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by tremdog
615 days ago
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This argument falls apart when you consider digital photographers. They didn’t create the image - a machine and software did. Would you make the same argument that a photographer’s only copyright is the input to the camera settings and the color-grading software? Proof of process is totally reasonable, since all art should have a clear provenance, and I’m sure the original prompt & seed could prove his ownership. This goes beyond your point, but the argument that AI Art can’t be copyrighted because it was trained on copyrighted works is a little hackneyed. Every human artist is trained on copyrighted works, reproduces that art during school, and often incorporates referential elements into their own art. |
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The problem is that while some people view machine learning as equivalent to human learning (including some case law apparently) I do not believe most people see it that way. The way machine learning algorithms work today is decidedly very unique.
A human artist learns to draw or paint a picture, but diffusion models are literally going from noise to final image directly. There is no sketching, or painting. If it generates artifacts that make an image look "drawn" it is because it "learned" what those artifacts look like.
Even if a program really did paint something from scratch, and you sat there and directed it by typing in "over 600 prompts"... That's a lot closer to commissioning an artist than it is producing art. (Apparently, an artist who is very hard to communicate with, but thankfully has unlimited patience.)
Photography is an interesting comparison point specifically because it is one of the lowest effort things that a human can possibly do to have copyright eligibility. I'm not saying photography is not a valid art form, but every picture you take is eligible for copyright equally even if it is not particularly creative. That's not the point of photographs being eligible for copyright, though. As flawed as copyright may be, one thing you can say about copyright is that it is very nuanced and specific, and the reasoning that led to the legal status of copyright in photography is not likely to be repeated for diffusion models. The legal status for photography seems to hinge on the fact that it clears the "fixed in a tangible medium of expression" requirement. This is a pretty complicated concept and I'm not a lawyer and won't try to conjecture on whether or not results from machine learning models used today could clear this bar.
Either way, let's stop directly comparing "learning" for machine models that do things that humans literally can't and don't, such as "synthesize a fully-formed painting out of a bunch of latent noise" by analyzing trillions of pictures, to human learning wherein spend years and thousands of hours learning how to draw and paint (needless to say, analyzing factors fewer pictures in the process). They both behave very differently and accomplish very different goals. The only thing they really have in common is probably that they both boil down to statistics on some level.