CSCI 4150 Introduction and History of AI

CSCI 4150 Introduction and History of AI - 1 Introduction...

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Introduction and History 1 1.1 Brief history of AI 1 1.2 Approaches to AI? 3 1.3 AI areas 4 A precise definition of artificial intelligence (AI) is not easy and often contro- versial. This introductory chapter outlines some areas associated with AI in a historical context and by highlighting some modern approaches in AI. Many problems in AI can be formulated as search problems, which we review in the first part of the course. The course then concentrates on machine learning (ML) and probabilistic reasoning. 1.1 Brief history of AI 1943 McCulloch & Pitts: Boolean circuit model of brain 1950 Turing’s “Computing Machinery and Intelligence” 1952–69 Look, Ma, no hands! 1950s Early AI programs, including Samuel’s checkers program, Newell & Simon’s Logic Theorist, Gelernter’s Geometry Engine 1956 Dartmouth meeting: “Artificial Intelligence” adopted 1965 Robinson’s complete algorithm for logical reasoning 1966–74 AI discovers computational complexity Neural network research almost disappears 1969–79 Early development of knowledge-based systems 1980–88 Expert systems industry booms 1988–93 Expert systems industry busts: “AI Winter” 1985–95 Neural networks return to popularity 1988 Resurgence of probability; general increase in technical depth “Nouvelle AI”: ALife, GAs, soft computing 1995 Agents, agents, everywhere . . . Machine learning comes to age, web intelligence, smart machines 2003 Human-level AI back on the agenda The term AI was born in 1956, at a workshop in Dartmouth organized by John McCarthy. Those gathered agreed to adopt McCarthys name for the new field: Artificial Intelligence. At that point, there was lots of enthusiasm. Things seemed to work out really well. Only a few years before, computers were viewed as large calcula- tors, and now truly intelligent systems seemed within reach. Early programs did amazing things by simply representing knowledge about a domain and searching for a solution. For example, Newell & Simon’s ‘Logic Theorist’ proved qualitative mathematical theorems, and even found a shorter proof for one of the theorems in Russell and Whitehead’s ‘Principia Mathematica’. In 1958, McCarthy suggested how the same paradigm could be used for commonsense reasoning: represent knowledge about the everyday
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2 Introduction and History world as logical axioms, and use that knowledge to figure out how to act. Amazingly, a general-purpose logical theorem prover was able (for instance) to generate a plan for driving to the airport. Arguably the first convincing ma- chine learning program, Arthur Samuel’s Checkers playing program started out playing poorly, but learned to play better by playing many games against itself. Growing to play better than Samuel, this program disproved the (still-made) argument that computers can only do what they are told to do. A particu- larly good example of how a simple set of rules can produce seemingly complex behavior was Joseph Weizenbaum’s Eliza program, which simulates a Roge- rian psychotherapist. Although Eliza’s algorithms are best described as simple
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