Comment by Daniel_Van_Zant
14 hours ago
To add to this I think there are “reductionist sciences” and “systems sciences”. Reductionist sciences are ones, like physics where there are lots of copies of a thing with near identical properties at different scales (the periodic table would be considered an incredibly short list and that seems to be one of the longest list of “different things” in physics).
Systems sciences are ones where each scale has lots of things with unique properties. If you think of “biological things between 2-4m” the amount of unique objects with unique properties and unique interactions is absolutely mind boggling. These sciences generally do not lend themselves to clean highly parsimonious models. The best you can do is coarse-grain a thing as a system (some very roughly defined set of interactions between different things) with certain behavior while accepting that you are losing enormous amounts of predictive power.
I suspect AI will remain worse at systems sciences for a long time because the “possible space” of models for a particular system is much larger than in the reductionist sciences and navigating it takes a human taste and instinct that LLMs don’t seem to have (yet).
One other thought, I think another aspect of biological sciences is just how much effort it takes to do research. Hypotheses are a dime a dozen, but actually verifying these is quite difficult. It takes a lot of patience, time, attention to details, and even then it often fails to replicate.
I don't think LLMs will just magically be able to connect all the dots, because "garbage in, garbage out" still applies. And there's just too many invisible intermediate states, there is literally just not enough information to fully constrain a model. Hence my interest in scaling computational chemistry to get these intermediates :)
An interesting take. I would nuance it a bit:
Your "reductionist sciences" I would instead phrase as "exact sciences", where the input variations are known (even the most generous particle theory only has a 'few' fundamental particle types, such as up-quarks and photons) and their interactions are either well-known (Fermi Principle is apparently inviolable), or reasonably expected to be known someday (exact relationship of gravitons, IF they exist, with other particles).
The "inexact sciences" use either truly infinite, or practically uncountable, inputs - such as the possible shapes of a single protein, which affects the outputs.
You could criticize this grouping by saying the number of particles in the Universe is practically uncountable, but sufficiently accurate statistical models can be built (a galaxy exerts <this much> gravitational force). We can't do the same with the "inexact sciences": we can't predict how a mole of protein will react with a mole of enzymes, without physical testing. OK, maybe for some very low levels of complication this could be reliably predicted, but in general it may be a !P problem.