The biggest lesson that can be read from 70 years of AI research is that general methods that leverage
computation are ultimately the most effective, and by a large margin. The ultimate reason for this is
Moore’s law, or rather its generalization of continued exponentially falling cost per unit of
computation. Most AI research has been conducted as if the computation available to the agent were
constant (in which case leveraging human knowledge would be one of the only ways to improve
performance) but, over a slightly longer time than a typical research project, massively more
computation inevitably becomes available. Seeking an improvement that makes a difference in the
shorter term, researchers seek to leverage their human knowledge of the domain, but the only thing
that matters in the long run is the leveraging of computation. These two need not run counter to each
other, but in practice they tend to. Time spent on one is time not spent on the other. There are
psychological commitments to investment in one approach or the other. And the human-knowledge
approach tends to complicate methods in ways that make them less suited to taking advantage of
general methods leveraging computation. There were many examples of AI researchers’ belated
learning of this bitter lesson, and it is instructive to review some of the most prominent. — Read More

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