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Rating: Summary: Redefining what artificial intelligence is all about Review: Melanie Mitchell's analogy-making as perception is a remarkably original book. It documents an artificial intelligence project known as copycat, which was implemented as the author's PhD project under Douglas Hofstadter.Copycat is unlike anything in artificial intelligence. It is not a symbolic system, neither a connectionist one. The major goal of the project is to study the nature of concepts. Concepts, as we all know, are flexible, context-sensitive creatures. For instance, DNA has nothing to do with a computer program, but there is a sense on which we can see DNA as a computer program that guides embrionary development. DNA can also be seen as a zipper, as it "zips down" in two parts (for cell reproduction). Still another view would be DNA as a will, for it carries valuable hereditary "property". Now, DNA is in truth just a molecule, and nothing else. The question is, how can we see the same thing (such as DNA) as so many different things? Moreover, how can these fluid context-sensitive concepts be implemented in rigid, rule-obeying computers? To which the answer is: what we view is the abstract roles that DNA plays in embrionary development, cell division, and in individual reproduction. And this is the very idea of "Analogy-making as perception". Well, not so fast. The copycat project is not designed to grasp such extremely complex subjects as DNA, but, on the other hand, it presents a computational architecture that suggests what the nature of concepts is like, and how flexible concepts may emerge from inflexible mechanisms. Copycat can solve analogy problems such as abc->abd:ijk-> ?. But it is not restricted to trivial ones. Consider the following analogy: abc ->abd:xyz->?. How would you solve it? How do you think that copycat solves it? Obviously, this project doesn't fit in very easily in classical artificial intelligence, as it attacks some of the most pervasive ideas of the field, such as the separation of perception and cognition. In fact, I think this book redefines the major questions of artificial intelligence (and although Mitchell does not state it, I think the copycat model does not fall prey to either the frame problem or to the symbol grounding problem). It is very unfortunate that this is not one of the best-selling books in AI. But I believe that it will ultimately make its mark on the History of the field, if for no other reason than it simply is the right approach to genuine intelligence and authentic understanding. Should one day Amazon.com let me give a 6-star to a book, but charge me a dollar for giving it, this is one that would definitely deserve to be such a 6-star. ============================================ PS. I would also recommend Hofstadter's Fluid Concepts and Creative Analogies; and Robert French's Subtlety of Sameness.
Rating: Summary: Redefining what artificial intelligence is all about Review: Melanie Mitchell's analogy-making as perception is a remarkably original book. It documents an artificial intelligence project known as copycat, which was implemented as the author's PhD project under Douglas Hofstadter. Copycat is unlike anything in artificial intelligence. It is not a symbolic system, neither a connectionist one. The major goal of the project is to study the nature of concepts. Concepts, as we all know, are flexible, context-sensitive creatures. For instance, DNA has nothing to do with a computer program, but there is a sense on which we can see DNA as a computer program that guides embrionary development. DNA can also be seen as a zipper, as it "zips down" in two parts (for cell reproduction). Still another view would be DNA as a will, for it carries valuable hereditary "property". Now, DNA is in truth just a molecule, and nothing else. The question is, how can we see the same thing (such as DNA) as so many different things? Moreover, how can these fluid context-sensitive concepts be implemented in rigid, rule-obeying computers? To which the answer is: what we view is the abstract roles that DNA plays in embrionary development, cell division, and in individual reproduction. And this is the very idea of "Analogy-making as perception". Well, not so fast. The copycat project is not designed to grasp such extremely complex subjects as DNA, but, on the other hand, it presents a computational architecture that suggests what the nature of concepts is like, and how flexible concepts may emerge from inflexible mechanisms. Copycat can solve analogy problems such as abc->abd:ijk-> ?. But it is not restricted to trivial ones. Consider the following analogy: abc ->abd:xyz->?. How would you solve it? How do you think that copycat solves it? Obviously, this project doesn't fit in very easily in classical artificial intelligence, as it attacks some of the most pervasive ideas of the field, such as the separation of perception and cognition. In fact, I think this book redefines the major questions of artificial intelligence (and although Mitchell does not state it, I think the copycat model does not fall prey to either the frame problem or to the symbol grounding problem). It is very unfortunate that this is not one of the best-selling books in AI. But I believe that it will ultimately make its mark on the History of the field, if for no other reason than it simply is the right approach to genuine intelligence and authentic understanding. Should one day Amazon.com let me give a 6-star to a book, but charge me a dollar for giving it, this is one that would definitely deserve to be such a 6-star. ============================================ PS. I would also recommend Hofstadter's Fluid Concepts and Creative Analogies; and Robert French's Subtlety of Sameness.
Rating: Summary: THE insightful project on machine perception Review: Since AI researchers are generally engineers, they historically did what engineers do: they broke up the mind in very clear-cut divisions, one for the perception of the things out there in the world, and another, symbolically, to do "abstract cogitation". For deep reasons, this was an invalid move, but only a few could see it. Melanie surely could, for her highly original copycat project exhibits some of the best insights in Artificial Intelligence ever. AI is still so much pervaded with the wrong ideas that this book will need to take some time to make its definitive mark on the history of the field. If genuine understanding is ever to be built into a machine, understanding of the kind that Searle's gang will be forever denying, then it will come from an architecture similar to that proposed in this book. Then again, I could turn out to be wrong. But let us let History decide on this issue.
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