What Computers Still Can't Do: A Critique of Artificial ReasonMIT Press, 1992年10月30日 - 408 頁 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. |
內容
Introduction to the MIT Press Edition | ix |
Acknowledgments | liii |
Introduction | 67 |
Ten Years of Research in Artificial | 89 |
Simulation | 91 |
Phase II 19621967 Semantic Information Processing | 130 |
Conclusion | 149 |
Introduction | 155 |
The Ontological Assumption | 206 |
Conclusion | 225 |
Introduction | 231 |
Orderly Behavior Without Recourse | 256 |
The Situation as a Function of Human Needs | 272 |
Conclusion | 281 |
Notes | 307 |
| 346 | |
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常見字詞
Allen Newell ambiguity tolerance analogue analysis artificial intelligence assumption Bobrow brain chess cited in note claim Cognitive Simulation complex concepts context context-free defined described determinate difficulties digital computer domain Edward Feigenbaum everyday example experience explicit facts Feigenbaum formal function gestalt Gestaltists global goals GOFAI H. A. Simon heuristic program heuristics Ibid information-processing input intelligent behavior involved knowledge learning Lenat logical MacHack machine Marvin Minsky meaning means-ends analysis mechanical micro-world mind Minsky Minsky's natural language Neisser Newell and Simon objects Oettinger operations organization Papert pattern recognition perception performance philosophical physical physical symbol system play possible principle problem solving psychology question Quillian reason recognize representation robot rules Schank seems Semantic Information Processing sense Seymour Papert Shaw SHRDLU significance simple situation sort specific structure success symbolic Terry Winograd theory tion traits understanding Weizenbaum Winograd Wittgenstein workers

