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>except if you can tell that the novel GPTx "paraphrased" is a derivative work from yours, rather than transformative, you can either sue for loyalties, or take

>except if you can tell that the novel GPTx "paraphrased" is a derivative work from yours, rather than transformative, you can either sue for loyalties, or take action. Respectfully, no. The top comment in this thread is saurik asking Stable Diffisuion to generate "an avatar of saurik" and getting a veritable likeness of themselves back. It would be laughable to think that this is feasible without the model having labeled photos of saurik, which are all posted by (go figure) saurik. The AI also generated a "better" photo than the one saurik posted. The AI saurik has more hair. That doesn't change the fact that without the source material, this output would not exist. Nor that it would be really hard for saurik to "take action" on that. ---- In the end, the experiment to do would be very simple. Exclude certain data from the training set, retrain the model, give it the same prompt, and see whether the output changes significantly. If it does, then that data was indispensable for generating the output for that prompt. Something tells me we won't see such experiments done by OpeanAI any time soon.

zarzavat 4y ago
A paraphrased novel is clearly a copyright violation under existing laws. If you use GPT to produce one, then you will be sued. This does not imply that the underlying model is a copyright violation. There is no case law on the copyright status of models but it is generally believed that training a model is fair use. In the human analogy: it is legal to read Harry Potter and to learn Harry Potter from memory such that a copy of it is stored within your head. But if you reproduce and publish or perform substantial parts of Harry Potter from memory you are violating copyright (subject to the various exceptions to copyright: quotation, parody, etc).
l33tman 4y ago
"If it does, then that data was indispensable for generating the output for that prompt." It gets murkier than this, by far. The diffusion models are not just trained on images, but on text. It varies by implementation but Stable Diffusion for example used a pre-trained CLIP transformer network from OpenAI (and subsequently OpenCLIP). CLIP can have internal associations between words that in turn steer the diffusion image generation. To given a simple example of how this could work (I'm not saying this particular example does work though): the model in total could understand that a "swedish flag" consists of a "yellow cross on a blue background", and it could have yellow crosses in its image training set, but no images of Swedish flags, and it would still understand how to draw a "Swedish flag" based on the language semantics. Just as a human, actually. So, this won't work. A style can be inferred and described and it doesn't necessarily need to be in the image training set at all.
- romwell 4y ago
  Thanks for pointing this out! I guess the test I described is the simplest case of detecting appropriation by AI. Still, I think it would be a good start.

zarzavat 4y ago

A paraphrased novel is clearly a copyright violation under existing laws. If you use GPT to produce one, then you will be sued. This does not imply that the underlying model is a copyright violation. There is no case law on the copyright status of models but it is generally believed that training a model is fair use. In the human analogy: it is legal to read Harry Potter and to learn Harry Potter from memory such that a copy of it is stored within your head. But if you reproduce and publish or perform substantial parts of Harry Potter from memory you are violating copyright (subject to the various exceptions to copyright: quotation, parody, etc).

l33tman 4y ago

"If it does, then that data was indispensable for generating the output for that prompt." It gets murkier than this, by far. The diffusion models are not just trained on images, but on text. It varies by implementation but Stable Diffusion for example used a pre-trained CLIP transformer network from OpenAI (and subsequently OpenCLIP). CLIP can have internal associations between words that in turn steer the diffusion image generation. To given a simple example of how this could work (I'm not saying this particular example does work though): the model in total could understand that a "swedish flag" consists of a "yellow cross on a blue background", and it could have yellow crosses in its image training set, but no images of Swedish flags, and it would still understand how to draw a "Swedish flag" based on the language semantics. Just as a human, actually. So, this won't work. A style can be inferred and described and it doesn't necessarily need to be in the image training set at all.

romwell 4y ago
Thanks for pointing this out! I guess the test I described is the simplest case of detecting appropriation by AI. Still, I think it would be a good start.