Thread

1. aakil · 1790189583 · focus · HN ↗

   This shows why biology is so much harder a problem area for LLMs than math, finding RTs is tedious but pretty doable today, they had to scope the problem down a lot from something that would be the equivalent of Navier Stokes in biology. Glad they’re doing it though, even if it’s just marketing.

   1. 6thbit · 1790195237 · focus · HN ↗

      It's really a matter of iterating on the problem and validation right?

      Models make progress on coding and math because they can write tests and proofs to an extent. Many industries that are more 'physical' and require performing experiments lack that instant feedback loop. Find a way to close that loop and AI begins to look useful.

      But try and convince companies to invest on closing that loop just to see if the current models work well on their problems or not? Tough sell. So Anthropic just shows them, hey look, this is possible and if you don't do it I will.. so they fold.

      1. asdff · 1790206091 · focus · HN ↗

         >Models make progress on coding and math because they can write tests and proofs to an extent. Many industries that are more 'physical' and require performing experiments lack that instant feedback loop.

         This is basically what they targeted with this approach. They can't automate the experiments since they are often bespoke towards certain goals or even feelings and assumptions based on sage technician knowledge that isn't really taught in any one place. Instead, they tried to automate the process of searching for candidate targets to then test in downstream lab experiments.

         Seems exciting, but this sort of thing has been done for a while with just about every single ml classifier method out there for all sorts of biological data. Just yet another way to slice the pie.

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