That's a really good point, and related to my sibling comment, one of the other things that makes me uncomfortable about ML models is the mysterious generalizab

That's a really good point, and related to my sibling comment, one of the other things that makes me uncomfortable about ML models is the mysterious generalizability. Some generalize well, some start spewing nonsense when you move slightly outside of their training zone. Special-purpose models, I think, do quite a bit better than attempted larger generalized models. For your chess example, if you trained a word2vec model using only a large corpus of text about chess, you very likely wouldn't get the "king - manliness" vector being anything meaningful at all, but you would likely see word associations that are meaningful and also potentially unexpected.