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by thfuran 1228 days ago
Training a model maybe, but is it clear that the output of the model isn't a derivative work?
2 comments

To about the same degree as the output of a human.

I just started writing a new novel. It's an interesting, in my opinion highly novel fantasy/SF(ish) story, for once not fanfiction of anything that's still in copyright -- most people wouldn't count stories based on ancient norse mythology as 'fanfiction' -- but that doesn't mean it isn't derivative. It means, instead of naming two or three things it's derivative of, I can name ten to fifteen.

That's normal. All stories are derivative, and if you point me at an author who claims theirs aren't, you're pointing at a liar. The job of an author is to put the building blocks together in a new and interesting form, not to make them up from whole cloth. It's impossible to invent more than two or three truly novel ideas per day, even if you're incredibly imaginative, and most of those won't be any good.

The difference between humans and AIs, nowadays, seem to be that the AIs use millions of sources instead of ten to fifteen. Or, alternately, that they use none -- and theirs is less derivative -- because certainly everything I've ever read goes into my writing, not just the things I recognise I'm using.

>To about the same degree as the output of a human.

No. Full stop. Humans aren't stochastic parrots. Pointing to a lack of understanding about what exactly happens in the human mind is, FULL STOP, not evidence that LLMs are doing the same things humans do.

This being HN, I get to be pedantic ;-).

Humans are not stochastic, they're obviously chaotic[1]. Which is to say: not parrots at all.

Some of the modern models I've seen also seem to be chaotic too though, so that's interesting [2]. I'm going to assume LLMs probably exhibit the same properties.

[1] https://en.wikipedia.org/wiki/Chaos_theory (Chaotic systems sometimes seem to be stochastic, but they're actually much stranger and more interesting!)

[2] I've been messing with stable diffusion to get a feel for (and/or avoid) tipping points: that is to say, points in latent space where the model becomes very sensitive to small changes in initial parameters. You can find instances fairly quickly even by hand by doing bisect search.

>[2]. I'm going to assume LLMs probably exhibit the same properties.

That's quite an assumption to make.

They use very similar technology, so it's not a large leap.
LOL this isn't serious, is it? you can't point to a lack of understanding in the human brain as a basis for equating the logic a machine performs when it is also unclear. that's just rhetorically fallacious as a first step.
I don't think I ever claimed that?

That's not my argument. My argument is that the anti-AI arguments, as spoken, also match to what I know I'm doing as a human. In my opinion better than it matches to what the AIs are doing, because as you say, they aren't human.

Maybe the output isn't, but what the LLM turns the work into when it becomes a constituent element of the model is probably a derivative work.