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Microsoft just released a demo of BigGPT-Large, which they define as "a domain-specific generative model pre-trained on large-scale biomedical literature, has achieved human parity, outperformed other general and scientific LLMs, and could empower biologists in various scenarios of scientific discovery."
Here's the response to the first question that I asked: @ct_bergstrom @emilymbender
@tob@hachyderm.io@twitskeptic @ct_bergstrom @emilymbender I can think of few domains worse suited for LLM than medicine.
LLM's are not smart. They're not poring through the literature. They're free-associating. They're bullshit machines.
The core problem (and I suspect it's true here) is that the LLM doesn't *know* why it said "yes." It doesn't know anything.
@twitskeptic @ct_bergstrom @emilymbender That lack of actual knowledge is why I think of LLM as an AI research cul de sac. You can push the algorithm as hard as you want, but it's just associations.
It makes sense for a useful AI to incorporate LLM theories and algorithms because, as we see, they're capable of rendering human-sounding responses. But it is not a requirement for useful AI, nor is it a precursor of useful AI.
It's a party trick.
@emilymbender @tob @twitskeptic @ct_bergstrom this is what a lot of people mis-understand. These models don’t understand the domain you’re asking about. They are glorified text autocomplete.
@tob @twitskeptic @ct_bergstrom @emilymbender the reason it said yes is probably because of that study that shows it works in vitro, but the dosage would have to be way too high for a human to get that same effect. Just a dumb program not understanding anything - we really need to move away from calling this AI.