Fluid Concepts and Creative Analogies by Douglas R. Hofstadter

Fluid Concepts and Creative Analogies Reviews

• 
  Matt

  In this book Douglas Hofstadter and his colleagues from the FARG / Fluid Analogies Research Group give away details of their findings on computer’s ability to make analogies, creativity, and what is called “fluid concepts”. There’s a couple of programs the “FARGonauts” developed over the years. The different chapters had been published before in science magazines but received an overhaul for this book. There are also newly written prefaces to each chapter and a very interesting epilogue called Creativity, Brain Mechanisms, and the Turing Test.
  
  The book was first published in 1991 which, in computer terms, is ages ago. One can assume that in the meantime many new insights have been uncovered. Nevertheless I think the concepts and ideas presented here are still relevant today. There are some intriguing approaches to imitating human cognition into a program. In particular the formation of analogies through programs and the “slippage” of concepts are very revealing. The systems presented here all operate on so-called micro-domains, that is, on tiny sections of the virtually infinite real world.
  
  For example the program called Copycat operates on letters only and is able to give answers to problems of the following kind:

  Suppose the letter-string abc were changed to abd; how would you change the letter-string ijk in “the same way”?

  This does not sound like much, but it is a very interesting and wide field, if you take a closer look at it. The general idea is also addressed by Melanie Mitchell, a co-author and developer of Copycat in this video of a lecture, which I highly recommend:
  
  https://www.youtube.com/watch?v=I1ay-...
  This video is from 2015, which leads me to believe that the themes and general architecture of the programs described in this book are still relevant, and my time reading it wasn’t wasted after all.
  
  The problem above looks like some question from an IQ test, but in fact it’s not. There is no right or wrong answer, there’s only answers that are more elegant and “deep” (one of Hofstadter’s favorite expressions) than others. Humans, when faced with this sort of problem usually start building analogies that help them find a rule behind the given letter-change, and apply this rule to the other string. In this case there are several possible rules one can think of:
  
  1) Change the third letter to d, so that ijk becomes ijd which doesn’t seem very appealing.
  
  2) Change everything to abd, so that ijk becomes abd which is even less subtle or elegant (at least to me, it might be different for the current US president)
  
  3) Change every occurrence of c to d, so that ijk won’t change at all. This, I think, seems a little better than above, but is still not satisfactory.
  
  4) Finally; change the last letter to its successor in the alphabet, so that ijk becomes ijl. That’s the answer most people think of right away. But why is that the case? Because of the analogy you discover between the “rising” string abc and ijk and the knowledge that d comes after c in the alphabet.
  
  Here’s another problem:

  Suppose the letter-string abc were changed to abd; how would you change the letter-string xyz in “the same way”?

  This is rather similar to the problem above, but it obviously has some obstacle built into it. The concept “successorship” doesn’t work for the last letter of xyz anymore. Copycat (at least some of the times) offers wyz as an answer. This might look strange at first, but it’s actually a rather deep answer. The program has discovered the rising sequence of letters a-b-c, and the change to the successor in the last position. It then slipped these concepts and instead of going up from left to right and change the last letter to its successor it is now going down from right to left and changes the first letter to its predecessor. How is that for analogy making?
  
  There are a couple more programs like that presented in the book. This is all done without any maths or actual program code. So laypeople should have no problem following Hofstadter and his colleagues’ reasoning.
  
  This was actually the first book I read about artificial intelligence, AI, and the possibility to mimic human cognition. There’s a lot of talk about AI and “intelligent machines” and how those might overcome humans in the future, the so called Technological Singularity, that is the time when a artificial superintelligence emerges. I think this scenario is still far down the road, if it comes at all. Unless some very clever people have some very clever concepts hidden somewhere in a drawer I don’t think computers will achieve human intelligence anytime soon. Today there are “neural nets”, of cause, and “deep learning” and there’s great progress in these fields, but, to me and to Hofstadter as well, those have little to do with intelligence and human cognition. This is only the simulation of a rather low layer in perception (the neurons) and a neural net seems even less aware of the concepts it’s dealing with than any ordinary program, like, for instance, a word processor, whereas programs like Copycat & Co seem to be more like the real deal when it comes to actual thinking agents.
  
  _______________________
  
  A little tidbit: It seems that Fluid Concepts and Creative Analogies was the very first book ever sold by Amazon:
  https://en.wikipedia.org/wiki/Amazon....
  
