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Ownership of AI-Generated Code Hotly Disputed

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Re: Ownership of AI-Generated Code Hotly Disputed

#41

Post by skibidibipiti »

andybak wrote: post #23
vouaobrasil wrote: post #15

Earlier quoted context omitted.

I think that kind of depends on how common the snippet is. If it's code for swapping to variables, well everyone has written that and it's small. But what if it's a rarer thing like a new kind of debayering algorithm that takes a much larger "snippet" from a novel algorithm? As far as I'm aware there are no rigorous studies showing the level to which one could duplicate longer stretches of code and if it can do somet…

Algorithms aren't copyrightable as far as my understanding goes and this is where it gets murky. The expression is copyrightable, the algorithm isn't (except when it is...) My personal feeling is that we've gone too far down the road of assigning IP to code and we should be rolling it back. I'd hate for the current AI boom to trigger an extension of current IP law.

Every corporate code base is copyrighted. Engineers can’t write code similar to what they worked on, why should a corporate AI be able to violate copyleft?

Re: Ownership of AI-Generated Code Hotly Disputed

#42

Post by hackinthebochs »

wrs wrote: post #16

“…modify[ing] its AI model so that it traces attribution and gives credit to the original authors of the code, adding the associated copyright notices and license terms in the process…Biderman says is technologically feasible.” Is it really feasible? What does “traces attribution” even mean here? It’s not emitting “code”, it’s emitting individual tokens that each were found throughout the input corpus. The “code” is…

In the ideal case the next token is determined by the local context (the prefix string) and the entire corpus of trained code. In this case the prefix string has not been seen before and so the generator must do some interpretation/extrapolation to determine the likely continuation. But in some cases, perhaps many cases, the prefix string has been seen before, or is similar enough to what has been seen before, that the best continuation is just to spit out the similar string in the training corpus. Presumably such cases can be detected due to specific patterns of activation in the network and attributions can be captured/applied.

One dumb way to do this would be to include self-attributions directly in the stream of training data. So in the cases where the best continuation is to just transcribe the training data, the attributions is included in the data itself.

Re: Ownership of AI-Generated Code Hotly Disputed

#43

Post by wongarsu »

andybak wrote: post #23
vouaobrasil wrote: post #15

Earlier quoted context omitted.

I think that kind of depends on how common the snippet is. If it's code for swapping to variables, well everyone has written that and it's small. But what if it's a rarer thing like a new kind of debayering algorithm that takes a much larger "snippet" from a novel algorithm? As far as I'm aware there are no rigorous studies showing the level to which one could duplicate longer stretches of code and if it can do somet…

Algorithms aren't copyrightable as far as my understanding goes and this is where it gets murky. The expression is copyrightable, the algorithm isn't (except when it is...) My personal feeling is that we've gone too far down the road of assigning IP to code and we should be rolling it back. I'd hate for the current AI boom to trigger an extension of current IP law.

The abstract algorithm isn't covered by copyright, but can be patented (lots of asterisks here about software parents). But the exact lines of code you write are covered by copyright.

If you tell Github Copilot to write a fast inverse square sum algorithm it's allowed to reproduce the idea of the famous quake3 algorithm, but if it produces the same lines and comments as those in the quake 3 source code then that's a pretty open and shut copyright violation

Re: Ownership of AI-Generated Code Hotly Disputed

#44

Post by danShumway »

hutzlibu wrote: post #14
randombits0 wrote: post #9

Code cannot be owned. A creative expression may be copyrighted. Purely functional expressions may not be copyrighted. The output of a trained AI is insufficiently creative to be copyrighted. Only humans can hold a copyright. Now with all that, there really isn’t anything here to get worked up over.

"Purely functional expressions may not be copyrighted." Is this more than a opinion? Because you can have whole programs as a long functional expression (not that I am a fan of such a coding style, but it exists).

It's not worded great (code can be owned under current law). But surprisingly enough, there is a core here that is more than opinion, it just needs some elaboration.

It's also going to be hotly debated in the future, because right now most of the commercial AI-generators are just kind of ignoring this and at some point I think they're going to make the argument, "this law/interpretation can't hold or else it would be commercially devastating for us, and it needs to change."

But under current US copyright law, the US copyright office has pretty consistently ruled that:

- purely functional expressions without a creative aspect can't be copyrighted (ie, you patent an invention, you don't copyright it, and stuff like recipes aren't eligible for copyright at all, only the surrounding text is).

- AI-generated content does not have a creative aspect and isn't eligible for copyright protection.

They've even gone so far as to revoke copyright protections to AI-generated content[0][1]. We haven't gotten a similar ruling about code that I'm aware of, but given that the US copyright office already only grants copyright to code under the assumption that coding is a creative act, it's very difficult to imagine code having more legal protections than an image or a book.

----

In my opinion, this is the much more interesting debate than model's source[2], because nobody is really talking about it, and depending on how the inevitable legal challenges play out, it could completely change the field. It's an interesting choice to think about -- let's say an AI gets good enough that you can sit down, describe an app, and the AI just generates it straight up. Would you still use that tool even if the resulting code was public domain and you couldn't assert ownership over it?

Which would be more important: the accessibility/ease-of-creation, or owning it?

It's also interesting because I haven't heard a ton of good legal arguments from people about why this shouldn't be the case, so there's a nontrivial chance that it holds in the future. I've heard tons of logic-based arguments: "it's creative because I decided what to generate, photographs are copyrightable, etc..." And those aren't necessarily bad arguments, they're just arguments that the US copyright office has already rejected. I haven't seen a lot of arguments about "here's why AI generated code would be eligible without a legal challenge overturning existing copyright rules."

----

[0]: https://www.theverge.com/2022/2/21/22944335/us-copyright-off...

