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(More customer reviews)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.
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PS. I would also recommend Hofstadter's Fluid Concepts and Creative Analogies; and Robert French's Subtlety of Sameness.
Click Here to see more reviews about: Analogy-Making as Perception: A Computer Model (Neural Network Modeling and Connectionism)
The psychologist William James observed that "a native talent forperceiving analogies is... the leading fact in genius of every order." Thecentrality and the ubiquity of analogy in creative thought have been noted again andagain by scientists, artists, and writers, and understanding and modeling analogicalthought have emerged as two of the most important challenges for cognitivescience.Analogy-Making as Perception is based on the premise that analogy-making isfundamentally a high-level perceptual process in which the interaction of perceptionand concepts gives rise to "conceptual slippages" which allow analogies to be made.It describes Copycat - a computer model of analogymaking, developed by the authorwith Douglas Hofstadter, that models the complex, subconscious interaction betweenperception and concepts that underlies the creation of analogies.In Copycat, bothconcepts and high-level perception are emergent phenomena, arising from largenumbers of low-level, parallel, non-deterministic activities. In the spectrum ofcognitive modeling approaches, Copycat occupies a unique intermediate positionbetween symbolic systems and connectionist systems a position that is at present themost useful one for understanding the fluidity of concepts and high-levelperception.On one level the work described here is about analogy-making, but onanother level it is about cognition in general. It explores such issues as thenature of concepts and perception and the emergence of highly flexible concepts froma lower-level "subcognitive" substrate.Melanie Mitchell, Assistant Professor in theDepartment of Electrical Engineering and Computer Science at the University ofMichigan, is a Fellow of the Michigan Society of Fellows. She is also Director ofthe Adaptive Computation Program at the Santa Fe Institute.
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