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The Long-‐‑Term Future of (Artificial) Intelligence
The Long-‐‑Term Future of (Artificial) Intelligence

... planet to a cinder is better than preserving human civilization. Futurists such as Edward Fredkin and Hans Moravec have, however, suggested that once the human race has fulfilled its destiny in bringing into existence entities of higher (and perhaps unlimited) intelligence, its own preservation may ...
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... complex biomedical systems including sophisticated medical imaging equipment and CAD (computer aided diagnosis) tools enabling the better delivery of health care services. In parallel, computational intelligence, incorporating neural computing, fuzzy systems, and evolutionary computing emerged as pr ...
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... expert system consisted of a relatively large number of "if then" type of statements that were interrelated in a manner that, in theory at least, resembled the sequence of mental steps that were involved in the human reasoning process. Because of the need for large storage capacities and related pro ...
ARTIFICIAL INTELLIGENCE IN CANADA
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... 1943 – W. McCulloch and W. Pitts designed the first neural network. M. Minsky and D. Edmonds built the first one in 1951 at Princeton. 1950 – A. Turing, "Computing Machinery and Intelligence". 1956 – J. McCarthy organized a workshop at Darmouth where the name of AI was officially adopted for the fie ...
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Tutorial on Knowledge Engineering in Medicine and Health Care
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... http://www.asu.edu.eg/staff/profile.php?action=show&pid=8256 [email protected] [email protected] In order to act intelligently, a computer must have knowledge about the domain of interest. The process of collecting, representing, and organizing the knowledge is called knowledge engineering (KE ...
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Extinguished philosophies lie about the cradle of every science as the
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GOVERNORS STATE UNIVERSITY
GOVERNORS STATE UNIVERSITY

... During the past few years, theoretical and applied computer sciences have focused attention on the concepts of artificial intelligence (AI). Although AI has been around for decades, only recently has it become known outside of the computer world, since the Japanese began to put large national effort ...
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AI winter

In the history of artificial intelligence, an AI winter is a period of reduced funding and interest in artificial intelligence research. The term was coined by analogy to the idea of a nuclear winter. The field has experienced several hype cycles, followed by disappointment and criticism, followed by funding cuts, followed by renewed interest years or decades later. There were two major winters in 1974–80 and 1987–93 and several smaller episodes, including: 1966: the failure of machine translation, 1970: the abandonment of connectionism, 1971–75: DARPA's frustration with the Speech Understanding Research program at Carnegie Mellon University, 1973: the large decrease in AI research in the United Kingdom in response to the Lighthill report, 1973–74: DARPA's cutbacks to academic AI research in general, 1987: the collapse of the Lisp machine market, 1988: the cancellation of new spending on AI by the Strategic Computing Initiative, 1993: expert systems slowly reaching the bottom, and 1990s: the quiet disappearance of the fifth-generation computer project's original goals.The term first appeared in 1984 as the topic of a public debate at the annual meeting of AAAI (then called the ""American Association of Artificial Intelligence""). It is a chain reaction that begins with pessimism in the AI community, followed by pessimism in the press, followed by a severe cutback in funding, followed by the end of serious research. At the meeting, Roger Schank and Marvin Minsky—two leading AI researchers who had survived the ""winter"" of the 1970s—warned the business community that enthusiasm for AI had spiraled out of control in the '80s and that disappointment would certainly follow. Three years later, the billion-dollar AI industry began to collapse.Hypes are common in many emerging technologies, such as the railway mania or the dot-com bubble. An AI winter is primarily a collapse in the perception of AI by government bureaucrats and venture capitalists. Despite the rise and fall of AI's reputation, it has continued to develop new and successful technologies. AI researcher Rodney Brooks would complain in 2002 that ""there's this stupid myth out there that AI has failed, but AI is around you every second of the day."" In 2005, Ray Kurzweil agreed: ""Many observers still think that the AI winter was the end of the story and that nothing since has come of the AI field. Yet today many thousands of AI applications are deeply embedded in the infrastructure of every industry."" He added: ""the AI winter is long since over.""
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