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An example is this argument that Deep Blue was just massive search. It wasn't: it used more than 8,000 hand-engineered features that incorporated a huge amount of chess knowledge. Contrast Sutton's essay on the left with the paper about Deep Blue on the right:pic.twitter.com/xyFQJH7LL9
Of course, I don't disagree at all with the essay that computational scale buys you an incredible amount, and it's easy to under-estimate. But GANs & levering pawns (etc) weren't discovered with computational scale.
Nice followup from @gdb, making the point that it's a false dichotomy (scale versus cleverness):https://twitter.com/gdb/status/1106386231338762240 …
A better essay collecting some (correct!) examples in the same general direction is this paper by Banko & Brill: http://www.aclweb.org/anthology/P01-1005 … See eg this great graph, showing performance as a function of training data size. In this example: more data >> smarter algorithmpic.twitter.com/zwEmQSGMcP
There is, IMO, a good paper to be written following this up, carefully understanding the relationship between scale and clever ideas.
GANs are a meta method... Levering pawns is not. The former is universal, the latter is specific for the problem. AlphaZero may not have needed this levering pawn prior.
But did levering pawns get discovered by AlphaZero? I would argue that it did absent of any human cleverness.
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