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The Measure of All Minds: Evaluating Natural and Artificial Intelligence

The Measure of All Minds: Evaluating Natural and Artificial Intelligence (Hardback)

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Synopsis

Are psychometric tests valid for a new reality of artificial intelligence systems, technology-enhanced humans, and hybrids yet to come? Are the Turing Test, the ubiquitous CAPTCHAs, and the various animal cognition tests the best alternatives? In this fascinating and provocative book, Jose Hernandez-Orallo formulates major scientific questions, integrates the most significant research developments, and offers a vision of the universal evaluation of cognition. By replacing the dominant anthropocentric stance with a universal perspective where living organisms are considered as a special case, long-standing questions in the evaluation of behavior can be addressed in a wider landscape. Can we derive task difficulty intrinsically? Is a universal g factor - a common general component for all abilities - theoretically possible? Using algorithmic information theory as a foundation, the book elaborates on the evaluation of perceptual, developmental, social, verbal and collective features and critically analyzes what the future of intelligence might look like.

Computing & ITComputer scienceArtificial intelligenceNatural language & machine translationEducationEducation TheoryEducational psychologyPhilosophy, Psychology & Social SciencesPhilosophy TheoryPhilosophy of mind Publisher: Cambridge University Press Publication Date: 11/01/2017 ISBN-13: 9781107153011  Details: Type: Hardback Format: Books
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Jose Hernandez-Orallo is Professor of Information Systems and Computation at the Universitat Politecnica de Valencia, Spain. He has published four books and more than a hundred articles and papers on artificial intelligence, machine learning, data mining, cognitive science, and information systems. His work in the area of machine intelligence evaluation has been covered by both scientific and popular outlets, including The Economist and New Scientist. He pioneered the application of algorithmic information theory to the development of artificial intelligence tests.

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