51–60 of 66 posts
In some fields of research, the amount of literature out there is stupendous, and with little hope of a human reading, much less understanding the whole literature. Its becoming a major problem in some fields, and I think, in some ways, approaches that can combine knowledge algorithmically are needed, perhaps llms.
Traditionally, that's what meta-analyses and published reviews of the literature have been for.
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Assume free-associations = hallucinations. Assume hallucinations are exactly what makes LLMs useful and your question can be rephrased as "Can you list some examples where LLMs were useful to you?"
Hallucinations are lies. So not the same thing.
Hallucinations are inaccuracies it doesn't realize are inaccurate.
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If we keep extrapolating eventually GPT will be omniscient. I really can't think of any reason why that wouldn't be the case, given the exponential curve we find ourselves on.
How do you know you're not on a logistic curve? Don't you think costs and the availability of training data might impose some constraints?
The ideas aren't the hard part.
The ideas aren't the hard part.
Tell that to PhD advisor that took credit for all my work because they were his ideas (at least so he claimed).
Go to any meeting and state the obvious fact that "any idiot can have an idea. Making it happen is the tough part" then watch how the decision makers react
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This. Any researcher should, over a lunch, be able to generate more idea than can be tackled in a life time.
The fact that a human expert can also do it doesn't mean the AI isn't valuable. Even if you just consider the monetary aspect, those few API calls would definitely be cheaper than buying the researcher lunch. But the big benefit is being able to generate those ideas immediately and autonomously every time there's new data.
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Such as?
rsfern is asking exactly that
I've found where LLMs can be useful in this context is around free-associations. Because they don't really "know" about things, they regularly grasp at straws or misconstrue intended meaning. This, along with the volume of language (let's not call it knowledge) result in the LLMs occasionally bringing in a new element which can be useful.