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Two Forms of Dependence in Propositional Logic
Two Forms of Dependence in Propositional Logic

An Efficient Learning Procedure for Deep Boltzmann Machines
An Efficient Learning Procedure for Deep Boltzmann Machines

... 1. The performance is comparable with the best other single models, such as probabilistic matrix factorization. By averaging many models it is possible to do better and the two systems with the best performance on Netflix both use multiple RBMs among the many models that are averaged. ...
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CV - Computer Science Intranet
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... Lecturer, Department of Electronic Engineering at Queen Mary and Westfield College, University of London. March 1995 – September 1995 Research Fellow, Department of Electronic Engineering at Queen Mary and Westfield College, University of London. ...
Fuzzy Logic - Authentic Leadership Center
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... Fuzzy set theory, the bedrock of fuzzy logic, was introduced by Lofti Zadeh in 1965. It was specifically designed to mathematically represent uncertainty and vagueness with formalized logical tools for dealing with the imprecision inherent in many real-world problems (Zadeh, 1965). Until this date, ...
Machine Condition Monitoring Using Artificial Intelligence: The
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Meinongian Semantics and Artificial Intelligence
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CS Accred Books - Kutztown University

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An Efficient Learning Procedure for Deep Boltzmann Machines
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... well with a learning rate that is much larger than the obvious asymptotic analysis would allow. In an attempt to reduce the time required by the sampling process, Peterson and Anderson (1987) replaced Gibbs sampling with a simple mean-field method that approximates a stationary distribution by repla ...
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... well with a learning rate that is much larger than the obvious asymptotic analysis would allow. In an attempt to reduce the time required by the sampling process, Peterson and Anderson (1987) replaced Gibbs sampling with a simple mean-field method that approximates a stationary distribution by repla ...
Fuzzy Genetic Algorithms
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A Google-Proof Collection of French Winograd Schemas
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pre-print - School of Computer Science, University of Birmingham.
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... The metaphorical quality of commonsense models of mind is extremely important, but is not the focus of the present article. My concentration is rather on their commonsensicality. I say more about metaphor in Barnden (1989), Barnden (1991), and Barnden (1992b). ...
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Full Dynamic Substitutability by SAT Encoding

... To evaluate the maximal encoding we compare it with the direct and support encodings on a set of binary CSPs. We use vertex colouring problems and a generalisation called bandwidth colouring, in which two adjacent vertices i and j cannot be assigned colours that are closer in value than a specified ...
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AMC - Queen Mary University of London

... a distinction is drawn between three types of creative systems: those which are purely generative; those which contain internal or external feedback; and those which are capable of reflection and self-reflection. To address the evaluation of each of these aspects, concrete examples of methods and te ...
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Pickman`s Machine: A Reasoning Architecture Baki Cakici

... A definition of artificial intelligence and an introduction to earlier research is attempted in this chapter. This is not without its challenges; examples from various sources illustrate the diversity of opinion within the field: “Artificial intelligence is the design and study of computer programs ...
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History of artificial intelligence

The history of artificial intelligence (AI) began in antiquity, with myths, stories and rumors of artificial beings endowed with intelligence or consciousness by master craftsmen; as Pamela McCorduck writes, AI began with ""an ancient wish to forge the gods.""The seeds of modern AI were planted by classical philosophers who attempted to describe the process of human thinking as the mechanical manipulation of symbols. This work culminated in the invention of the programmable digital computer in the 1940s, a machine based on the abstract essence of mathematical reasoning. This device and the ideas behind it inspired a handful of scientists to begin seriously discussing the possibility of building an electronic brain.The field of AI research was founded at a conference on the campus of Dartmouth College in the summer of 1956. Those who attended would become the leaders of AI research for decades. Many of them predicted that a machine as intelligent as a human being would exist in no more than a generation and they were given millions of dollars to make this vision come true. Eventually it became obvious that they had grossly underestimated the difficulty of the project. In 1973, in response to the criticism of James Lighthill and ongoing pressure from congress, the U.S. and British Governments stopped funding undirected research into artificial intelligence. Seven years later, a visionary initiative by the Japanese Government inspired governments and industry to provide AI with billions of dollars, but by the late 80s the investors became disillusioned and withdrew funding again. This cycle of boom and bust, of ""AI winters"" and summers, continues to haunt the field. Undaunted, there are those who make extraordinary predictions even now.Progress in AI has continued, despite the rise and fall of its reputation in the eyes of government bureaucrats and venture capitalists. Problems that had begun to seem impossible in 1970 have been solved and the solutions are now used in successful commercial products. However, no machine has been built with a human level of intelligence, contrary to the optimistic predictions of the first generation of AI researchers. ""We can only see a short distance ahead,"" admitted Alan Turing, in a famous 1950 paper that catalyzed the modern search for machines that think. ""But,"" he added, ""we can see much that must be done.""
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