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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.
sutton wasn't arguing a dichotomy. his argument was that scale of search and learning beats cleverness (human knowledge models). reinforcement learning is the process of acquiring better knowledge models of a problem. cleverness scales linearly with computational power.
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