[eli_gottlieb]

>If you take a purely input-output view of the world (which by the way, even classical Physics does), every problem _is_ curve fitting in a sufficiently high dimensional space.

Not all spaces are Euclidean, and "purely input-output" still contains a lot of room for counterfactuals that ML models fail to capture.

[nerdponx]

What do you mean by counterfactuals? NNs are function approximation algorithms, in any geometry. No ifs ands or buts about it.

[eli_gottlieb]

Oh, I agree that neural networks are function approximators with respect to some geometry. When I say "counterfactuals", I'm talking about typical Bayes-net style counterfactuals, but as also used in cognitive psychology. We know that human minds evaluate counterfactual statements in order to test and infer causal structure. We thus know that neural networks are insufficient for "real" cognition.