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1. as I understand, GPT-x are trained on generic datasets. Why would anyone expect it could just be repurposed for a domain specific task, without additional fi
by joe_the_user 6y ago
1. as I understand, GPT-x are trained on generic datasets. Why would anyone expect it could just be repurposed for a domain specific task, without additional fine tuning?
With due respect, I don't think that's the problem.
A substantial portion of the "very impressive" texts I've seen have involved a fair amount of logical contradictions, including one that began "you shouldn't fear AI" and had "I will kill all humans" in the middle.
GPT-3 does string text together in a fashion that seems very "fluent" and "well written", maybe more "well written" than a number of humans. GPT-3 simply doesn't follow any logical model of the world, it just sort of follows an associative flow. Which to me says that training on a specific medical database couldn't solve the problem - it might only mask the problem by avoid big error but allowing small errors that can still be deadly.
- k__ 6y agoI guess, it's a bit like images of people who don't exist. It looks like it makes sense, but in the end there is nothing behind it. And this gets more obvious with text.
- acdha 6y ago> Which to me says that training on a specific medical database couldn't solve the problem - it might only mask the problem by avoid big error but allowing small errors that can still be deadly. I think it might even make it worse: if something is obviously unnatural in a context, the reader will be less inclined to trust it. If GPT-3 used more valid terms and phrasing common in the field, it might lead someone to trust it more than they should, especially if the error rates are low enough that routine sets in.