A secretive, Google-backed lab is using artificial intelligence to invent new medicines for humanity's worst illnesses. But can we trust drugs designed by a mind that isn't human?
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.
Reproducibility of what we call LLM 's as opposed to what we call other forms of machine learning?
Or are you responding to my assertion that these are different enough to warrant different language with a counterexample of one way in which they are similar?
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
Life sciences are where this sort of thing will shine.
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.
Reproduceability is always an issue.
?
Reproducibility of what we call LLM 's as opposed to what we call other forms of machine learning?
Or are you responding to my assertion that these are different enough to warrant different language with a counterexample of one way in which they are similar?