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CS 294-5: Statistical Natural Language Processing
CS 294-5: Statistical Natural Language Processing

...  Cognitive neuroscience: Direct identification from neurological data (bottom-up)  Both approaches now distinct from AI  Both share with AI the following characteristic:  The available theories do not explain (or engender) anything resembling human-level general intelligence} ...
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Artificial intelligence

... traced back to ancient Egypt, but with the development of the electronic computer in 1941, the technology finally became available to create machine intelligence. The term artificial intelligence was first coined in 1956, at the Dartmouth conference, and since then Artificial Intelligence has expand ...
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AGRICULTURE ADVANCEMENT USING ARTIFICIAL INTELLIGENCE

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Interim Report from the Panel Chairs
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... After several months of discussion by email and phone, a face-to-face meeting was held at Asilomar, at the end of Feburary 2009. Asilomar was selected as a site for the meeting primarily because it is simply a fabulous place for a reflective meeting. We also selected the site given the broad symboli ...
Syllabus - UBC Computer Science
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Current and Future Trends in AI

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Virtual Program Modules of AI Systems

... understanding and other similar capabilities. AI is possible to consider like the top solution of autonomous machines and equipments and this solution represents wider range like same of traditional automation. AI offers flexible techniques for nonnumeric problem solving. Processing of symbolic info ...
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CS7075sy_FA_2016 - Kennesaw State University
CS7075sy_FA_2016 - Kennesaw State University

... Artificial Intelligence (AI) is a broad and diverse area of research and practice, and an introductory course can only touch briefly on the basic ideas and techniques of the field. CS7075 is designed to provide you with a basic background in the fundamentals of AI, whether you are planning to pursue ...
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Introduction to Artificial Intelligence

... Pattern-directed reasoning systems: knowledge is represented as “rules” (such as “if A, then B”), and the so-called “search engine” identifies and fires rules which antecedents hold. Example: the KM* system (pp. 92). Truth Maintenance Systems: the latest AI methodology for efficient search and gener ...
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... creating) intelligence has been broken down into a number of specific subproblems. These consist of particular traits or capabilities that researchers would like an intelligent system to display. The traits described below have received the most attention : ...
ISE6810: Special Topics in Intelligent Decision Support Systems
ISE6810: Special Topics in Intelligent Decision Support Systems

... Dr. W.H. Ip (Industrial and Systems Engineering) Pre-requisite: Nil Recommended background knowledge: Basic understanding of computing language and database is expected. Objectives: This subject aims to provide student with the advance modelling and methodology for integration of expert systems and ...
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... The idea started as early as the 17th century with Rene Descartes envisioning animals in the future being complex machines, at least partly. The idea evolved as the 1900s approached, and in the 1950s, algorithms began to be produced. The 60s and 70s brought promising ideas and mechanical methods of ...
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STAIR Grant RFP Addendum

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Artificial Intelligence A Review on Role of Expert System
Artificial Intelligence A Review on Role of Expert System

... Artificial Intelligence & the technology are one side of the life that always interest and surprise us with the new ideas, topics, innovations, products …etc.AI is at the centre of a new enterprise to build computational models of intelligence. Finally, the capability to continually change and obtai ...
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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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