Your approach will be completely untenable in future when we'll have embodied LLMs capable of dynamically learning (live weight updates). It'd make it illegal for such a machine to read any book, watch any movie or browse any webpage, because it could potentially memorise and regurgitate the content. Which would be completely impossible to enforce.
BTW, I like that you spell it GAI. General artificial intelligence feels more natural to say. I wonder if there's some rule of english I don't know which makes AGI more correct or if all the highly educated people are just trying to avoid sounding like they're saying "gay".
Please, don't anthropomorphize it. A model does not "read" a book - an algorithm updates weights which are _based on_ (therefore derivative work) existing training data. Basing them on more work performed by other people does not make it less derivative.
It's not only about regurgitation verbatim. Doing that just means it gets caught more easily.
LLMs are just another way the uber rich try to exploit everyone, hoping that if they exploit every single person's work just a little, they will get away with it.
Nobody is 1000x more productive than the average programmer at writing code. There is no reason somebody should make 1000x more money from it either.
> A model does not "read" a book - an algorithm updates weights which are _based on_ (therefore derivative work) existing training data. Basing them on more work performed by other people does not make it less derivative.
This isn't really how derivative works operate.
If you read Harry Potter and you decide you want to write a book about how Harry and Hermione grow up and become professors at Hogwarts, that's probably going to be a derivative work.
If you read Harry Potter and decide you want to write a book about a little Korean girl who lives with abusive parents but has a knack for science and crawls her way out of that family by inventing things for an eccentric businessman, is that a derivative of Harry Potter? Probably not, even if that was the inspiration for it.
To be a derivative work it has to be pretty similar to the original. That's actually the test, it's based on similarity. Causing it to not be one is done exactly by mixing it with so many other things that it's no longer sufficiently like any of them.
- how things work now vs how they should work
- and also how it works when a human does something vs when a an LLM is used to generate something imitating the human work.
A human has limited time and memory. Human time is valuable, computer time is not. Memorizing something by a human takes time.
When a human is inspired by a work and writes something based on that, he invests a lot of time and energy into it. Therefore people have decided that this creative output should be protected by the law.
Also a human is limited by how much he can remember from the original work. Even if writing what you described, he would inevitably fall back on his own life experiences, opinions, attitude, ways of thinking, etc.
When an LLM is used, it generated a statistical mashup of works it ingested during training. No part of this process has any intrinsic value. It literally only costs what the electricity does. And it's almost infinitely scalable. The law might not call it derivative because it was written at a time where this kind of mechanical derivation was not feasible.
They probably should not by copyrightable by the person prompting the model, at least not to the full extend of normal copyright.
But they are still based on the training data. An untrained model is a random noise generator. A model trained exclusively on GPL code will therefore obviously only generate useful code thanks to the GPL input. The output is literally derived from the "training data" input and the prompt.
Now, given the input is a much more substantial than the prompt by orders of magnitude, the prompt is basically irrelevant.
So what the license of the output should be based on is the training data. The big players can only avoid this logical conclusion by pretending that the model ("AI") is some kind of intelligent entity and also by training on everything so any license is only a minority of the input. It's just manipulation.
> The big players can only avoid this logical conclusion by pretending that the model ("AI") is some kind of intelligent entity and also by training on everything so any license is only a minority of the input.
Whether it's an intelligent entity or not doesn't really enter into it. The real question is whether the output is taking enough from some particular input to make it a derivative. Which ought to depend on what a given output actually looks like.
