Splinelinus

This is also my thinking. A(ffero)GPL does something similar by saying a user of an API to AGPL code is bound by the AGPL license. You can always choose not to use the code, and not to use the license.

For the parent comment on discoverability, I honestly don't know. Some models list their data sources, others do not. But if it came down to a dispute it may be that a court order could result in a search of the actual training data and the system that generated it.

For the second case of derived work through context inclusion, it may end up in a similar situation with forensic analysis of the data that generated some output.

I'm waiting for AGPL to become AIGPL: If you train a model with some or all of the licensed work, you agree that the weights of that model constitute a derivative work, and further for the weights, as well as any inference output produced as a result of those weights to be bound by the terms of the license. If you run a model with the licensed work in part or in full as input, you agree that any output from the model is bound by the terms of the license.

I think you're seeing a magic trick that's been around forever: https://www.youtube.com/watch?v=o-KDuOvMAfQ&t=80s

This article is kind of vague on that tbf: To conclude, we observed no credible evidence for a beneficial effect of L-dopa (vs. Haloperidol) on reinforcement learning in a reward context, as well as the proposed mechanistic account of an enhanced striatal prediction error response mediating this effect.