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by atomicity
2146 days ago
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I don't know if this means something is truly wrong. AI is a mix of engineering and scientific research, just like most CS subfields. Recently, the emphasis has shifted towards engineering, as the applications of neural nets have skyrocketed after a few breakthroughs in performance. It's similar to computer systems research. For example, a research paper on filesystems might tell us a simple trick which leads to better performance on NVMM. The paper may go into why the trick works, but it doesn't (and shouldn't need to) generalize and try to improve our general understanding of how to design filesystems on different hardware. We've been designing filesystems to this day and well, we are always still guessing about which approaches to use and hoping for the best. In the same vein, we don't even have a widely-accepted theory of how to use data structures yet. So, I don't think that neural nets aren't scientific enough means that it's all BS. We have gaps in understanding, but the power of the models warrants a lot of continued work on finding useful applications. Doesn't mean I don't think AI is over-hyped/overfunded though... |
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For example, people once thought playing chess was hard. So they thought that if a computer could beat the world champion, then computers would probably also be able to replace every job and so on. If you sent Deep Blue back in time to the 1960s, they wouldn't understand how it works so they'd probably assume that it since it could beat Petrosian in chess, it could probably drive cars and treat disease.
But then we built Deep Blue and realized that you don't need AGI to play chess; a very specialized algorithm will do it.
So we're like people in the 70s who've been handed Deep Blue. It's irresponsible, in my opinion, to over-hype it when we have no idea how it works.