I didn't miss the point. my graduate work was using constraint optimizers in molecular dynamics (n-body physics) and it translated to ML (I didn't have to relearn anything). The one part that was truly simpler is the objective functions in ML are believable to be convex, while n-body physics with constraints are highly non-convex).
Re:Why is AI hard and physics simple?
by banamonster1 on Tuesday June 22, 2021 @04:33PM
(#27596831)
people on the board, not you
> my graduate work was using constraint optimizers in molecular dynamics
me too for qm/mm sims -- did some rg/complex systems work too ;)
the author is talking about constraint optimization in physics vs ml/ai
constraint optimization is easier with a well defined prior vs the guess work in ml algos.
dl models in physics can scale and emergent phenomena will depend on scaling parameters -- this is coarse graining in physics.