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Exponential Family Distributions
Exponential Family Distributions

... C. Bregler and S.M. Omohundro. Nonlinear manifold learning for visual speech recognition. In Fifth International Conference on Computer Vision, pages 494–499, Boston, Jun 1995. J. Buhler, T. Ideker, and D. Haynor. Dapple: Improved techniques for finding spots on DNA microarrays. Technical report, Un ...
ppt - UTRGV Faculty Web
ppt - UTRGV Faculty Web

... assumptions and unresolved questions ◦ For some, the goal is to create an artificial mind ◦ For others, to discover a formal model of thought ◦ Others, to build machines (programs) that perform difficult human-like tasks ...
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Artificial Intelligence Expert Systems

... User interface provides interaction between user of the ES and the ES itself. It is generally Natural Language Processing so as to be used by the user who is well-versed in the task domain. The user of the ES need not be necessarily an expert in Artificial Intelligence. It explains how the ES has ar ...
Mtech Syllabus - GEHU CS/IT Deptt
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... Procedural Vs declaration knowledge-forward Vs backward reasoning – matching techniques – control knowledge/strategies-symbol reasoning under uncertainty – introduction to non – monotonic reasoning-logic for monotonic reasoning-implementation issues-augmenting a problem solverstatistical reasoning-B ...
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Biological Intelligence and Computational Intelligence
Biological Intelligence and Computational Intelligence

... The definition of intelligence raises a further question. Can intelligence be considered as a unique function, in the physiological sense, or is it the product of a set of independent mechanisms which, when combined, lead to intelligent activity? However, we must first lay down a general definition ...
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ISP / ISPI Reading Group: Artificial Intelligence, Robotics and Law

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Shortest Path in a Combined Street and Public

... Computerized car navigation systems are probably the most popular application of geographic information systems. They go back to one of the first thorough analyses of an algorithm by computer pioneer Edsger Dijkstra (1976): the task to find the shortest path in a (street) network. Dijkstra's shortes ...
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IEEE Global Initiative for Ethical Considerations in Artificial
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... and non-rigid sorts, and formalize the concept definition and relationships in the formulas of sorted predicates. Then, we prove the satisfiability of sorts, consistency of subsumption between sorts, and the soundness of our logic system in terms of Herbrand model. We also illustrate the dual subsum ...
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... A human is connected to a person and a machine via a terminal of some kind and cannot see either the person or machine. The interrogator's task is to find out which of the two candidates is the machine, and which is human only by asking them questions. If the human cannot make a decision within a ce ...
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... – Costs are associated with activities of an agent  solution quality – You have to trade off computation time and solution quality: an anytime algorithm can provide a solution at any time; given more time it can produce better solutions (e.g., hill-climbing algorithms) – You don’t only need to be c ...
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... Computer Science and they are largely neuroscientists or psychologists. These are people who are concerned with real, human intelligence. As such, Searle would categorize their work as “weak AI” and he explicitly exempts them from his argument. On the other side, we have a group of researchers who a ...
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Beneficial AI 2017 - Future of Life Institute

... and empirical approach to making AI systems safe. Dario is currently a research scientist at OpenAI, and prior to that worked at Google and Baidu. Dario also helped to lead the project that developed Deep Speech 2, which was named one of 10 “Breakthrough Technologies of 2016” by MIT Technology Revie ...
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Neural Networks - Temple Fox MIS

... A hurdle value for the output of a neuron to trigger the next level of neurons. If an output value is smaller than the threshold value, it will not be passed to the next level of neurons ...
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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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