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Fluid Concepts And Creative Analogies: Computer Models Of The Fundamental Mechanisms Of Thought Paperback – March 22, 1996

ISBN-13: 978-0465024759 ISBN-10: 0465024750 Edition: 1st

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Product Details

  • Paperback: 528 pages
  • Publisher: Basic Books; 1 edition (March 22, 1996)
  • Language: English
  • ISBN-10: 0465024750
  • ISBN-13: 978-0465024759
  • Product Dimensions: 1.2 x 7.2 x 9.7 inches
  • Shipping Weight: 2.4 pounds (View shipping rates and policies)
  • Average Customer Review: 4.3 out of 5 stars  See all reviews (20 customer reviews)
  • Amazon Best Sellers Rank: #324,500 in Books (See Top 100 in Books)

Editorial Reviews

Amazon.com Review

Douglas Hofstadter, best known for his masterpiece Godel, Escher, Bach: An Eternal Golden Braid, tackles the subject of artificial intelligence and machine learning in his thought-provoking work Fluid Concepts and Creative Analogies, written in conjunction with the Fluid Analogies Research Group at the University of Michigan. Driven to discover whether computers can be made to "think" like humans, Hofstadter and his colleagues created a variety of computer programs that extrapolate sequences, apply pattern-matching strategies, make analogies, and even act "creative." As always, Hofstadter's work requires devotion on the part of the reader, but rewards him with fascinating insights into the nature of both human and machine intelligence.

About the Author

Douglas R. Hofstadter is College Professor of Cognitive Science and Computer Science at Indiana University, Bloomington, Indiana. His previous books are the Pulitzer Prizewinning Gödel, Escher, Bach; Metamagical Themas, The Mind’s I, Fluid Concepts and Creative Analogies, Le Ton Beau de Marot, and Eugene Onegin.

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Customer Reviews

Read whatever interests you, and think about it for a while.
David R. Maass
This book is, as others have commented, different from DH's other more entertaining books.
Tim Josling
It definitely makes a clear distinction between "expert systems" and proper intelligence.
J. Schmuecker

Most Helpful Customer Reviews

73 of 76 people found the following review helpful By A. Linhares on October 2, 2000
Format: Paperback
Where does meaning enter the picture in artificial intelligence? How can we say that a machine possesses understanding? Where, and how, does such understanding happen? These are among the deepest and hardest questions faced by the field, which, as many skeptics claim, has not yielded much about them so far. Consider, for instance, that most current research in AI can be roughly classified over two distinct classes:
(1) Low-level perception. The best example of this type of work comes obviously from computer vision systems. These systems, given a set of input images, usually extract some important information from this input, generating, well, other images (i.e. depth image, edge contours etc.). But this extracted information is usually on a still very low, meaningless, level, to be used by, for instance, a theorem-proving system. To make it clear to all readers what is meant by "meaning", consider the information-processing that must occur whenever an animal, given its massive sensorial information, perceives danger. Going from a set of images and sounds to a feeling of danger involves extracting meaning from the original input, and this is not what is done by current low-level perception projects. It is almost as if these perceptual processes "delegate" the extraction of meaning to another upcoming process. To get into the meaning of a situation, low-level perceptual processes are not enough; there is a clear need for further perceptual processing.
(2) GOFAI symbolic manipulation. This is the other side of the AI coin, dubbed by philosopher John Haugeland as GOFAI, for "good-old-fashioned artificial intelligence", where programs usually handle (syntactically) a representation that supposedly should have been formed by a perceptual process.
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37 of 38 people found the following review helpful By David R. Maass on November 26, 2001
Format: Paperback
This book has received some poor reviews and been unfairly compared to Hofstader's previous book, Goedel, Escher, Bach. While both are books about cognitive science, the former is a book of philosophy -- it's written for the layperson and discusses the topic in relatively abstract terms. This book is no less interesting for the fact that it deals in concretes: it discusses the actual architecture, the design of the programs which simulate the intelligent processes described so well in GEB. Those with a background in computer programming will especially appreciate the novelty of Hofstadter's architecture, and will perhaps be inspired to implement their own. Those without a background probably won't have any trouble visualizing the processes for themselves. The book is written as a collection of essays, so my recommendation is: skip around. Read whatever interests you, and think about it for a while. This book is neither a narrative nor an exhaustive reference, and you won't enjoy it if you try to read it as either.
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34 of 35 people found the following review helpful By A Customer on January 5, 1997
Format: Paperback
For a number of years now, I've followed the works of Douglas Hofstadter. I was instantly hooked
when I first read his column Metamagical Themas, which ran in Scientific American from 1981 through 1983.
In that column, he tackled all manner of thought provoking subjects. In the interveneing years, he
has released some pretty meme-rich tomes, none for the faint of heart. From the far-out thought
experiments of The Minds Eye to the Pulitzer Prize winning Godel, Escher, Bach: An Eternal Golden Braid,
to his latest (reviewed here), Mr. Hofstadter always keeps the reader on his or her mental toes.

Many researchers in the field of Artificial Intelligence take the approach of attempting to mimick the
behavior of people with computer programs. On the surface, this might seem a logical direction to take, and so
AI researchers have a tendency to go and dream up batteries of tests that aim to characterize some area
of human behavior, then the sum up all the results and come up with the range of responses that fits cozily
into their bell-shaped curves. Armed with what they've assured themselves is normal human response to
all their scenerios, the go off and attempt to write computer programs that react the same way as John or
Jane Doe did. Once they've gotten a program that generally responds like 'most of the human subjects' did,
they usually beef it up by programming in more and more details about the domain of the scenerio at hand.
A good example of this line of thought is
Deep Blue, IBM's massively parallel chess playing supercomputer.

What Douglas Hofstader's latest book points out is that this sort of thinking about artificial intelligence is
the brute force approach.
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18 of 18 people found the following review helpful By Tim Josling on April 18, 2004
Format: Paperback Verified Purchase
This book is, as others have commented, different from DH's other more entertaining books.
It is a serious attempt to discuss the real issues and difficulties with AI research. There is a lot of quite dry material and in places it is repetitive.
It provides terrific insight into the problem of imitating human thinking at a deep level, and I found it very rewarding. It was also very interesting to follow the threads of how he went about doing research, and what he thought of other AI research.
His views of various flavours of AI research were very instructive and inightful I thought.
In summary a good book, but this is not (high quality) brain candy like Godel Escher Bach etc.
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