> AlphaFold was also a massive breakthough in protein folding.
My PhD was applying simpler ML models to problems in structural biology (this was a while ago, so transformers and other large neural models didn't exist then), including protein structure prediction. Most recently I worked in applying generative models and neural network classifiers to problems in drug discovery. I have also worked in IR (search) and NLP, but again with older technologies.
The AlphaFold example is apt: yes, it outperformed methods at CASP. It was indeed a dramatic leap relative to those methods...which didn't work very well. The claims from laypeople that "protein folding is a solved problem" is total bunk, and yet we see that constantly. If you don't know the field, you will be fundamentally misled by statements like "breakthrough".
For other areas (e.g. NLP), the definition of "progress" is also well quantified, and is not accurately captured by the breathless hype that surrounds this technology. Are these large transformers a dramatic improvement? Absolutely. No question. Are they the end of white-collar labor? No. That's ridiculous. Has this been an evolutionary change over the last decade or so? Not a smooth curve, but yes. The world didn't suddenly change overnight.
> Idk what your "PhD" was in but everyone I know in the field is struggling to keep up with the pace of research so unless you are working on "Expert Systems" and trying to convince everyone that the past 10 years of DNN progress has been irrelevant
Except, I'm not trying to convince anyone of that. I'm saying that these models are evolutionary, progress in research is always a form of punctuated equilibrium, and picking random points of progress across fields and extrapolating to the infinite, glorious future doesn't work well. That's what people are doing today.