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Analogy-Making as Perception: A Computer Model [Hardcover]

Melanie Mitchell (Author)
5.0 out of 5 stars  See all reviews (3 customer reviews)


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Book Description

0262132893 978-0262132893 May 14, 1993
The psychologist William James observed that "a native talent for perceiving analogies is ... the leading fact in genius of every order." The centrality and the ubiquity of analogy in creative thought have been noted again and again by scientists, artists, and writers, and understanding and modeling analogical thought have emerged as two of the most important challenges for cognitive science.

Analogy-Making as Perception is based on the premise that analogy-making is fundamentally a high-level perceptual process in which the interaction of perception and concepts gives rise to "conceptual slippages" which allow analogies to be made. It describes Copycat - a computer model of analogymaking, developed by the author with Douglas Hofstadter, that models the complex, subconscious interaction between perception and concepts that underlies the creation of analogies.

In Copycat, both concepts and high-level perception are emergent phenomena, arising from large numbers of low-level, parallel, non-deterministic activities. In the spectrum of cognitive modeling approaches, Copycat occupies a unique intermediate position between symbolic systems and connectionist systems a position that is at present the most useful one for understanding the fluidity of concepts and high-level perception.

On one level the work described here is about analogy-making, but on another level it is about cognition in general. It explores such issues as the nature of concepts and perception and the emergence of highly flexible concepts from a lower-level "subcognitive" substrate.

Melanie Mitchell, Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan, is a Fellow of the Michigan Society of Fellows. She is also Director of the Adaptive Computation Program at the Santa Fe Institute.


Product Details

  • Hardcover: 284 pages
  • Publisher: The MIT Press (May 14, 1993)
  • Language: English
  • ISBN-10: 0262132893
  • ISBN-13: 978-0262132893
  • Product Dimensions: 10.3 x 8.3 x 0.9 inches
  • Shipping Weight: 2.1 pounds
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (3 customer reviews)
  • Amazon Best Sellers Rank: #2,921,670 in Books (See Top 100 in Books)

More About the Author

Melanie Mitchell was born in Los Angeles, California. She attended Brown University, where she majored in mathematics and did research in astronomy, and the University of Michigan, where she received a Ph.D. in computer science, working with her advisor Douglas Hofstadter on the Copycat project, a computer program that makes analogies. She is currently Professor of Computer Science at Portland State University and External Professor at the Santa Fe Institute, and does research in the fields of artificial intelligence, cognitive science, and complex systems. She lives in Portland, Oregon with her husband and two sons. Melanie blogs about complexity at http://exploringcomplexity.blogspot.com

 

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20 of 20 people found the following review helpful:
5.0 out of 5 stars Redefining what artificial intelligence is all about, February 17, 2000
This review is from: Analogy-Making as Perception: A Computer Model (Hardcover)
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.

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2 of 2 people found the following review helpful:
5.0 out of 5 stars Amazing, September 22, 2006
By 
E. Nichols (Bloomington, IN USA) - See all my reviews
(REAL NAME)   
This review is from: Analogy-Making as Perception: A Computer Model (Hardcover)
I can't believe I waited so long to read this book -- it truly is a classic. This is the way AI should be done: by focusing on the right level of abstraction, situated above the level of neuroscience but below the level of simple input-output function mapping. Finally, a computer model that makes those first steps towards doing the same thing that people do.

True believers in those original goals of artificial intelligence take heart -- this book gives new hope to a field that has come to be dominated by engineering approaches that only work in special cases like the logic behind the cruise control switch in a car. Mitchell's model provides the fluidity and flexibility that is lacking from classical machine learning techniques.
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6 of 8 people found the following review helpful:
5.0 out of 5 stars THE insightful project on machine perception, February 5, 2000
This review is from: Analogy-Making as Perception: A Computer Model (Hardcover)
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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