'data efficiency' -- that's a good candidate for the next buzz imo. People talk a lot about how massive amounts of data regressed over giant networks of dumb functions happens to perform well on many tests, but they ignore the fact that the performance of the system is sick. It fails if you tweak the input by small amounts that would never confuse a human. You shouldn't NEED that much data. You need better priors. Current ML systems rely on arbitrary coincidences to succeed at multiple choice tests. I did that in courses I didn't go to class. They are primitive and childish and won't scale with pure data/processing. We are still missing something.