Chapter 1 - MITI 5113 ARTIFICIAL INTELLIGENCE LECTURE 1...

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MITI 5113 ARTIFICIAL INTELLIGENCE LECTURE 1 LECTURE 1 Artificial Intelligence 04/16/17 1
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Intelligence: “the capacity to learn and solve problems” (Websters dictionary) in particular, the ability to solve novel problems the ability to act rationally the ability to act like humans Artificial Intelligence build and understand intelligent entities or agents 2 main approaches: “engineering” versus “cognitive modeling” 04/16/17 2
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Ability to interact with the real world to perceive (observe), understand, and act e.g., speech recognition and understanding and synthesis e.g., image understanding e.g., ability to take actions, have an effect Reasoning and Planning modeling the external world, given input solving new problems, planning, and making decisions ability to deal with unexpected problems, uncertainties Learning and Adaptation we are continuously learning and adapting our internal models are always being “updated” e.g., a baby learning to categorize and recognize animals 04/16/17 4
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Philosophy Logic, methods of reasoning, mind as physical system, foundations of learning, language, rationality. Mathematics Formal representation and proof, algorithms, computation, (un)decidability, (in)tractability Probability/Statistics modeling uncertainty, learning from data Economics utility, decision theory, rational economic agents Neuroscience neurons as information processing units. Psychology/ how do people behave, perceive, process cognitive Cognitive Science information, represent knowledge. Computer building fast computers engineering Control theory design systems that maximize an objective function over time Linguistics knowledge representation, grammars 04/16/17 5
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1943: early beginnings McCulloch & Pitts: Boolean circuit model of brain 1950: Turing Turing's "Computing Machinery and Intelligence“ 1956: birth of AI Dartmouth meeting: "Artificial Intelligence“ name adopted 1950s: initial promise Early AI programs, including Samuel's checkers program Newell & Simon's Logic Theorist 1955-65: “great enthusiasm” Newell and Simon: GPS, general problem solver Gelertner: Geometry Theorem Prover McCarthy: invention of LISP 04/16/17 6
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1966—73: Reality dawns Realization that many AI problems are intractable Limitations of existing neural network methods identified Neural network research almost disappears 1969—85: Adding domain knowledge Development of knowledge-based systems Success of rule-based expert systems, E.g., DENDRAL, MYCIN But were brittle and did not scale well in practice 1986-- Rise of machine learning Neural networks return to popularity Major advances in machine learning algorithms and applications 1990-- Role of uncertainty Bayesian networks as a knowledge representation framework 1995-- AI as Science Integration of learning, reasoning, knowledge representation AI methods used in vision, language, data mining, etc 04/16/17 7
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