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I don't think training is photocopying, and I don't think it's breaking copyright. > Why do you believe that? We know exactly how it’s built correct > we kno

I don't think training is photocopying, and I don't think it's breaking copyright. > Why do you believe that? We know exactly how it’s built correct > we know what the algorithms do Dubiously correct, we know their function but not the purpose of the particular individual patterns learnt, see interpretability research > and there’s nothing in there capable of original thought. Incorrect, we have no idea what it takes to be capable of "original thought", the concept isn't even defined, we don't even know if humans do it. It's entirely possible that networks are capable of original thought, in fact there's a lot of evidence for it, like how if you put them in situations that aren't covered in the training they can often handle it fine. > it builds a Markov model Okay, do you know how the networks work? No, complete nonsense, Markov models have nothing to do with this. For one, LLMs are exactly not "locally" due to the long-range attention mechanism. Even things like RWKV, that "just" uses persistent state, can express correlations much, much longer than any Markov chain ever built. If you built a Markov chain for the sorts of correlation distances you'd get in LLMs you would need exponentially more states than there are atoms in the universe, which demonstrates that what's going on cannot be modelled as a Markov process without torturing the concept into incoherence. > It's deterministic Sure, it's deterministic plus a random noise factor. Anything in the world is deterministic plus a random noise factor, us included. If you perfectly recorded the thermal noise in a human brain you could deterministically repeat its output forever. Imo this view is just "well the network can't be doing the same thing we're doing, it's too simple. I'm not that simple am I?" Yeah sorry, the human brain isn't that complicated.

dahart 2y ago
You’re moving your own goal posts. There are different kinds of stealing even when only humans are doing all the stealing. It seems really funny to try to argue that all art is stolen, and that AI is not stealing, in the same breath. > I don't think training is photocopying, and I don't think it's breaking copyright. 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. OpenAI is seeking copyright exemption for their models, which implicitly and openly admits they’re breaking current laws, and want the laws to change in their favor. Even when output isn’t obviously breaking copyright, still, we know a-priori that we built a copy-and-remix machine by design. You can carry on arguing with everything I say, and that doesn’t change the fact that NNs are built with the intent to statistically reproduce their training data. Side note that breaking copyright law does not require that the output is recognizable as a copy of one or more inputs. US Copyright Law specifies that one may not acquire and consume copyrighted material without permission. Some models are trained on legally acquired datasets, but many are not. Whether AI is stealing depends on the specific model and training data, so a blanket disagreement that no laws are broken is obviously incorrect. > we have no idea what it takes to be capable of “original thought”, the concept isn’t even defined May be true, but we do know for a fact that today’s neural network aren’t it, regardless of what original thought takes. Maybe we’ll crack the code in the future, and maybe today’s AI has started down the right track. Or, maybe not. > plus a random noise factor. Anything in the world is deterministic plus a random noise factor, us included. If you perfectly recorded the thermal noise in a human brain you could deterministically repeat its output forever. Maybe, but this hasn’t been proven or shown to be true, so is speculation. This armchair physics idea is hundreds of years old. Anyway, today’s AI is not deterministic plus a random noise factor, it’s just purely deterministic. > Yeah sorry, the human brain isn’t that complicated. Haha, the hubris. Prove it! BTW this is straw man; I didn’t make any statements about the brain and I wasn’t comparing today’s AI to the human brain. Whether AI is stealing pixels does not depend on comparisons to humans or answering existential questions about consciousness.
- FeepingCreature 2y ago
  > 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.

dahart 2y ago

You’re moving your own goal posts. There are different kinds of stealing even when only humans are doing all the stealing. It seems really funny to try to argue that all art is stolen, and that AI is not stealing, in the same breath. > I don't think training is photocopying, and I don't think it's breaking copyright. 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. OpenAI is seeking copyright exemption for their models, which implicitly and openly admits they’re breaking current laws, and want the laws to change in their favor. Even when output isn’t obviously breaking copyright, still, we know a-priori that we built a copy-and-remix machine by design. You can carry on arguing with everything I say, and that doesn’t change the fact that NNs are built with the intent to statistically reproduce their training data. Side note that breaking copyright law does not require that the output is recognizable as a copy of one or more inputs. US Copyright Law specifies that one may not acquire and consume copyrighted material without permission. Some models are trained on legally acquired datasets, but many are not. Whether AI is stealing depends on the specific model and training data, so a blanket disagreement that no laws are broken is obviously incorrect. > we have no idea what it takes to be capable of “original thought”, the concept isn’t even defined May be true, but we do know for a fact that today’s neural network aren’t it, regardless of what original thought takes. Maybe we’ll crack the code in the future, and maybe today’s AI has started down the right track. Or, maybe not. > plus a random noise factor. Anything in the world is deterministic plus a random noise factor, us included. If you perfectly recorded the thermal noise in a human brain you could deterministically repeat its output forever. Maybe, but this hasn’t been proven or shown to be true, so is speculation. This armchair physics idea is hundreds of years old. Anyway, today’s AI is not deterministic plus a random noise factor, it’s just purely deterministic. > Yeah sorry, the human brain isn’t that complicated. Haha, the hubris. Prove it! BTW this is straw man; I didn’t make any statements about the brain and I wasn’t comparing today’s AI to the human brain. Whether AI is stealing pixels does not depend on comparisons to humans or answering existential questions about consciousness.

FeepingCreature 2y ago
> 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.