Indeed, ML is here to stay. But there's a difference between biology/chemistry and math, e.g. There isn't enough data out there on which to train MLs for the former.
I'm thinking here of LLMs as a lossy compression of all our published knowledge. And then agents doing some smart/heuristic search relative to that. They can surely find things that we have missed -- just because they'll tireless try all avenues of investigation that humans might have given up on -- but if the underlying data is not there they can't manufacture it.
LeCun has made this point elsewhere wrt world models and more ordinary physics. This issue is exaggerated in the case of biophysics and quantum mechanics.
I'm thinking here of LLMs as a lossy compression of all our published knowledge. And then agents doing some smart/heuristic search relative to that. They can surely find things that we have missed -- just because they'll tireless try all avenues of investigation that humans might have given up on -- but if the underlying data is not there they can't manufacture it.
LeCun has made this point elsewhere wrt world models and more ordinary physics. This issue is exaggerated in the case of biophysics and quantum mechanics.