Let me respond on a higher level then. I think you're doing here is connecting two very disconnected things. I agree with you entirely that cases were a model

Let me respond on a higher level then. I think you're doing here is connecting two very disconnected things. I agree with you entirely that cases were a model is overfitting are copyright violations. I think you should have the right to sue open AI or whoever it is that made the model over the violation of distributing what is basically a copy of your code when you can go in there and reproduce it so exactly. I do not agree is the idea that you think that this means that training on your data is a copyright violation. If the model was trained properly and set up so that it didn't overfit and it didn't reproduce your exact code, you would have absolutely zero right to any claim of copyright. The fact that your data was trained on is not the problem, the fact that your data is reproduced (without a sufficiently detailed explanation that would cause it to be reproduced by any sufficiently capable model) is the problem. Open AI and friends should be dinged for the fact that they've allowed this to happen, they should pay that liability, and that should encourage them to very strongly police their models and make sure the sort of overfitting is very very rare to non-existent. But you, nor anyone else, should have what I believe to be the egregious right to say that no one can learn on the data from your work on the internet. And I understand that's technically not what you're claiming, but when you say that you can deny the right to train models on your data, that's basically what you're saying. You do have claim to also sue the shit out of GitHub for letting people host your code on their service without your permission, and the guy who originally uploaded it. So keep in mind here as you respond to me, that I am drawing a line now between properly training a model where it does not overfit, and the example you have where it did produce your copyright material. > The model is literally trained on my picture; how can it _not_ be a derivative of my work? In the same way that when a person looks at your code and sees some technique you used and goes oh that's neat I'm going to do that too, that's also not a derivative work. An AI model does not copy chunks of your code and just sort of haste that code occasionally when it does other things. An AI model actually learns patterns, builds real working knowledge of connections between things, and uses that knowledge to do new things. An AI model is going to look at your work and learn things like "oh, I can use an if statement." You don't have copyright to that. The data contained in your code? The ability to observe facts about how to program? You don't own those. You only own your specific code. A trained AI model is larger than the sum of its training data. It's not like your code and everyone else's code is sort of bunched into a search engine or something like that, it's actually doing real thinking in there. And that knowledge? It's just not yours to claim. > Photoshop analogy is still not really working It works, again, when you consider what I was talking about above. When the AI model is trained so that it does not overfit. In the case there is no overfit, the tool doesn't compress your work. It doesn't contain your work. It's just trained on it. The point of the analogy is to point out that even if you can create something with a tool, you don't own the copyright claim for that tool. Even with a properly trained AI model, if I were to give it a description of your work, it's probably going to create something that's basically a copyright violation, but that's going to be on the person making that prompt, not the model. So I think that's where our disparity is. My analogy works because I'm thinking of a model on the average where it doesn't overfit. You're thinking of the model in the "exceptional" times where it does overfit. And I do think they are exceptional times. The people making these models really don't want them to overfit so they're using all sorts of techniques to make it happen as little as possible. Clearly not enough, but as a result the average output of these models is actually novel stuff. So when I see a model I see something more like Photoshop. I see it not as a collection, or a compression of other people's work. Rather, it is something that knows how to write code (or roughly knows the patterns in which words need to be arranged to make code a thing), and there are exceptions within that where you can get it to reproduce what was in its training data. So when you say this > Would a very clever compression algorithm suddenly strip from its output the copyright protections of its input? You're imagining me agreeing to something I don't agree with. I think that when a model reproduces an original work, it's a copyright violation. But I don't think that abstracts to the whole picture. I do not think that proves training a model on data is compression.