Also, one of the major classes of problem that gets solved and we view as "progress" in machine learning is framing problems. Like we couldn't define "draw a go

Also, one of the major classes of problem that gets solved and we view as "progress" in machine learning is framing problems. Like we couldn't define "draw a good picture" in a way we could actually assess well, GANs and Diffusion turn out to be good ways to phrase problems like that. In the former case, it creates a way to define the problem as "make something indistinguishable from an example pulled from this dataset" and in the latter case, "I've randomized some of these pixels, undo that based on the description" The idea of "efficiency" and "progress" is this post-hoc rationalization that people who never understood the problem, pay people to understand the problem, apply to problems once they have a working solution in hand. It's a model that is inherently as dumb as a model can be, and the assumption it makes is that there is some general factor of progress on hard problems that can be dialed up and down. Sure, you can pay more scientists and probabilistically increase the rate at which problems are solved, but you can't predict how long it will take, how much failure it will involve, whether a particular scientist will solve a particular problem at all, whether that problem is even solvable in principle sometimes. Businesspeople and investors like models where you put in money and you get out growth at a predictable rate with a controllable timescale, and if this doesn't work you just kick it harder, and this ill fits most frontier research. Hell, it ill suits a lot of regular work.