Bitter lesson

The bitter lesson is the observation in artificial intelligence that, in the long run, general approaches that scale with available computational power tend to outperform ones based on domain-specific understanding because they are better at taking advantage of the falling cost of computation over time. The principle was proposed and named in a 2019 essay by Richard Sutton and is now widely accepted.

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