I wonder how this will work with AI stuff generating code without any source or attribution. It’s not like the LLMs make this stuff up out of thin air it comes from source material.
Copyright and patents are completely independent concepts.
The “perpetual” part is the issue but “rent seeking” is the entire reason that copyright and patents exist to begin with.
This sounds pedantic, but it’s important to not mistake the means for an end:
> To promote the Progress of Science and useful Arts, by securing for limited Times to Authors and Inventors the exclusive Right to their respective Writings and Discoveries;
https://constitution.congress.gov/browse/article-1/section-8...
Did you know that Facebook owns the patent on autocompletes? Yahoo owned it and Facebook bought it from them as kind of a privately owned nuclear weapon to create a doctrine of mutually assured destruction with other companies who own nuclear-weapons-grade patents.
Of course the penalty for violating a patent is much worse if you know you are doing it, so companies are very much not eager to have the additional liability that comes with their employees being aware that every autocomplete is a violation of patent law.
The real (legal) question in either case, is how much is actually copied, and how obvious is it.
It’s also incredibly hard to tell if a LLM copied something since you can’t ask it in court and it probably can’t even tell you if it did.
But the issue with copyright I think comes from the distribution of a (potentially derivative or transformative in the legal sense) work, which I would say is typically done manually by a human to some extent, so I think they would be on the hook for any potential violations in that case, possibly even if they cannot actually produce sources themselves since it was LLM-generated.
But the legal test always seems to come back to what I said before, simply "how much was copied, and how obvious is it?" which is going to be up to the subjective interpretation of each judge of every case.
Questions about LLMs are primarily about whether it's legal for them to do something that would be legal for a human to do and secondarily about the technical feasibility of policing them at all.
It's already fixed. Anything you make with AI cannot be protected in any way (UK gives some leeway on certain types of creations).
So if it mimics code from ffmpeg for example, then ffmpeg wins.
Really, it comes down to encoding. Arbitrarily short utf-8 encoded strings can be generated using a coin flip.
It's trivially true that arbitrarily short reconstructions can be reproduced by virtually any random process and reconstruction length scales with the similarity in output distribution to that of the target. This really shouldn't be controversial.
My point is that matching sequence length and distributional similarity are both quantifiable. Where do you draw the line?
Picking randomly out of a non-random distribution doesn't give you a random result.
And you don't have to use randomness to pick tokens.
> If you mean chance=uniform probability you have to articulate that.
Don't be a pain. This isn't about uniform distribution versus other generic distribution. This is about the very elaborate calculations that exist on a per-token basis specifically to make the next token plausible and exclude the vast majority of tokens.
> My point is that matching sequence length and distributional similarity are both quantifiable. Where do you draw the line?
Any reasonable line has examples that cross it from many models. Very long segments that can be reproduced. Because many models were trained in a way that overfits certain pieces of code and basically causes them to be memorized.
Right, and very short segments can also be reproduced. Let's say that "//" is an arbitrarily short segment that matches some source code. This is trivially true. I could write "//" on a coin and half the time it's going to land "//". Let's agree that's a lower bound.
I don't even disagree that there is an upper bound. Surely reproducing a repo in its entirety is a match.
So there must exist a line between the two that divides too short and too long.
Again, by what basis do you draw a line between a 1 token reproduction and a 1,000 token reproduction? 5, 10, 20, 50? How is it justified? Purely "reasonableness"?
There are very very long examples that are clearly memorization.
Like, if a model was trained on all the code in the world except that specific example, the chance of it producing that snippet is less than a billionth of a billionth of a percent. But that snippet got fed in so many times it gets treated like a standard idiom and memorized in full.
Is that a clear enough threshold for you?
I don't know where the exact line is, but I know it's somewhere inside this big ballpark, and there are examples that go past the entire ballpark.
I don't care where specifically the bound is.
[0] https://githubcopilotlitigation.com [1] https://www.theverge.com/2022/11/8/23446821/microsoft-openai...
Doesn’t invalidate your story in the slightest - I just know they’ve gotta be chasing this, specifically, like it’s life or death.