Chapter 11 Outline - Chapter 11: TED TALKS Why don't we...

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Chapter 11: TED TALKS Why don’t we have a good brain theory? -because we have intuitive, strongly held but incorrect assumption that has prevented us from seeing the answer— people think intelligence is defined behavior, when actually intelligence is defined by prediction -theory will be about: memory, sequence, and predictions Why is it important? -people are curious; will give a new understanding of who we are and how we feel and perceive; can build intelligent machines once we understand the brain What can we do about it? -start simple, intelligent cars that understand traffic, and intelligent security systems will come before a full-throttle robot -Plato gave voice to idea that our ability to be rational was closely tied to intelligence; this has been a driving force in AI -Historical Perspective: -Man has long been interested in creating a machine in his own image -many mechanical dolls created in 18 th century (automates) -cultural: in the opera “The Tales of Hoffman,” the protagonist falls in love with a mechanical doll -What is AI? -From one perspective, AI is the study of automata (machines) that can: learn, understand, interpret, and arrive at conclusions in a manner considered intelligent, just as if it were being carried out by a human -Practical AI: beyond TT -some fear the economic goal of AI is to replace human workers with machine equivalents. -machines can out perform humans in intelligent tasks, such as chess and math, but cannot achieve the perceptive, reasoning, and manipulative capabilities of adult humans. -from AI perspective, machines current demonstrate intellect of low order insect in some aspects of perception and information processing. -Criteria for successful AI: 1) Does the application have a clearly defined task 2) Does the application solve a real world problem 3) Are extensions possible? Can we build on what the machine can do or are we going to have to restart. 4) Does the solution embody a well-defined architecture or organization, or is the result impressive but ad hoc and suitable only in a limited number of cases? -A Sampling of Applications: - Medical : image processing; diagnosis; rehabilitation. - Management : cost estimates, scheduling; intelligent document retrieval. : prediction of chemical reactions; chemical identifications; equipment configuration; system troubleshooting; circuit design. - Industrial: process control; mfg. quality control.
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- Financial/legal : investment strategies; prediction of financial trends; loan application analysis; real estate price evaluation; estate planning. - Military and Space : classification of fingerprints; computer security; signal/target recognition. - Other : language (natural language processing); speech recognition; prediction of sporting events; handwriting recognition; optical character recognition However, designing computer-based machines that are intelligent is not the same as building computers that simulate intelligence Ways cognitive theories help create useful machines:
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This note was uploaded on 09/17/2009 for the course ISYE/PSYC/ 3790 taught by Professor Arriaga during the Fall '08 term at Georgia Institute of Technology.

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Chapter 11 Outline - Chapter 11: TED TALKS Why don't we...

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