Mean Field Methods for a Special Class of Belief Networks

The chief aim of this paper is to propose mean-field approximations for a broad class of Belief networks, of which sigmoid and noisy-or networks can be seen as special cases. The approximations are based on a powerful mean-field theory suggested by Plefka. We show that Saul, Jaakkola and Jordan' s approach is the first order approximation in Plefka's approach, via a variational derivation. The application of Plefka's theory to belief networks is not computationally tractable. To tackle this problem we propose new approximations based on Taylor series. Small scale experiments show that the proposed schemes are attractive

SCARY DARK SIDE OF ARTIFICIAL INTELLIGENCE: A PERILOUS CONTRIVANCE TO MANKIND

Purpose of Study: The purpose of the study is to investigate the dark side of artificial intelligence followed by the question of whether AI is programmed to do something destructive or AI is programmed to do something beneficial? Methodology: A study of different biased Super AI is carried out to find the dark side of AI. In this paper SRL (system review of literature approach methodology is used and the data is collected from the different projects of MIT’s media lab named “Norman AI”, “Shelley” and  AI-generated algorithm COMPAS. Main Finding: The study carried out the result if AI is trained in a biased way it will create havoc to mankind. Implications/Applications: The article can help in developing super-AIs which can benefit the society in a controlled way without having any negative aspects. Novelty/originality of the study: Our findings ensure that biased AI has a negative impact on society

Mean-field methods for a special class of belief networks

Journal of Artificial Intelligence Research1591-114JAIR