What Computers Still Can't Do A Critique of Artificial Reason

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Format: Paperback
Pub. Date: 1992-10-30
Publisher(s): The MIT Press
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Summary

When it was first published in 1972, Hubert Dreyfus's manifesto on the inherent inability of disembodied machines to mimic higher mental functions caused an uproar in the artificial intelligence community. The world has changed since then. Today it is clear that "good old-fashioned AI," based on the idea of using symbolic representations to produce general intelligence, is in decline (although several believers still pursue its pot of gold), and the focus of the Al community has shifted to more complex models of the mind. It has also become more common for AI researchers to seek out and study philosophy. For this edition of his now classic book, Dreyfus has added a lengthy new introduction outlining these changes and assessing the paradigms of connectionism and neural networks that have transformed the field. At a time when researchers were proposing grand plans for general problem solvers and automatic translation machines, Dreyfus predicted that they would fail because their conception of mental functioning was naive, and he suggested that they would do well to acquaint themselves with modern philosophical approaches to human beings. What Computers Can't Do was widely attacked but quietly studied. Dreyfus's arguments are still provocative and focus our attention once again on what it is that makes human beings unique. Hubert L. Dreyfus, who is Professor of Philosophy at the University of California, Berkeley, is also the author of Being-in-the-World. A Commentary on Heidegger's Being and Time, Division I.

Author Biography

Hubert L. Dreyfus is Professor of Philosophy at the University of California at Berkeley.

Table of Contents

Introduction to the MIT Press Edition
Acknowledgments
Introduction to the Revised Edition (1979)p. 1
Introductionp. 67
Ten Years of Research in Artificial Intelligence (1957-1967)p. 91
Phase 1 (1957-1962) Cognitive Simulation
Analysis of Work in Language Translation, Problem Solving, and Pattern Recognition
The Underlying Significance of Failure to Achieve Predicted Results
Phase II (1962-1967) Semantic Information Processingp. 130
Analysis of Semantic Information Processing Programs
Significance of Current Difficulties Conclusionp. 149
Assumptions Underlying Persistent Optimism
Introductionp. 155
The Biological Assumptionp. 159
The Psychological Assumptionp. 163
Empirical Evidence for the Psychological Assumption: Critique of the Scientific Methodology of Cognitive Simulation
A Priori Arguments for the Psychological Assumption
The Epistemological Assumptionp. 189
A Mistaken Argument from the Success of Physics
A Mistaken Argument from the Success of Modern Linguistics
The Ontological Assumptionp. 206
Conclusionp. 225
Alternatives to the Traditional Assumptions
Introductionp. 231
The Role of the Body in Intelligent Behaviorp. 235
The Situation: Orderly Behavior Without Recourse to Rulesp. 256
The Situation as a Function of Human Needsp. 272
Conclusionp. 281
Conclusion: The Scope and Limits of Artificial Reason
The Limits of Artificial Intelligence
The Future of Artificial Intelligencep. 285
Notesp. 307
Indexp. 346
Table of Contents provided by Blackwell. All Rights Reserved.

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