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Book Title: Fluid Concepts and Creative Analogies|
The author of the book: Douglas R. Hofstadter
Edition: Basic Books
Date of issue: March 22nd 1996
ISBN 13: 9780465024759
Format files: PDF
The size of the: 5.50 MB
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Reader ratings: 7.8
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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:
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....
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Read information about the authorDouglas Richard Hofstadter is an American academic whose research focuses on consciousness, thinking and creativity. He is best known for his book Gödel, Escher, Bach: an Eternal Golden Braid, first published in 1979, for which he was awarded the 1980 Pulitzer Prize for general non-fiction.
Hofstadter is the son of Nobel Prize-winning physicist Robert Hofstadter. Douglas grew up on the campus of Stanford University, where his father was a professor. Douglas attended the International School of Geneva for a year. He graduated with Distinction in Mathematics from Stanford in 1965. He spent a few years in Sweden in the mid 1960s. He continued his education and received his Ph.D. in Physics from the University of Oregon in 1975.
Hofstadter is College of Arts and Sciences Distinguished Professor of Cognitive Science at Indiana University in Bloomington, where he directs the Center for Research on Concepts and Cognition which consists of himself and his graduate students, forming the "Fluid Analogies Research Group" (FARG). He was initially appointed to the Indiana University's Computer Science Department faculty in 1977, and at that time he launched his research program in computer modeling of mental processes (which at that time he called "artificial intelligence research", a label that he has since dropped in favor of "cognitive science research"). In 1984, he moved to the University of Michigan in Ann Arbor, where he was hired as a professor of psychology and was also appointed to the Walgreen Chair for the Study of Human Understanding. In 1988 he returned to Bloomington as "College of Arts and Sciences Professor" in both Cognitive Science and Computer Science, and also was appointed Adjunct Professor of History and Philosophy of Science, Philosophy, Comparative Literature, and Psychology, but he states that his involvement with most of these departments is nominal.
In April, 2009, Hofstadter was elected a Fellow of the American Academy of Arts and Sciences and a Member of the American Philosophical Society.
Hofstadter's many interests include music, visual art, the mind, creativity, consciousness, self-reference, translation and mathematics. He has numerous recursive sequences and geometric constructions named after him.
At the University of Michigan and Indiana University, he co-authored, with Melanie Mitchell, a computational model of "high-level perception" — Copycat — and several other models of analogy-making and cognition. The Copycat project was subsequently extended under the name "Metacat" by Hofstadter's doctoral student James Marshall. The Letter Spirit project, implemented by Gary McGraw and John Rehling, aims to model the act of artistic creativity by designing stylistically uniform "gridfonts" (typefaces limited to a grid). Other more recent models are Phaeaco (implemented by Harry Foundalis) and SeqSee (Abhijit Mahabal), which model high-level perception and analogy-making in the microdomains of Bongard problems and number sequences, respectively.
Hofstadter collects and studies cognitive errors (largely, but not solely, speech errors), "bon mots" (spontaneous humorous quips), and analogies of all sorts, and his long-time observation of these diverse products of cognition, and his theories about the mechanisms that underlie them, have exerted a powerful influence on the architectures of the computational models developed by himself and FARG members.
All FARG computational models share certain key principles, among which are: that human thinking is carried out by thousands of independent small actions in parallel, biased by the concepts that are currently activated; that activation spreads from activated concepts to less activated "neighbor concepts"; that there is a "mental temperature" that regulates the degree of randomness in the parallel activity; that promising avenues tend to be explored more rapidly than unpromising ones. FARG models also have an overarching philosophy that
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