alt Hacker NewsSpecifically, it's because the verification loops are much longer in applied sciences. In biology you need to wait for wet lab experiments, clinical trials, regulatory steps, etc. before you can call something "solved".
Since sequential steps are slow, I think we're going to see massive increases in parallelization. I'm not sure how the regulatory frameworks will keep up honestly.
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> we're going to see massive increases in parallelization
Why? This is not an AI problem, it's an industry one. And if it was simple, it would already exist.
> wet lab experiments, clinical trials, regulatory steps
I wonder what the unlock would be, if any, of having simulations substitute any (or all, in time) of these steps. Akin to the method used to train FSD cars, or robots.-
We already have as much parallelization as companies can afford: Hundreds, if not thousands of new corn varieties, coming from one lab. The budget is the limit. Testing is just expensive.
Process improvements come from failing faster, so you spend less money on your test. Finding the actual answer without testing would also be wonderful, but LLMs might be fast, but they aren't magic. Something like Alphafold saves time in some steps, but it's not just "hand me a candidate", but find completely novel verification mechanisms, which would be just as useful for humans. Either way, parallelization is unlikely to increase.