Rating: 
Summary: Thought-provoking account of a diverse field of research
Review: 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.
Rating: 
Summary: "Analogies" - Bucks the status quo in the field of AI !
Review: 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. What you end up with is a computer that knows a *lot* about a particular domain (i.e. chess), but has no other redeeming features whatsoever. Deep Blue could probably whip 99.9%
of the human population at chess, but it can't even begin recognize the elegance of a particular strategy (such as the sicilian defense) because it has no ability to make analogies to other domains.
The ongoing thread of Hofstadter's work has always been quite clear. He's interested in understanding
human thought, not mimicking it. In his latest work, Analogies, he and his FARGonaouts (students at his
Fluid Analogies Research Group - FARG) introduce us to several of their long term projects that uncover
some of the 'fundamental mechanisms of thought'.
His usual modus operandi is to examine the problem space of extremely simple microdomains - problem sets having very few parameters, but that scale up well into higher domains with the analogies it evokes. For instance, he describes
a very simple game called "TableTop" in which two players face each other across a table in a cafe. On both
sides of the table are arranged various objects of the TableTop domain - knives, spoons, cups, plates, salt
and pepper shakers, etc. The game begins when one player touches an object on their side of the table, saying "Do This", and the
other player then must touch a corresponding object on their side of the table which best mirrors the other person's
choice. The goal in each exchange is to choose the most appropriate corresponding object. Simple, right? Say I touch the coffee cup sitting in the middle of my placemat. You don't have a coffecup on your side. But you
do have a soup bowl there. You touch it. You've made an analogy. The soup bowl's physical arrangement on the
table was similar to the situation of the coffee cup, and the 'round container-ness' also made it a good match, even
though it was a totally different object. This simple microdomain affords us a lot of insight into the
process of analogy making. That is, the lessons learned in the TableTop domain can be used in other domains
with different details, but similar problem space.
For instance, the Battle-Op Domain, where, two geographical entities are pitted against each other:
(Excerpt from "Analogies")
A war breaks out between California and Indiana over the former's attempt to divert rain clouds from
soggy Indiana to the parched San Joaquin Valley. Unfortunatley, the conflict goes nuclear, and California
obliterates Bloomington. The war council in Indianapolis, wishing to be appropriately punitive but not risk further escalation, must then decide what Californian entity to annihilate in retaliation. Thus -
what is the Bloomington of California?
Given the act of agression committed by California, it would be nonsense to blast Los Angeles, a city with
a population over 100 times that of Bloomington. Attacking San Diego would be precluded because of its
world-famous zoo. And detonating an H-Bomb in the Pacific so as to cause a tidal wave to destroy Carmel would
be ruled out because an attack mounted on that jewel of a city would likely enrage Californians to a too-risky
degree. After some consideration, then, the war council might reason that the Hoosier Armed Forces would best
achieve 'the same result' not by destroying a city, but by offering all the migrant workers of California one dollar
an hour more to come and work in Indiana.
(Excerpt from "Analogies" ends.)
As you can see, lessons learned about analogy making in one domain can be easily mapped on to other domains of
problems. This is one of the uniquely human attributes of thought - that we can see analogies to things
we've experienced, and use those analogies to help us tackle new problems faced in other domains of life.
When a computer program can be endowed with this ability, then we'll be on the road to artificial intelligence.
The research outlined in Analogies is very intriguing and bucks the status quo in the field of AI at every
turn by focusing tightly upon the goal of understanding rather than mimicking human thought. If you ever find
yourself thinking about thinking - how we think and why we think, then I highly recommend you pick up a copy of Fluid Concepts and Creative Analogies: Computer Models of the Fundemental Mechanisms of
Thought and curl up by the fireplace with it soon!
Rating:
Summary: "Analogies" - Bucks the status quo in the field of AI !
