If it's worth saying, but not worth its own post (even in Discussion), then it goes here.

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As far as I am aware, Solomonoff induction describes the singularly correct way to do statistical inference in the limits of infinite compute. (It computes generalized/full Bayesian inference)

All of AI can be reduced to universal inference, so understanding how to do that optimally with infinite compute perhaps helps one think more clearly about how practical efficient inference algorithms can exploit various structural regularities to approximate the ideal using vastly less compute.