  
  
  This work is licensed under a
  Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.
• 
  Maurizio Codogno

  Non è facile commentare un libro di cui sono il traduttore, e nel quale sono citato e ringraziato nella postfazione...
  Il libro è "di lavoro", nel senso che è formato da vari capitoli che raccontano i progetti portati avanti dal gruppo di ricerca sulle scienze cognitive guidato da Hofstadter. I lavori si basano sul cercare di vedere come un sistema possa riuscire a trovare delle analogie su ambienti molto specializzati: ad esempio, trovare l'elemento successivo di una serie di numeri, oppure rispondere alla domanda "se abc diventa abd, allora cosa diventerà bcd?" o ancora, come riuscire a disegnare le lettere mancanti in un alfabeto costruito con una serie di segmenti.
  Tutti questi problemi non hanno una soluzione univoca nemmeno per noi, e costituiscono pertanto una sfida che a volte riesce, e altre volte no.
  Per chi non ama questi temi il libro può pertanto risultare pesante, anche se la prosa di Hofstadter è sempre piacevole e infarcita di giochi linguistici che ho per quanto possibile conservato oppure adattato nella versione italiana.
• 
  Mommalibrarian

  Today I officially cry UNCLE! I read and skimmed 273 of 491 pages. The subject is Artificial Intelligence. The author builds a series of computer programs to solve rigorously defined but not automatic-t-solve logic games. We get to play these games with pencil and paper but do not see the actual code. There is a lot of discussion of the problems other AI researchers have chosen and whether the solutions they have come up with constitute AI to any degree. Some chapters are, verbatim, papers which the author has previously published in scientific journals. It is heavy sledding and I did not find enough illumination for my feeble intellect to invest further time. I picked the book up because I own it. I cannot remember when or why I purchased it. I am making an effort to read the books I own now. We will see how long that can last.
• 
  Eric Hamen

  One of the denser of Hofsteader's books. Ruminations on thought vs program. Not light reading but no more difficult that Godel Esher Bach. Took me a good while to finish but am very glad I did.
• 
  Eliza

  I picked up FC + CA after being completely blown away by Hofstadter's acclaimed GodelEscherBach. However, I was admittedly a bit disappointed by this book. Though Hofstadter warns in the preface that this book is meant to contain overlap between the chapters, I found the repetitive nature of this book a bit of a roadblock to someone who was determined to read it cover to cover. Though, the nuances found between each research project and how a group of core ideas could be applied in a multitude of different variations was of some interest.
  
  I decided to ultimately promote this rating to 4 stars over 3 stars because of Hoftstadter's commentary on "hollow" connectionist models. As neural networks continue to be the hot topic in today's ML/AI communities, Hofstadter's criticism that there are merely quite skilled in manipulating language and not truly understanding the concepts/data they work with is worth thinking on. To use his technical jargon, they succumb to the ELIZA effect (presenting the illusion of being more intelligent than they really are) and would surely not pass the Turing test! However, since FC + CA was written 30 years agao, his concept of neural networks is a bit outdated. It would be interesting to hear his take on the modern Generative Adversarial Networks, as Hofstatder makes the bold claim that connectionist networks will never be able to generate unique compositions in the style of known works!
  
  Hofstadter proposes architecture that [he claims] does not fall prey to the weakness of the ELIZA effect. Whether or not Hofstadter is truly successful in his claim of higher intelligence is up to the reader (though I think I would grant that the answer is yes), his unique way of thinking, execution, and presentation is a welcome beam of light in academia. I found that chapter 3, the description of the Jumbo model to solve anagrams, to be of most interest, since it contained one of the more detailed technical descriptions of its implementation (spoiler, he does not use an exhaustive brute force method to solve!). His proposed parallel terraced scan + idea of many minute stochastic decisions creating a culminative deterministic effect is quite flexible and could be used in many areas of research outside of his own.
  
  Hoftstadter is faithful to his primary goal of modeling and respecting the emergent properties of human creativity in a system, and not just focusing solely on the end product of his programs. Hofstadter at his core is a cognitive scientist. Much of his critique on neural networks stems from the fact that they do not respect the central feedback loop of creativity that humans are gifted with and do not clarify the inner workings of the human mind. Is this true?
• 
  Katherine Green

  How to build a fragment of something that does something quite a bit like actual thinking, at least about analogy puzzles.
• 
  Brian Powell

  Provocative exploration of computer models of human creativity. Given today's strong emphasis on perception within AI, Hofstadter's book is a refreshing departure into the mysteries of mental fluidity and analogy, key bellwethers of human intelligence.
  
  This book is a collection of articles by Hofstadter and his fellow researchers that describe a series of computer programs developed to model the mechanics of analogy-making and creativity. At times I'd lament the level of detail, thinking "I don't really need to know how this specific program does what it does to understand how human minds identify the key concepts underlying novel analogies"; but, in hindsight the level of depth is really spot on -- without it, the whole project would have been too high-level and hand-wavy and unconvincing.
  
  This work stands in contrast to much modern-day work in AI (like deep learning), which, importantly, does not attempt to model human thought and perception. Though much influenced by human neurology, these systems are developed to achieve some particular engineering task (e.g. recognize stop signs, or translate spoken French to English), not to emulate any particular human mental characteristic. Even generative connectionist models, which might be considered "creative", are vastly different from the kinds of models discussed in this book, which really try to get at *how* human minds originate ideas. Unless novel ideas and analogies are truly randomly sampled from complicated distributions in our brains, then human creativity is *not* well-modeled by generative networks (though, indeed, it seems at best to be well *imitated* by them). It seems likely that Hofstadter's take, which is utterly original and skirts the boundaries of traditional connectionist and symbolic AI, is the more believable proposal.
  
