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>The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin. The ultimate reason for this is Moore's law, or rather its generalization of continued exponentially falling cost per unit of computation. That doesn't sound right. If there's an "ultimate reason" for computation AI success, is that the problem, of thought, signal processing, pattern matching etc, is not "rational" or based on semantic manipulation, but computational in its nature/substrate in humans too. Computation being increasingly cheaper wouldn't guarantee a success in AI, if those problems weren't inherently solvable by mere throwing computation at them. That is, it's not like we solve it with a brute force over a "proper semantic" approach because brute force got cheaper. It's more like the proper approach is more like our computation one, than a semantic one, and we wrongly assumed that this is not the case in the 70s and 80s, because we only considered the higher levels of conscious/semantic processing in our brains, and not the deep computational processing underneath them. >They said that ``brute force" search may have won this time, but it was not a general strategy, and anyway it was not how people played chess. I'd think that most chess playing in humans happens at an uncoscious level, after the player has "trained" their brain, than actual conscious semantic arguing about the next move. The conscious manipulation comes only after huge swaths of moves have been pruned through pattern matching/computation going on in the background. |