Review: For a number of years now, I've followed the works of Douglas Hofstadter. I was instantly hookedwhen 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. What you end up with is a computer that knows a *lot* about a particular domain (i.e. chess), but has no other redeeming features whatsoever. Deep Blue could probably whip 99.9% of the human population at chess, but it can't even begin recognize the elegance of a particular strategy (such as the sicilian defense) because it has no ability to make analogies to other domains. The ongoing thread of Hofstadter's work has always been quite clear. He's interested in understanding human thought, not mimicking it. In his latest work, Analogies, he and his FARGonaouts (students at his Fluid Analogies Research Group - FARG) introduce us to several of their long term projects that uncover some of the 'fundamental mechanisms of thought'. His usual modus operandi is to examine the problem space of extremely simple microdomains - problem sets having very few parameters, but that scale up well into higher domains with the analogies it evokes. For instance, he describes a very simple game called "TableTop" in which two players face each other across a table in a cafe. On both sides of the table are arranged various objects of the TableTop domain - knives, spoons, cups, plates, salt and pepper shakers, etc. The game begins when one player touches an object on their side of the table, saying "Do This", and the other player then must touch a corresponding object on their side of the table which best mirrors the other person's choice. The goal in each exchange is to choose the most appropriate corresponding object. Simple, right? Say I touch the coffee cup sitting in the middle of my placemat. You don't have a coffecup on your side. But you do have a soup bowl there. You touch it. You've made an analogy. The soup bowl's physical arrangement on the table was similar to the situation of the coffee cup, and the 'round container-ness' also made it a good match, even though it was a totally different object. This simple microdomain affords us a lot of insight into the process of analogy making. That is, the lessons learned in the TableTop domain can be used in other domains with different details, but similar problem space. For instance, the Battle-Op Domain, where, two geographical entities are pitted against each other: (Excerpt from "Analogies")
A war breaks out between California and Indiana over the former's attempt to divert rain clouds from soggy Indiana to the parched San Joaquin Valley. Unfortunatley, the conflict goes nuclear, and California obliterates Bloomington. The war council in Indianapolis, wishing to be appropriately punitive but not risk further escalation, must then decide what Californian entity to annihilate in retaliation. Thus - what is the Bloomington of California? Given the act of agression committed by California, it would be nonsense to blast Los Angeles, a city with a population over 100 times that of Bloomington. Attacking San Diego would be precluded because of its world-famous zoo. And detonating an H-Bomb in the Pacific so as to cause a tidal wave to destroy Carmel would be ruled out because an attack mounted on that jewel of a city would likely enrage Californians to a too-risky degree. After some consideration, then, the war council might reason that the Hoosier Armed Forces would best achieve 'the same result' not by destroying a city, but by offering all the migrant workers of California one dollar an hour more to come and work in Indiana.
(Excerpt from "Analogies" ends.) As you can see, lessons learned about analogy making in one domain can be easily mapped on to other domains of problems. This is one of the uniquely human attributes of thought - that we can see analogies to things we've experienced, and use those analogies to help us tackle new problems faced in other domains of life. When a computer program can be endowed with this ability, then we'll be on the road to artificial intelligence. The research outlined in Analogies is very intriguing and bucks the status quo in the field of AI at every turn by focusing tightly upon the goal of understanding rather than mimicking human thought. If you ever find yourself thinking about thinking - how we think and why we think, then I highly recommend you pick up a copy of Fluid Concepts and Creative Analogies: Computer Models of the Fundemental Mechanisms of Thought and curl up by the fireplace with it soon!
Rating:
Summary: A Subdued Hoftadter, But Not a Bad Hofstadter
Review: For those who are familiar with Hofstadter's style in Godel Escher Bach, as well as Matamagical Themas, this might be a shock. The normally outrageous style of Hofstadter is quite subdued in this tome, as you are taken on a journey of his research projects. I enjoyed reading about the goals of he and his FARG collaborators. Even more interesting, to me at least, were his thoughts on the state of Artificial Intelligence... err I mean Cognitive Science, and what he believes are some fundamental flaws in the philosophies of many of his contemporaries.
If you're up to reading research-type papers on some very interesting projects, this book is very worthwhile.
Rating: 
Summary: Godel, Escher, Bach it's not.
Review: Having really liked Godel, Escher, Bach, I bought this book. I'm amazed I actually read the whole thing. I guess I expected eventually to reach some point to the inane research that is described ad nauseam in multiple permutations by the author. If this is the best that artificial intelligence research has been able to come up with after all these years, it's time to call the project a failure and go home
Rating: 
Summary: Disappointed
Review: 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.
Rating:
Summary: Disappointed
Review: 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.
Rating: 
Summary: Too distant from my usual routes ...
Review: 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.
Rating:
Summary: Definite Recommendation for Hofstadter's Book
Review: The book _Fluid Concepts and Creative Analogies_ by D.H. is definitely one of *the* most interesting books I've ever read. The book analyzes the intriguing topic of fitting computer programs with the capability to solve problems like humans do. Filled with useful analogies and creative diagrams to explain the advanced topic, the book keeps the reader interested. I believe that the book, and I admit I found some parts a bit boring, is the best attempt I've seen to explain the fascinating topic of AI. A sure 10
Rating:
Summary: Novel approaches to artificial intelligence
Review: 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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