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Forgive me for this one, but it comes from genuine curiosity and not snark. You are making assertions about how copyright law works, but you don't qualify it wi

• yokem55 3y ago
  > I think that from a legal standpoint, the actual technical means by which something was accomplished doesn't matter if the process as a whole is effectively copyright infringement. Which is why when the user of the model prompts for something infringing, and is successful at getting close to verbatim output (because the prompt was too constraining, becuase the work is overrepresented in the training) it is that particular output that is infringing. And maybe that means that services operating that prompt/response software are guilty of contributory infringment if they can't adequetly prevent that kind of output. But that doesn not mean that training the model was infringing. Nor does that mean distribution of the model is infringing. And if a user of the prompt/response software never prompts for anything infringing, and the software never spontaneously recreates anything infringing, there's no infringment happening. There are lots of technologies out there that are highly capable of enabling infringment at a massive scale. And where the vast majority of their actual usage is absolutely infringing. But we don't completely shut down those technologies that on their own - are not infringing. Bittorrent clients are pefectly legal to develop. And distribute. And people use those clients to commit infringment at large scale. But they are still pefectly legal to write and distrubute.
  • theamk 3y ago
    TensorFlow is also perfectly legal to develop and distribute, and no one contests this. People object to specific artifact, "model weights", which were produced using copyrighted works at the input, and can be used to reproduce those same copyrighted works back. In bittorrent analogy, people want to shut down specific pirate trackers and the pirate bay website.
    • yokem55 3y ago
      From the above EFF article: > First, a derivative work still has to be “substantially similar” to the original in order to be infringing. If the original is transformed or abridged or adapted to such an extent that this is no longer true, then it’s not a derivative work. A 10-line summary of a 15,000-line epic isn’t a derivative work, and neither are most summaries of books that people make in order to describe those copyrighted works to others. The statistics generated about the works entered as input, do not resemble the original works. Nor can those statistics on their own reproduce the original work. At most they are brief mathematical summaries of the work. And it's only after combining those stats with the stats of billions of other works (which is its own creative process to determine the best statistical methodologies to achieve that combination) that anything intelligble can be produced in the output stage.
      • jchw 3y ago
        I think the case for Stable Diffusion in general is not too bad, however EFF tempers their optimism when it comes to cases where the model may actually memorize the inputs: > To sum up: a diffusion model can, in rare circumstances, generate images that resemble elements of the training data. De-duplication can substantially reduce the risk of this occurring. But the strongest copyright suit against a diffusion-based AI art generator would likely be one brought by the holder of the copyright in an image that subsequently was actually reproduced this way. EFF's position seems to be (to which I personally agree, FWIW) that Stable Diffusion almost certainly does not run afoul of at least the vast majority of copyright holders of data it was trained from. > The statistics generated about the works entered as input, do not resemble the original works. Nor can those statistics on their own reproduce the original work. At most they are brief mathematical summaries of the work. Of course, this needs a lot of qualification. Compression and intelligence are generally considered to be related, and indeed, compression also works on statistical analysis (like entropy coding a la Huffman, or frequency analysis via Fourier transforms). Granted, compression algorithms are designed to reproduce their input verbatim--it's the entire point. But I think ML weights may exist somewhere "in the middle" so to speak; depending on the model architecture and how it's trained, it may be more or less literally like compression. Vastly overfit models are very much like compression, whereas large generalized models like Stable Diffusion are pretty far away and yes, generally can't reproduce inputs verbatim. (However: I suspect many LoRA models are quite overfit and may not be in the same boat.) However, that's just for image generation. I feel like LLMs and text generation are an entirely different ballgame, and given that we can't actually inspect the model weights in the case of GPT4, the best we can really do to surmise what's going on is to see how badly it seems to overfit its training data. I am unconvinced that this matter is settled as a whole, although I do think the EFF article presents a good overview of the case regarding Stable Diffusion and it does coincide pretty closely with what I actually believe. But this article is about large language models, which may legitimately be a completely different ball game.
• Scion9066 3y ago
  The EFF's post about it at each of the three steps of obtaining, training, and generating an output image: https://www.eff.org/deeplinks/2023/04/how-we-think-about-copyright-and-ai-art-0 https://www.eff.org/deeplinks/2023/04/how-we-think-about-cop... It's an interesting read and makes a good case for why none of the steps are directly copyright infringement, even if you can prompt the output to be (and in that case the person doing the prompting should be the one at fault, same as someone drawing something infringing directly).