Any person with a "scientific attitude" in this field would find it incredibly easy to observer that the training target for natural language is, f(Q) = {A1..A

Any person with a "scientific attitude" in this field would find it incredibly easy to observer that the training target for natural language is, f(Q) = {A1..An} -- n being very very large and the target for mathematics is, g(Q) = A1 And the model they're using approximates with, m(Q) = A_guess So it's incredibly easy to model f with m because A_guess just has to be close to one of A1...n; and its very hard to model mathematics because it has to be only A1. The reason articles like this are written isn't because people dont know this; it's because they just do not have a sceptical attitude. And that's a problem I can't fix. If they'd approached this issue with the goal of "finding nothing surprising about this behaviour", ie., trying to make it maximally consistent with existing (basic, trivial, widely-taught) theory, they'd reach this conclusion in under 5min. The problem is their goal is always to find something surprising so they can release some PR about it. There's nothing I can write to fix this problem, it's edemic in the whole field. It makes ML/AI research much more like psychology than physics. Alas!