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No, it talks about the other AI effect, where AI researchers thinks that generalizing the model will be relatively easy. Like, lets say someone makes an AI to p
by username90 7y ago
No, it talks about the other AI effect, where AI researchers thinks that generalizing the model will be relatively easy. Like, lets say someone makes an AI to play tic-tac-toe at the same level as the best human and then says that with just a little bit more work it will be able to beat a grandmaster at Go.
- Animats 7y agoRight. Someone linked to "Artificial Intelligence Meets Natural Stupidity" last week, which makes that argument in detail. People in the field have been claiming Strong AI Real Soon Now since the General Problem Solver of the 1960s, which is a simple tree search algorithm. I heard a lot of that at Stanford in the 1980s, when the expert systems boom was about to crash and people thought Symbolics LISP machines were magical. There is progress, though. Machine learning does do a lot. And it makes money, so effort will continue at a high level. AI used to be a dinky field - maybe 20-50 people at MIT, CMU, and Stanford. Now it's huge.
- NoodleIncident 7y agoI've only seen the headlines whizzing by, but isn't this exactly what Deepmind(/AlphaGo/AlphaZero/MuZero) has been doing lately?
- username90 7y agoMy point was that machines mastered tic-tac-toe 50 years ago and mastered Go just last year. Making a statistical model to predict a small subset of three body problems isn't very impressive at all, kinda like tic-tac-toe. It is a bit interesting that it works, but not much more than that. Adding another body, or adding starting velocities to these three bodies, would make the problem a lot less tractable, so it is very unlikely the same techniques will work there.