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Fluid Concepts And Creative Analogies: Computer Models Of The Fundamental Mechanisms Of Thought [Paperback]

Douglas R. Hofstadter
3.9 out of 5 stars  See all reviews (9 customer reviews)
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Book Description

March 22 1996 0465024750 978-0465024759
Readers of earlier works by Douglas Hofstadter will find this book a natural extension of his style and his ideas about creativity and analogy; in addition, psychologists, philosophers, and artificial-intelligence researchers will find in this elaborate web of ingenious ideas a deep and challenging new view of mind.

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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.

Customer Reviews

Most helpful customer reviews
1 of 1 people found the following review helpful
3.0 out of 5 stars Too distant from my usual routes ... April 22 2004
Many books by D. Hofstadter are at the top standings of my personal parade, but in reading this book I found myself very likely too distant from my usual interests and preferred styles. The initial part is very interesting, but when the author carries on detailed descriptions about programs' features in conversational shape, I have been quickly bored, and I have given up attentive reading turning to an eagle eye approach. I would have been by far more comfortable with a more formal explanation, because, once I make the effort to follow the thourough description of what and how a program does, it is more convenient to study its algorithms.
So, the book is surely very pleasing for people professionally involved in semantics, but I am not confident in its general interest.
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1 of 1 people found the following review helpful
Douglas Hofstadter is best known for his seminal work 'Godel, Escher, Bach' (1981), but not much was known about the work he carried out at the University of Indiana. This work collects a number of research papers from the 80s, thus offering a glimpse into the continuation of the work that was carried out with the help of the 'fluid concepts'-group. Hofstadter writes well, which means that the accounts of the projects that were undertaken are exciting, thought-provoking, and intruiging. I'm not entirely happy about the theoretical background to some of the work, maybe Hofstadter tries too deliberately to maintain things at a simple level. Still, if you're at all interested in the state of the art in AI research, this is a book you may not want to miss.
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5.0 out of 5 stars Novel approaches to artificial intelligence Nov. 26 2001
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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5.0 out of 5 stars Artificial Intelligence, Redefined Oct. 2 2000
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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2.0 out of 5 stars Disappointed Sept. 26 2000
I read G.E.&B. and had very high hopes for this book. I read a review of this book where it talked about the work he and his group did looking at analogies and I thought wow, this was going to be killer. For example, his group wrote a computer program to solve problems like "What comes next in this series? 2,4,7,8,20...", and looked at things like "What is the Hackensack NJ of Nebraska?" (more interesting than it seems), and "I touch this coffee cup in front of me on the table, now you do the same" (to a person who might not have a coffee cup near them), and others.
His point being that analogy making is the heart of human intelligence.
So I settled down with this 400 page tome and had great expectations of many wonderful evenings ahead of me. FORGET IT. The interesting bits in the books can fill 5 to 10 pages and the rest of the book is filled with talk about their computer program implementations of these ideas. And after the first program is written all the other ones are direct offshoots of it without much new work so it gets pretty monotomous pretty quickly. Oh sure, to sit down with Mr. H and discuss these things one evening over beers would be *amazing*, but to have to slog through this book is not.
If you really want a book that will blow your mind, check out "Consciousness Explained" by Daniel Dennett. That books is powers of 10 greater in intellectual amazement than this book ever hoped to be.
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