I think it’s silly to pretend that human programmers are emitting a lot of code with a high degree of originality either. We’re all remixing some long-forgotten influential code laying deep in latent memory just like the models.
Ownership of AI-Generated Code Hotly Disputed
111–120 of 205 posts
Re: Ownership of AI-Generated Code Hotly Disputed
#112Earlier quoted context omitted.
Sure. I’ve done it for Bel. (def no (x) (id x nil)) (def atom (x) (no (id (type x) ‘pair))) (def some (x f) (if (no x) nil (f (car x)) x (some (cdr x)))) (def all (x) (if (no x) t (f (car x)) (all (cdr x)) nil))) I don’t even have to pull up bel.bel to know that those are almost perfect replicas. I typed it on my iPad. EDIT: as far as I can tell, the only diff is that all comes before some. https://sep.yimg.com/ty/cd…
I'd wager that most folks would consider the code for some basic list operations to be a little different from outputting a complete specific implementation of a fast square root function with its exact constants, including the comments.
Why? They’re both basic functions. I’d argue that if pg had left comments and constants, I’d have replicated those too.
Perhaps my example isn’t as impressive as the model’s capability, but operationally it’s the same.
Re: Ownership of AI-Generated Code Hotly Disputed
#113Earlier quoted context omitted.
In my opinion using all of the code on GitHub without respecting the licenses was a capital mistake. It should have been opt-in, maybe with some incentive but to just take it all without so much as a by-your-leave is not going to play well in court.
In my opinion it was covered under the GitHub terms of service and is clearly transformative. I am optimistic that the courts will find it so and we can put these debates to rest similar to how we’ve done for web scraping.
Sorry but terms of service don't give you a blanket license to re-purpose someone else's copyrighted work at your pleasure. By that same token any hosting provider could make a small change to their terms of service and suddenly all of the data of all of the customers would be theirs. Copyright does not work that way, you need to actively sign away your rights.
Re: Ownership of AI-Generated Code Hotly Disputed
#114“…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…
They probably mean using a code search engine to check all snippets. The simplest thing would be am n-gram filter. A more advanced approach would use a code similarity neural net. It's not principled attribution, just locating the most similar example in the training set.
> It's not principled attribution, just locating the most similar
example in the training set.
This is a profoundly important distinction.
Back-tracing data to contributory training examples is a genuine "influenced by" relation. Picking the nearest neighbour to a given result (even if its an exact copy!) cannot say anything useful with respect to origins. And given that there will always be some proximate neighbour, it's really a "misattribution machine".
This is bit like how our broken patent system grants or denies ownership of a design based on similarity to extant art but regardless of actual originality.
Re: Ownership of AI-Generated Code Hotly Disputed
#115“…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…
The answer is of course it's possible, so long as you have a couple of gigabytes spare for the acknowledgements page. Any construct worth attributing will have roots in billions of parameters.
It's far worse than that: to attribute a particular output to an input you don't just need the input data, you need the gradient updates that data caused in the training run. A couple hundred gigabytes input tokens times 175 billion parameters equals... impossibility.
Re: Ownership of AI-Generated Code Hotly Disputed
#116Earlier 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…
Do you have a source for this claim? This isn't really how generative models work
https://news.ycombinator.com/item?id=34187038 points to the two specific claims WRT stable diffusion. The co-pilot ones are easily found. Quake 3's "fast inverse square root" is the best known instance.
Re: Ownership of AI-Generated Code Hotly Disputed
#117To me AI code generators are the equivalent of crypto tumblers or mixers for digital coins. You can pretend all you want that the output is 'clean' but we all know it came from somewhere else and wasn't actually generated by the software, just endless little snippets that other people made.
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Re: Ownership of AI-Generated Code Hotly Disputed
#118“…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…
Why wouldn’t it be feasible? (Maybe this depends on what you mean by ‘feasible’.) There’s no technical reason you can’t back-track the weights and make a list of which tokens from which training data were sampled. The list might be long, it could be impractical, but that has little bearing on whether it’s technically possible, right? The problem here happens when the same source is sampled for many tokens in a row be…
Fair use is a bit more context dependent, especially when applied on other form of media. Unstable diffusion is a big example where many seems to feel that no fair use should be allowed regardless of how small of a token is taken by the AI. A small amount also depend on the original work and how it fit into society at large.
Having fair use to be the pillar that all AI training stand on is going to take a while.
Re: Ownership of AI-Generated Code Hotly Disputed
#119Earlier quoted context omitted.
In my opinion it was covered under the GitHub terms of service and is clearly transformative. I am optimistic that the courts will find it so and we can put these debates to rest similar to how we’ve done for web scraping.
Sorry but terms of service don't give you a blanket license to re-purpose someone else's copyrighted work at your pleasure. By that same token any hosting provider could make a small change to their terms of service and suddenly all of the data of all of the customers would be theirs. Copyright does not work that way, you need to actively sign away your rights.
> Sorry but terms of service don't give you a blanket license to re-purpose someone else's copyrighted work at your pleasure.
That’s correct. It’s also not what happened here. I don’t violate your copyright when I scrape your web page. I don’t violate your copyright when I train a model on a web page that I scraped.
I violate your copyright only if I use that tool to produce code that violates your copyright. This is similar to how I don’t commit a copyright violation when I read your code, I commit the violation when I produce and publish code that violates your copyright.
Re: Ownership of AI-Generated Code Hotly Disputed
#120When Copilot was released the copyright discussion focused exclusively on code, but now we see very similar discussion around images with stable diffusion and the sister project unstable diffusion. When Copilot do reach the courts there will be some indication on how courts view author consent when it comes to training material. After that we might then see court cases for each form of media (images, video, text, sou…
In my opinion using all of the code on GitHub without respecting the licenses was a capital mistake. It should have been opt-in, maybe with some incentive but to just take it all without so much as a by-your-leave is not going to play well in court.
It would have been awesome if Copilot had been written so that you tell it what your license is and it emits code trained only on code with compatible licenses. That would have created an incentive for people to publish their own code under more open licenses, instead of facilitating IP theft and thus creating a disincentive. That GH didn't choose this option is rather telling IMO.