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To me there are two ways to look at this. Either the laws of physics as we have them are too general when compared to what can be encountered in reality (in other words, actual reality is simple enough that a Turing machine approximated by logic circuits can manage well enough at finding a description) or, models found by fitting data are too specialized, ignoring subtleties not captured by loss functions acting on the data they were trained on.

Any physical model gained by fitting data which purports to be faster than those based on a computable approximation of the laws of physics is so constrained. There is room to maneuver however. If the model being replaced has a limited and known domain of applicability due to approximations made for tractability, a fitted model with suffciently large capacity and expressiveness will for sure improve things.

It's just that it's unlikely to be generally applicable without violating what we know about physics, which is why I am skeptical of the latter part of your post.




>Any physical model gained by fitting data which purports to be faster than those based on a computable approximation of the laws of physics is so constrained.

Constrain the model so that there aren't any superfluids, semiconductors, plasmas, metals or Bose-Einstein condensates and you can still simulate any medicine I know of.


Assume you can then simulate the folding of full-sized proteins, too?


Given enough time and a well-considered methodology, yes. I know two groups that work directly upon this and have great results.


Lithium?


Yeah, take metals out of the equation and it probably a lot more likely.

You are still going to need things like: Sodium, Potassium, Magnesium, Iron, etc.


I meant metals in the solid-state physics sense, with the oceans of electrons and band gaps and stuff. You can keep your monatomic ions (which are crucial for biology in many ways).




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