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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.

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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
Format:Paperback
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
Format:Paperback
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 Wonderful but quite dry in parts April 18 2004
Format:Paperback
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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5.0 out of 5 stars Novel approaches to artificial intelligence Nov. 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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5.0 out of 5 stars Artificial Intelligence, Redefined Oct. 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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