Earlier quoted context omitted.
> Just because you can view something or free doesn't mean you can use it anyway you want. This whole thread really makes me want to pull my hair out. Difference between illegaly creating a (even temporary) copy of a copyrighted work (e.g. streaming a movie) vs. creating a derivative work of said copyrighted work: Two completely different things, with completely different legal outcomes. If OpenAI in any shape or for…
> anything that a computer does can be construed as making a copy. but that's not the point of contention. The training data set has been granted the right to be distributed (by virtue of it being available for viewing already - it's not hidden or secret). The proof is that a human can already view it manually. Let's call this 'public'. The question is, whether using this public training dataset constitutes creating…
This is wrong. My paintings are publicly available (especially going by your definition [which I'm confused by the origin of?]). Taking a photograph of my paintings is still a copyright violation. I hope we can ignore all the legal kerfuffle about personal use, as it has no bearing on our discussion. Again -- all of this boils down back to what I've said before: Bare human consumption does not constitute as making a copy, nearly everything else does.
Your second point -- a copyrighted work automatically granting someone else any rights (especially distrubtionial rights) by just being available to be consumed -- is even more wrong. I'm not going to go further into that, as you can very easily prove yourself wrong by googling it.
>The question is, whether using this public training dataset constitutes creating a derivative work
I'm not well versed in the US copyright laws, but I would assume (strongly so) that this would not be the case. I -- again, for US copyright law -- assume that for something to be considered a derivative work, it needs to include (or be present in other ways) copyrightable (!) parts of the original work(s). In other words, the original work needs to "shine through" the derivative work, in one way or the other. The delta of parameter changes of a ML model would (imo) not constitute such a thing.
Problems with derivative works will come into play when considering the things ML models produce.