on 3, the convention could have just as easily gone a different way i.e. it could have converged to model "fitting" using the statistical parlance or the sklear

on 3, the convention could have just as easily gone a different way i.e. it could have converged to model "fitting" using the statistical parlance or the sklearn convention. Further if you take the math seriously most of these models are "just" fitting probability distributions to data. Also, in part it depends greatly on the objective function used. In GPT style models the objective is to precisely copy from input to output, token by token. I think its extremely bad-faith to argue that this has any relationship to human learning or learning objectives. you shouldn't take the math seriously and I'm not being dismissive with the word "just" in scare quotes. However the community somehow wants to have its cake an eat it too.