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Current copyright is too strong in terms of length but too weak in terms of derived work. Well, pending some lawsuits, perhaps. What copyright should do is pro

Current copyright is too strong in terms of length but too weak in terms of derived work. Well, pending some lawsuits, perhaps. What copyright should do is protect individual creators, not corporations. And it should protect them even if their work is mixed through complex statistical algorithms such as LLMs. LLMs wouldn't be possible without _trillions_ of hours of work by people writing books, code, music, etc. they are trained on. The _millions_ of hours of work spent on the training algorithm itself, the chat interface, the scraping scripts, etc. is barely a drop in the bucket. There is 0 reason the people who spent mere millions of hours of work should get all the reward without giving anything to the rest of the world who put in trillions of hours.

monetus 1y ago
Indefinite royalties on Spotify are one thing, but how are they supposed to work in neural nets? Dividing equal share based on inputs would require the company to potentially expose proprietary information. Basing it on outputs could make sense as well I suppose, but would take some slightly ridiculous work for an arguable result. Your point remains, but the problem of the division of responsibility and financial credit doesn't go away with that alone. Do you know if the openAI lawsuits have laid this out?
- martin-t 1y ago
  I admit, rewarding work fairly is very difficult with perfect information, much more with proprietary models and training data. With code, some licenses are compatible, for example you could take a model trained on GPL and MIT code, and use it to produce GPL code. (The resulting model would _of course_ also be a derivative work licensed under the GPL.) That satisfies the biggest elephant in the room - giving users their rights to inspect and modify the code. Giving credit to individual authors is more difficult though. I haven't been following the lawsuits much, I am powerless to influence them and having written my fair share of GPL and AGPL code, this whole LLM thing feels like being spat in the face.
- johnnyanmac 1y ago
  >how are they supposed to work in neural nets? It can be as simple as "you cannot train on someone's work for commercial uses without a license", It can be as complex as setting up some sort of model like Spotify based on the numbers of time the LLM references those works for what it's generating. The devil's in the details, but the problem itself isn't new. >Dividing equal share based on inputs would require the company to potentially expose proprietary information. I find this defense ironic, given the fact that a lot of this debate revolves around defining copyright infringement. The works being trained on are infringed upon, but we might give too many details about the tech used to siphon all these IP's? Tragic. >Do you know if the openAI lawsuits have laid this out? IANAL, but my understanding of high profile cases is going more towards the "you can't train on this" litigation over the "how do we setup a payment model" sorts. If that's correct, we're pretty far out from considering that.
logicchains 1y ago
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.
- martin-t 1y ago
  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.
  - AnthonyMouse 1y ago
    > 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.
    - 1y ago

monetus 1y ago

Indefinite royalties on Spotify are one thing, but how are they supposed to work in neural nets? Dividing equal share based on inputs would require the company to potentially expose proprietary information. Basing it on outputs could make sense as well I suppose, but would take some slightly ridiculous work for an arguable result. Your point remains, but the problem of the division of responsibility and financial credit doesn't go away with that alone. Do you know if the openAI lawsuits have laid this out?

martin-t 1y ago
I admit, rewarding work fairly is very difficult with perfect information, much more with proprietary models and training data. With code, some licenses are compatible, for example you could take a model trained on GPL and MIT code, and use it to produce GPL code. (The resulting model would _of course_ also be a derivative work licensed under the GPL.) That satisfies the biggest elephant in the room - giving users their rights to inspect and modify the code. Giving credit to individual authors is more difficult though. I haven't been following the lawsuits much, I am powerless to influence them and having written my fair share of GPL and AGPL code, this whole LLM thing feels like being spat in the face.
johnnyanmac 1y ago
>how are they supposed to work in neural nets? It can be as simple as "you cannot train on someone's work for commercial uses without a license", It can be as complex as setting up some sort of model like Spotify based on the numbers of time the LLM references those works for what it's generating. The devil's in the details, but the problem itself isn't new. >Dividing equal share based on inputs would require the company to potentially expose proprietary information. I find this defense ironic, given the fact that a lot of this debate revolves around defining copyright infringement. The works being trained on are infringed upon, but we might give too many details about the tech used to siphon all these IP's? Tragic. >Do you know if the openAI lawsuits have laid this out? IANAL, but my understanding of high profile cases is going more towards the "you can't train on this" litigation over the "how do we setup a payment model" sorts. If that's correct, we're pretty far out from considering that.

logicchains 1y ago

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.

martin-t 1y ago
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.
- AnthonyMouse 1y ago
  > 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.
  - 1y ago