Skill is boring -- of course any dumb algorithm can reach arbitrary skill levels when leveraging arbitrary amounts of compute, data, and human engineering. You're trading off intelligence for experience.
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It isn't very interesting that an algorithm can beat any human at chess -- but it would be significant to see an algorithm infer the rules of chess after looking at a few dozens of games, then proceed to develop a human-level chess solver using just a few hundreds of games
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Similarly, I don't want to see a deep learning model beat humans at a MOBA (which of course it can, given enough data), I want to see a model trained on a few thousands of plays, given a *completely new* character, doing just as well as a human in the same situation
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To make progress on AI, we must measure abstraction strength and generalization power, not plain skill (while ignoring *how* that skill was obtained). Focusing on skill is a bit like looking only at the speed of a vehicle but ignoring its energy consumption.
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Intelligence is about efficiently turning experience into generalization power. Skill at a given task, given infinite resources to get there, has no correlation with intelligence.
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1. I like this so much, interpreting from my perspective: skill is ability to act appropriately in each of the microsituations that turn up while on a task. Given large experience - exhaustive list of such situations it is almost trivial to build a lookup table learner
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2. First stage of generalization is leaving out, say 20% of such situations, now an interpolating function can fill in
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