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by dvt 1111 days ago
There's this new trend of what I'll dub techno-nihilism, which is essentially a counterargument to the stochastic parrot argument. The former being: well what if WE are stochastic parrots, after all that's how we learn, right? Well yes, but actually no.

It's trivially false because ChatGPT was trained on something (in this case, Stack Overflow), which, in turn was trained on something else (maybe a book), and so on. So knowledge, imagination, and genuine creativity must exist somewehere down that chain. Everything can't be just repeating what was learned prior ad infinitum, or we'd have nothing new. Ironically, even the development of large language models is an exercise in creativity.
2 comments
cosmolev 1111 days ago
But there is also this idea that you can't judge the system while being a part of it. You simply can't see the whole picture.
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dvt 1111 days ago
True, but like solipsism or nihilism, those ideas are just not that interesting.
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amalter 1111 days ago
Oh, it's not an "interesting idea" that we're teetering on the edge of not being able to differentiate between machine-generated and human-generated content. It does seem that our AI learning models simply mimic our own thinking processes, absorbing and combining experiences to create results that sometimes outshine their origins.

The real question that'll soon dominate is, "How can we even tell the difference?"*

(* - Reworded with ChatGPT 4)

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pixl97 1111 days ago
At the sametime I think you may be overvaluing new answers and the importance of reiterating known answers in a method the user asking the question understands.

For example, there is no intrinsic value in something new. If I take a new solution to a problem, lock it in a box, then it has zero value. It is not improving anyway.

Now, if I take a solution and present it to you in a manner that you can understand, that has an inherent value to the end user.

By this analogy attempting to say that LLMs are useless because they only know what already exists is far to harsh of measure because the vast volume of human output is rehashing what we already know.

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