this post was submitted on 09 Jul 2025
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There really needs to be a rhetorical distinction between regular machine learning and something like an llm.
I think people read this (or just the headline) and assume this is just asking grok "what interactions will my new drug flavocane have?" Where these are likely large models built on the mountains of data we have from existing drug trials
Those models will almost certainly be essentially the same transformer architecture as any of the llms use; simply because they beat most other architectures in almost any field people have tried them. An llm is, after all, just classifier with an unusually large set of classes (all possible tokens) which gets applied repeatedly
I'm not talking about the specifics of the architecture.
To the layman, AI refers to a range of general purpose language models that are trained on "public" data and possibly enriched with domain-specific datasets.
There's a significant material difference between using that kind of probabilistic language completion and a model that directly predicts the results of complex processes (like what's likely being discussed in the article).
It's not specific to the article in question, but it is really important for people to not conflate these approaches.
Actually I agree. I guess I was just still annoyed after reading just previously about how llms are somehow not neural networks, and in fact not machine learning at all...
Btw, you can absolutely finetune llms on classical regression problems if you have the required data (and care more about prediction quality than statistical guarantees.) The resulting regressors are often quite good.