from data and where the machine is predicting some part for missing parts and you get millions of bits that way whereas in supervised learning consider imagenet you have thousand classes so that's ten bits per image and if you have a million images you have basically a million times ten bit space key that's basically your whole data set and whereas if you're just doing generative modeling you're modeling all possible bits in your data set so that that's that's too huge right so some supervised learning is trying to find a middle