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Classification Problem Solving
Classification Problem Solving

... classes are stereotypes that are hierarchically organized, and the process of identification is one of matching observations of an unknown entity against features of known classes. A paradigmatic example is identification of a plant or animal, using a guidebook of features, such as coloration, struc ...
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... working in quality control in the United States. This was followed by trade show appearances, roadshows at dealers and system integrators, and a lively presence on social media. Now the ViDi software suite is being tested by well-known American companies for their production processes, and in some c ...
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... Artificial Intelligence is the area of computer science focusing on creating machines that can engage on behaviours that humans consider intelligent. The ability to create intelligent machines has intrigued humans since ancient times and today with the advent of the computer and 50 years of research ...
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From: AAAI Technical Report S-9 - 0. Compilation copyright © 199

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... start with a simple general model, and then correct the findings by including missing details. This was the motivation behind the choice of the presented model and its simulation. In the present version, the model and its simulation results do not tell us precise details about how humans learn thro ...
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Application of Artificial Intelligence in Today`s

... Different Types of Artificial Intelligence Acting Humanly- Modeling exactly how humans actually act ”The art of creating machines that perform functions that require intelligence when performed by people”, (Kurzweil,1990) Ultimately to be tested by the Turing Test This require physical interaction w ...
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Explanation-based Mechanisms for Learning: An

... explaining “Why?” drives the discovery of abstract regularities, which then provide the basis for generalization to novel contexts. The double-edged nature of this constraint is demonstrated in an explanation impairment effect: if people seek explanations when only misleading regularities are presen ...
Vision: Semantic Routing
Vision: Semantic Routing

... – attributes: sender, contents, context, constraints ...
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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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