I think lots of simple problems can serve as appropriate AI benchmarks (including chess) as long as we constrain them to measure *generalization power* and not *skill*.
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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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What is your view on the role of priors in the process of generalization?
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This is probably the most I've agreed with you. Well said!
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Kind of like comparing AlphaGo trained for 24 hours vs AlphaGo Zero trained in equal if not less time? Others compare # parameters required to achieve the same result. I think efficiency comparisons will become more relevant as adoption of ML increases in "real-life" use cases.
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From a business point of view: return on investment is the figure of merit.
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