51–60 of 205 posts
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.
Earlier quoted context omitted.
It is an accurate statement of the law in the US, but "purely functional expression" is not used in the sense of a mathematical function. It basically means expressions that solely reflect the way you need to do the thing to accomplish the function you are trying to achieve, without any significant creative element. For example, if I asked you to write Java code to print the sum of two integer variables named a and b…
It seems like a very blurry line for any non-trivial piece of code.
Opinion me, this statement could apply to the vast majority of copyright law in the US.
“…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…
Can you cite sources? I’ve heard this claim repeatedly but have yet to see a good example.
“…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 problem here happens when the same source is sampled for many tokens in a row because it’s the only match for the context. It could also happen that many tokens in a row each have a long list of sources, but when put together have a subset of sources that appear in every token’s list. That means that someone’s input is being repeated verbatim, even if the network wasn’t trying to reproduce a single source. We could prune the list of attribution sources at the expense of compute by running largest common subset algorithms, which might be sufficient for attribution tracing?
It feels like this whole question might hinge on Fair Use. The network is copying other people’s code one token at a time. We (society & copyright law) all tend to agree that’s fine when it’s a single token out of context, and we all tend to agree it’s not fine when the whole output program matches any single input source. The question naturally becomes, “where’s the line, how many tokens in a row from a single source should be allowed?”
“…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 t…
I don’t think people are even clear on the problem statement here. If I fed in three very similar functions from different sources, and I got a fourth, also very similar, output, what is the “traced attribution” supposed to be?
“…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…
I think it’s pretty obvious that if you train a model to reproduce existing work in its entirety, it fails the “sufficiently transformative” test and thus loses legal protection.
But there’s nothing stopping you from re-coding existing implementations of GPL’ed code. I used to do it. And your new code has your own chosen license, even if your ideas came from someone else. Are you sure the same logic shouldn’t apply to models?
Earlier quoted context omitted.
Yes, as long as it doesn't disregard the original creator's intentions in the formulation.
Even that notion is tricky in the context of text AI. It doesn't seem obviously clear to me what the implied stance towards AI training would be for many popular license types. It seems more like the creators of many licenses didn't explicitly consider this use case. And the more meta question is whether the creators' rights should even extend to that realm. Can you specify in a license "This text must not be read by…
It would be more accurate to compare those students with the corporate interests involved in AI training, not the model's use of the content.
by reducing credit, copilot reduces incentive to create and publish free code. biting the hand that feeds it. exact same problem exists with GPT3 and others. big tech slashing and burning, ruthlessly exploiting the least empowered people in the tech economy. neat hack.
Being able to ask a model for help writing Spark, SQLAlchemy, Tokio, whatever, actually increases the usability of free code vs proprietary code.
Everyone must unlearn the term "Intellectual Property". These laws are anti-property rights. They are Intellectual Slavery laws (https://breckyunits.com/an-unpopular-phrase.html).
The United States government employs more knowledge workers than all other companies (see NIH, DoD, CDC, NASA, NOAA, NWS, et cetera). Everything they produce is public domain, by law. And yet, the people producing these information products still get paid!
We don't need (c)opywrong laws. We don't need Intellectual Slavery laws. We still have cotton even after the 13th Amendment (we actually have more and better cotton now), and we will still have creative works after the passing of the Intellectual Freedom Amendment (we actually will have more and better creative works) - https://breckyunits.com/the-intellectual-freedom-amendment.h....
“…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…
Which makes sense when you consider that the sort of code that is getting reproduced verbatim is usually library functions which developers may copy and paste verbatim comments and all into their project, especially when you prompt the AI with the header of a function that has been copied and pasted often, so the weightings will in that instance be heavily skewed towards reproducing that function