm bidirectional associative memory for the process and the introduction of lie detection training through the sound use of bidirectional associative memory. The system was tested by simulating the training data and test data to generate a percentage of voice recognition and classification of these lies. Experiments performed with several changes in parameter values to obtain the best percentage of recognition and classification. The highest level of recognition contained in the verb "go" with up to 90%. Results of this research is a sound that indicated not indicated lies and deceit in the form of values are classified according to the type of sound that is known from the results of calculations of energy use bidirectional associative memory.
Category: Artificial Intelligence

[3] viXra:1509.0119 [pdf] submitted on 2015-09-13 20:01:24

The Maximum Deng Entropy

Authors: Bingyi Kang, Yong Deng
Comments: 17 Pages

Dempster Shafer evidence theory has widely used in many applications due to its advantages to handle uncertainty. Deng entropy, has been proposed to measure the uncertainty degree of basic probability assignment in evidence theory. It is the generalization of Shannon entropy since that the BPA is degenerated as probability, Deng entropy is identical to Shannon entropy. However, the maximal value of Deng entropy has not been disscussed until now. In this paper, the condition of the maximum of Deng entropy has been disscussed and proofed, which is usefull for the application of Deng entropy.
Category: Artificial Intelligence

[2] viXra:1509.0088 [pdf] replaced on 2015-09-08 18:58:24

Cognitive Architecture for Personable and Human-Like ai :A Perspective

Authors: Arvind Chitra Rajasekaran
Comments: 4 Pages.

In this article we will introduce a cognitive architecture for creating a more human like and personable artificial intelligence. Recent works such as those by Marvin Minsky, Google DeepMind and cognitive models like AMBR, DUAL that aim to propose/discover an approach to commonsense AI have been promising, since they show that human intelligence can be emulated with a divide and conquer approach on a machine. These frameworks work with an universal model of the human mind and do not account for the variability between human beings. It is these differences between human beings that make communication possible and gives them a sense of identity. Thus, this work, despite being grounded in these methods, will differ in hypothesizing machines that are diverse in their behavior compared to each other and have the ability to express a dynamic personality like a human being. To achieve such individuality in machines, we characterize the various aspects that can be dynamically programmed onto a machine by its human owners. In order to ensure this on a scale parallel to how humans develop their individuality, we first assume a child-like intelligence in a machine that is more malleable and which then develops into a more concrete, mature version. By having a set of tunable inner parameters called aspects which respond to external stimuli from their human owners, machines can achieve personability. The result of this work would be that we will not only be able to bond with the intelligent machines and relate to them in a friendly way, we will also be able to perceive them as having a personality, and that they have their limitations. Just as each human being is unique, we will have machines that are unique and individualistic. We will see how they can achieve intuition, and a drive to find meaning in life, all of which are considered aspects unique to the human mind.
Category: Artificial Intelligence

[1] viXra:1509.0069 [pdf] submitted on 2015-09-05 10:41:32

Startup Breakthrough in Brain-like Computing

Authors: George Rajna
Comments: 21 Pages.

A small, Santa Fe, New Mexico-based company called Knowm claims it will soon begin commercializing a state-of-the-art technique for building computing chips that learn. Other companies, including HP HPQ -3.45% and IBM IBM -2.10% , have already invested in developing these so-called brain-based chips, but Knowm says it has just achieved a major technological breakthrough that it should be able to push into production hopefully within a few years. [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