[1]: https://aibusiness.com/ml/ai-generated-comic-book-loses-copy...

[2]: Not that the debate over the training data isn't interesting or important; that could also have implications for stuff like fair-use in transformative contexts. It's just that I think it's less likely to have far-reaching implications or be upheld.

Re: Ownership of AI-Generated Code Hotly Disputed

#45

Post by AlotOfReading »

andybak wrote: post #23
vouaobrasil wrote: post #15

Earlier quoted context omitted.

I think that kind of depends on how common the snippet is. If it's code for swapping to variables, well everyone has written that and it's small. But what if it's a rarer thing like a new kind of debayering algorithm that takes a much larger "snippet" from a novel algorithm? As far as I'm aware there are no rigorous studies showing the level to which one could duplicate longer stretches of code and if it can do somet…

Algorithms aren't copyrightable as far as my understanding goes and this is where it gets murky. The expression is copyrightable, the algorithm isn't (except when it is...) My personal feeling is that we've gone too far down the road of assigning IP to code and we should be rolling it back. I'd hate for the current AI boom to trigger an extension of current IP law.

Leaving aside what I think IP law ought to look like, LLMs literally train on (and reproduce!) the fixed, copyrighted expression. They aren't producing a new work based on abstract knowledge of algorithms that happens to share a particular expression.

It's not at all obvious to me why the training set copyright wouldn't nominally flow through the model, even if that seems impossible to actually implement in practice.

Re: Ownership of AI-Generated Code Hotly Disputed

#46

Post by falcolas »

scotty79 wrote: post #6

https://en.m.wikipedia.org/wiki/Information_wants_to_be_free

I propose that information naturally wants to degrade. Paper decomposes. Bits flip. File formats are replaced and lost. Storage mediums degrade. It's all an extension of the universe trending towards entropy.

It actually takes quite a bit of effort to store, then distribute information precisely and broadly. There's a lot of infrastructure, effort, and money involved, and still information degrades and disappears over time.

Any libre information exists because people have put effort into it. Sometimes a lot of effort.

Re: Ownership of AI-Generated Code Hotly Disputed

#47

Post by wrs »

falcolas wrote: post #39
wrs wrote: post #16

“…modify[ing] its AI model so that it traces attribution and gives credit to the original authors of the code, adding the associated copyright notices and license terms in the process…Biderman says is technologically feasible.” Is it really feasible? What does “traces attribution” even mean here? It’s not emitting “code”, it’s emitting individual tokens that each were found throughout the input corpus. The “code” is…

> It’s not emitting “code”, it’s emitting individual tokens ... The “code” is the arrangement of those tokens, but that is determined by the weighting of the whole network This theory of operation is not borne out in reality. It's been clearly displayed that these tools are emitting verbatim copies of existing code (and its comments) in their input. It's even being seen in image generation, where NatGeo cover images…

The output may be recognizable by humans as being “the same”, but an ML classifier can look at what is “obviously” a cat and classify it as a turtle. So while your statement is accurate, it doesn’t explain how “traceability” could really work—what is the actual mechanism of “tracing”?

Re: Ownership of AI-Generated Code Hotly Disputed

#48

Post by mcintyre1994 »

falcolas wrote: post #39
wrs wrote: post #16

“…modify[ing] its AI model so that it traces attribution and gives credit to the original authors of the code, adding the associated copyright notices and license terms in the process…Biderman says is technologically feasible.” Is it really feasible? What does “traces attribution” even mean here? It’s not emitting “code”, it’s emitting individual tokens that each were found throughout the input corpus. The “code” is…

> It’s not emitting “code”, it’s emitting individual tokens ... The “code” is the arrangement of those tokens, but that is determined by the weighting of the whole network This theory of operation is not borne out in reality. It's been clearly displayed that these tools are emitting verbatim copies of existing code (and its comments) in their input. It's even being seen in image generation, where NatGeo cover images…

On your image generation point, something potentially interesting I saw on twitter/Reddit was about artists uploading “no to AI” images to artstation and then that logo almost appearing to vandalise AI generated art. I’m not sure if it’s a real thing or just people on twitter and stuff who don’t like AI art trolling though. Mostly because I’m not sure how up to date some of these models are being kept.

Re: Ownership of AI-Generated Code Hotly Disputed

#49

Post by AJ007 »

mcintyre1994 wrote: post #48
falcolas wrote: post #39

Earlier quoted context omitted.

> It’s not emitting “code”, it’s emitting individual tokens ... The “code” is the arrangement of those tokens, but that is determined by the weighting of the whole network This theory of operation is not borne out in reality. It's been clearly displayed that these tools are emitting verbatim copies of existing code (and its comments) in their input. It's even being seen in image generation, where NatGeo cover images…

On your image generation point, something potentially interesting I saw on twitter/Reddit was about artists uploading “no to AI” images to artstation and then that logo almost appearing to vandalise AI generated art. I’m not sure if it’s a real thing or just people on twitter and stuff who don’t like AI art trolling though. Mostly because I’m not sure how up to date some of these models are being kept.

That's a joke/fake

Re: Ownership of AI-Generated Code Hotly Disputed

#50

Post by varjag »

Filligree wrote: post #26
wrs wrote: post #16

“…modify[ing] its AI model so that it traces attribution and gives credit to the original authors of the code, adding the associated copyright notices and license terms in the process…Biderman says is technologically feasible.” Is it really feasible? What does “traces attribution” even mean here? It’s not emitting “code”, it’s emitting individual tokens that each were found throughout the input corpus. The “code” is…

It sounds like nonsense. The most plausible solution would be to provide credit to every single author whose code was used in the original training set; of course, that would run into gigabytes just for the credits.

And…? I think my last Xcode update was 20+ Gb.
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