  This work occupies an important (and increasingly less-explored) corner of the AI enterprise, and remains a key contribution to the field.
• 
  Dax

  Ugh. Note that I started this book in 2006 and am still trying to get through it.
  Douglas Hofstadter is most famouse for
  Godel, Escher, and Bach and I haven't gotten through that one fully yet either. However, I am intrigued by his continued search for "I". He has dedicated his life to unraveling how we think and looked at it from the lens of various fields: physics, philosophy, biology, mathematics, and, in this novel, computer science. I appreciate Hofstadter because his breadth is tremendous. I'm reminded of a
  Richard Feynman or even a
  Robert Pirsig although I think both are probably better writers.
• 
  Michael Guyer

  Thought about giving this 3 stars, but because I find Hofstadter's writing so readable I bumped it up to 4. Several interesting projects are detailed and discussed in this collection, and I largely agree with Hofstadter's assessment about what constitutes real decision making in a program designed to carry out some "intelligent" task such as analogy making, but the projects and their underlying architectures are all quite similar and thus grow a bit boring to read about by the end. This does serve to reinforce many of the key ideas of these projects, but as a reader it became tedious to finish. I feel as though I could have taken just as much away from this book if I had read a handful of chapters instead of reading it from cover to cover.
• 
  Dave Peticolas

  A collection of papers authored by Hofstadter and the members of his Fluid Analogies Research Group, this book presents the results of many years work in cognitive science research.

  Also included are a couple papers pointedly criticizing some other approaches in the field, the main criticism being a failure to model human cognition in a realistic way.

  Hofstadter is always worth reading and this collection is no exception to the rule.
• 
  Frank

  In depth look at some of Professor Hofstadter's recent and very original research. There is also an essay raising some interesting points about the difficulty in assessing the quality of work in artificial intelligence. (Prof H is too diplomatic to say so, but he obviously questions the promise and usefulness of many standard fields of exploration).
• 
  Philip Chaston

  A long book, a month's read on the morning train after the Economist and a bridge between instantiating puzzles within microdomains and how we are creative. Exploring his programs reminds me of how we should stop, take stock and understand that simple questions, a child's questions, are keys to unlocking the insight of how we use analogy, metaphor and the new.
• 
  Klenk

  The focus on small hard problems in tiny domains is something I am growing more sympathetic too. Also, his indictment of symbolic cognitive science is a must read for people who do work in the field.
• 
  Frank

  interesting
• 
  Gavin

  One of a pile of Mind books I grabbed desperately for a first-year philosophy essay. Did not understand it (naturally that didn't stop me citing it). Will have another go some day
• 
  David Jacobson

  As in all of his books I've read, Douglas Hofstadter (and co-authors) raise fascinating questions about the nature of human thought as a high-level language of the brain. Here they present a series of (now-outdated) artificial intelligence experiments in which they attempt to model human cognition in drastically restricted domains; e.g., the domain of word-scramble puzzles, the domain of numerical sequences, etc. In doing so, they expose the myriad complexities of modeling creative thought even in such simple cases; they point out, both implicitly and explicitly, that AI projects that claim to tackle larger, "real life" domains (they give examples from the time, but modern examples based on "machine learning" abound) must give up any attempt at genuinely modeling human thought processes in order to make any progress. In light of this, and in light of the tremendous progress in such brute-force AI over the past twenty years, it would be fascinating to read an updated version of this book.
  
  While the subject matter of this book is fascinating, its format is unbearable. It is a collection of essays—so often the haven for lazy authors who want to jam what they have already written into book form. Since many of the essays included are technical papers describing different particular AI projects that came out of Hofstadter's group, they continually explain the same concepts over and over. In this way, rather than learning more and more as you read through the book, you instead learn most of what they have to say in the first couple chapters; the ending comes not as a revelation but as a relief.
• 
  Bria

  On the one hand, I think Hofstadter is probably roundly vindicated by the last few decades of AI research. Having small systems work together stochastically for emergent results seems more line in with the direction we've gone in since then, and his resistance to hand-coding in specific relationships has certainly been borne out to be right. However I imagine he's not terribly satisfied with the progress in deep learning and neural networks, as they are, more than ever, completely mysterious black boxes to us, and thus don't do as much as he'd probably like to demonstrate how intelligence - ours or a computer's - actually *works*.
• 
  Serdar

  The three star rating is not a reflection of the book's quality, just that it's highly specialized. This isn't the total tour de force of "Gödel, Escher, Bach", but a series of discussions about the mechanisms of analogy and how that is a cornerstone of what we could call intelligence. Absorbing if again not quite the bolt of lightning "G,E,B" was, but what else could be?
• 
  AunFav

  In comparison to AI development, this book is still valid for understanding human ability and mentality in a broader sense. Machine learning has already impacted every day's activities, would be crucial to visit some initial goal of replicating a human mind (including emotion and such), as to review the model we know of, for its depth and representation.
• 
  Liedzeit Liedzeit

  Nicht annähernd so gut wie die anderen Werke. Anders ausgedrückt: ganz schön öde insgesamt, obwohl da natürlich einige Geistesspritzer zu finden sind.
• 
  Kevin

  Really a series of smaller works tied together. Gets quite repetitive after a while.
• 
  Csimplot Simplot

  Excellent book!!!