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> That’s a head in the sand position to take. There are thousands of examples, and dozens of lawsuits that demonstrate that literal copyright-breaking photocopy

> That’s a head in the sand position to take. There are thousands of examples, and dozens of lawsuits that demonstrate that literal copyright-breaking photocopying sometimes happens, I’ve seen it myself with Stable Diffusion and ChatGPT both. Sure, when a picture appears in the corpus hundreds of times. I guarantee you a human artist could reproduce these pictures with the same level of fidelity, because they are cultural cliches. We choose not to, and when we don't, we do not violate copyright. So who commits the crime here? The model didn't decide to reproduce that image spontaneously, it was told to. The broad majority of its output cannot be shown to violate copyright, even with great effort. When a paper comes out with a model that violates copyright, always you see the prompt egging it on. That the model cannot tell that it is reproducing the input distribution a little too well, doesn't mean that's its default mode. Besides, I suspect you don't want to rest your argument on a problem that can be fixed with the most basic effort put into dataset cleaning. (Also, this only happened with some networks, as I remember the paper. I suspect it's a matter of training quality.) So there are two questions: do companies violate copyright when training models, and do models violate copyright when generating images? I disagree with the former universally, and with the latter almost always. > Even when output isn’t obviously breaking copyright, still, we know a-priori that we built a copy-and-remix machine by design. Well, I just don't think this is true. > statistically reproduce their training data. Yes, on a high-dimensional abstract manifold, plus random noise. Again, at that point you've thrown enough divergences in that I genuinely don't see how humans aren't "mix-and-match machines" by this metric. Stop calling models names and make a concrete claim. > we do know for a fact that today’s neural network aren’t it Yes well I think they are it, or at least of the same kind. Show evidence. > Maybe, but this hasn’t been proven or shown to be true, so is speculation. Presumption of innocence still applies, even to code. I have my experience, you have your experience. In my time using models, I have used them to produce samples that were well outside the training distribution. They have managed this with no great difficulty. A mix-and-match machine would, by definition, not be capable of this, unless you use the concept so generically that it could apply to any computable function.

dahart 2y ago
> In my time using models, I have used them to produce samples that were well outside the training distribution No you haven’t, because it’s not yet possible. Either you didn’t see the training data, or you don’t know how neural networks work, or you’re anthropomorphizing and drinking AI kool aid.
- FeepingCreature 2y ago
  Or you're wrong. I mean, I'm just saying. I know what I saw. You're asserting I didn't see what I thought I did, because it's a problem for your argument. That's not very convincing though. edit: Let me clarify, obviously the LLMs/diffusion models I've used can only apply the patterns that they have happened to have learnt. However, due to the high level of abstraction these systems are capable of, the concept of a "pattern" can be very abstract, and even include such things as "recognizing a pattern in the input" as an abstract pattern itself. As such, at the least the output is not constrained to some sort of trivial interpolation of the training set, but a deep nonlinear function of the training set, that has even partially fed back on itself. Again, arguably, just like us.

dahart 2y ago

> In my time using models, I have used them to produce samples that were well outside the training distribution No you haven’t, because it’s not yet possible. Either you didn’t see the training data, or you don’t know how neural networks work, or you’re anthropomorphizing and drinking AI kool aid.

FeepingCreature 2y ago
Or you're wrong. I mean, I'm just saying. I know what I saw. You're asserting I didn't see what I thought I did, because it's a problem for your argument. That's not very convincing though. edit: Let me clarify, obviously the LLMs/diffusion models I've used can only apply the patterns that they have happened to have learnt. However, due to the high level of abstraction these systems are capable of, the concept of a "pattern" can be very abstract, and even include such things as "recognizing a pattern in the input" as an abstract pattern itself. As such, at the least the output is not constrained to some sort of trivial interpolation of the training set, but a deep nonlinear function of the training set, that has even partially fed back on itself. Again, arguably, just like us.