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Can differentiable programming learn arbitrarily complex behaviors, up to superhuman performance, given a dense sampling of the behavior manifold (e.g. an infinite training data generator, such as self-play)? Yes. We knew that. Arguably AlphaGo was the concrete proof-of-concept.
Thus, the question of failure or success boils down to "have we drawn enough training data yet?" (or in some cases, "does our architecture have sufficient capacity & appropriate structure wrt the training data available?"). Whether the answer is yes or no, it teaches us nothing.
Note that the question of model choice (the structure of the differentiable architecture used) is rather secondary, because as long as memorization capacity is sufficient, *any* model will do -- if there is training data to match. Even a single hidden Dense layer.
It just moves the threshold for "have we drawn enough data yet" by a bit -- by no more than a few orders of magnitude. But when infinite data is available, this is "just" the size of your cloud computing bill.
Wait, aren’t there solid mathematical arguments that present skepticism to that claim? I thought mode collapse in GANs is an instructive example of that skepticism. https://simons.berkeley.edu/news/research-vignette-promise-and-limitations-generative-adversarial-nets-gans …
CycleGAN solves that problem by requiring information preservation to reconstruct the original input The keyword here is "arbitrarily". We don't know. In more complex contexts, new problems may arise. I'm looking forward to machine victories in StarCraft 2 (very complex)
I’d tease-out arbitrarily complex. Even with the permutations in Go, the next state is super-constrained. E.g. Like sequence-to-sequence code generation... hard... ouch.
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