The solution must be co-evolved with the problem. (Or more precisely, with our understanding of the problem.)
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If you are not making progress on a solution, look for new problems.
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Complicated problems call for complicated solutions. Complicated solutions do not generalize and rarely scale. Ergo, prefer working on simple, abstract problems. Specifically: on the simpler, more abstract version of your problem.
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If there is no solution, you have a metaproblem.
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End of conversation
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Yes, aptly sums up my approach to AI and also other things.
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1:1, 1:n, n:1, n:m What type of problem-solution ratio is the best IYO? 1:1 - fine-tuned expert system 1:n where n approaches infinity - GPS (Shaw, Newell, et. al.,
@CarnegieMellon) n:1 - multiple problem representations for same solution n:m - MPR for different solutions#USA1pic.twitter.com/BkdyGEPdEXThanks. Twitter will use this to make your timeline better. UndoUndo
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You cannot solve the problems illusions creates and maintain the illusions.
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This is reminiscent of Stuart Firestein's book Failure: Why Science is so Successful
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