Books like Fluid Concepts and Creative Analogies

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.