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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.
model structure could make or break learning imho trying to solve, say, an nlp task using a bag of unigrams will never work regardless of the training data size
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