>I don't think the problem is with ML. You could easily train a net to make boulder selections with an appropriate data set. Ehhh... mathematically, most ML me

>I don't think the problem is with ML. You could easily train a net to make boulder selections with an appropriate data set. Ehhh... mathematically, most ML methods basically assume that the underlying data and label manifolds should be smooth (in the real analysis sense). Sharply peaked output distributions, small mountains of "loved it!" surrounded by valleys of "ugh", are hard to work with for many models, compared to a wavy surface between "ok" and "ok, I guess".