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AI tooling must be disclosed for contributions

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Re: AI tooling must be disclosed for contributions

#321

Post by blurbleblurble »

david_allison wrote: post #241
blurbleblurble wrote: post #194

Earlier quoted context omitted.

"Every piece of feedback you give to an LLM is like screaming into the void - it wastes your time, and doesn't change the LLM one iota." I think you just haven't gotten the hang of it yet, which is fine... the tooling is very immature and hard to get consistent results with. But this isn't a given. Some people do get good, steerable LLM coding setups.

As a maintainer, if you're dealing with a contributor who's sending in AI slop, you have no opportunity to prompt the LLM. The PR effectively ends up being an extremely high-latency conversation with an LLM, via another human who doesn't have the full context/understanding of the problem.

You're totally dismissing this person's agency and their ability to learn. You're all but writing off their existence.

Re: AI tooling must be disclosed for contributions

#322

Post by watwut »

neilv wrote: post #26

There is also IP taint when using "AI". We're just pretending that there's not. If someone came to you and said "good news: I memorized the code of all the open source projects in this space, and can regurgitate it on command", you would be smart to ban them from working on code at your company. But with "AI", we make up a bunch of rationalizations. ("I'm doing AI agentic generative AI workflow boilerplate 10x gettin…

There is same pretensiom with "hallucinations". If I did the same, it would bs called lying or bullshitting.

Re: AI tooling must be disclosed for contributions

#323

Post by strogonoff »

raggi wrote: post #294
jedbrown wrote: post #286

Provenance matters. An LLM cannot certify a Developer Certificate of Origin ( https://en.wikipedia.org/wiki/Developer_Certificate_of_Origi... ) and a developer of integrity cannot certify the DCO for code emitted by an LLM, certainly not an LLM trained on code of unknown provenance. It is well-known that LLMs sometimes produce verbatim or near-verbatim copies of their training data, most of which cannot be used witho…

For a large LLM I think the science in the end will demonstrate that verbatim reproduction is not coming from verbatim recording, as the structure really isn’t setup that way in the models under question here. This is similar to the ruling by Alsup in the Anthropic books case that the training is “exceedingly transformative”. I would expect a reinterpretation or disagreement on this front from another case to be both…

In the West you are free to make something that everyone thinks is a “derivative piece of trash” and still call it yours; and sometimes it will turn out to be a hit because, well, it turns out that in real life no one can reliably tell what is and what isn’t trash[0]—if it was possible, art as we know it would not exist. Sometimes what is trash to you is a cult experimental track to me, because people are different.

On that note, I am not sure why creators in so many industries are sitting around while they are being more or less ripped off by massive corporations, when music has got it right.

— Do you want to make a cover song? Go ahead. You can even copyright it! The original composer still gets paid.

— Do you want to make a transformative derivative work (change the composition, really alter the style, edit the lyrics)? Go ahead, just damn better make sure you license it first. …and you can copyright your derivative work, too. …and the original composer still gets credit in your copyright.

The current wave of LLM-induced AI hype really made the tech crowd bend itself in knots trying to paint this as an unsolvable problem that requires IP abuse, or not a problem because it’s all mostly “derivative bits of trash” (at least the bits they don’t like, anyway), argue in courts how it’s transformative, etc., while the most straightforward solution keeps staring them in the face. The only problem is that this solution does not scale, and if there’s anything the industry in which “Do Things That Don’t Scale” is the title of a hit essay hates then that would be doing things that don’t scale.

[0] It should be clarified that if art is considered (as I do) fundamentally a mechanism of self-expression then there is, of course, no trash and the whole point is moot.

Re: AI tooling must be disclosed for contributions

#324

Post by asdff »

Filligree wrote: post #271
asdff wrote: post #245

Earlier quoted context omitted.

Is there really no logging capability with these tools that would track all of that prompting/testing/editing/inputting?

Sure, screen recorders exist. And if contributions are that unwelcome, then it's better not to contribute. There has to be some baseline level of trust that the contributor is trying to do the right thing; I get enough spying from the corporations in my life.

I'm not talking screen recorder but a log file where I could be given it and use it for input and repeat the work exactly as it was done. Sort of like how one could do the same with their bash history file to repeat an initially exploratory analysis effort using the same exact commands. I'm surprised that isn't already a capability given the business interest with AI. One would think they would like to cache these prompt flows to ensure long term fidelity in process.

Re: AI tooling must be disclosed for contributions

#325

Post by kentm »

nullc wrote: post #151
wahnfrieden wrote: post #14

Earlier quoted context omitted.

You should care. If someone submits a huge PR, you’re going to waste time asking questions and comprehending their intentions if the answer is that they don’t know either. If you know it’s generated and they haven’t reviewed it themselves, you can decide to shove it back into an LLM for next steps rather than expect the contributor to be able to do anything with your review feedback. Unreviewed generated PRs can stil…

> is that they don’t know either It would be nice if they did, in fact, say they didn't know. But more often they just waste your time making their chatbot argue with you. And the chatbots are outrageous gaslighters. All big OSS projects have had the occasional bullshitter/gaslighter show up. But LLMs have increased the incidence level of these sorts of contributors by many orders of magnitude-- I consider it an open…

There was some post that comes to mind of an example of this. Some project had a security issue reported that was not a security issue, and when asking questions it became extremely obvious that someone was just feeding the conversation into an LLM. There was no security issue. I can imagine this is happening more and more as people are trying to slam in LLM generated code everywhere.

Everyone promoting LLMs, especially on HN, claim that they're expertly using them by using artisanal prompts and carefully examining the output but.. I'm honestly skeptical. Sure, some people are doing that (I do it from time to time). But I've seen enough slop to think that more people are throwing around code that they barely understand than these advocates care to admit .

Those same people will swear that they did due diligence, but why would they admit otherwise? And do they even know what proper due diligence is? And would they still be getting their mythical 30%-50% productivity boost if they were actually doing what they claimed they were doing?

And that is a problem. I cannot have a productive code review with someone that does not even understand what their code is actually doing, much less trade offs that were made in an implementation (because they did not consider any trade offs at all and just took what the LLM produced). If they can't have a conversation about the code at all because they didn't bother to read or understand anything about it, then theres nothing I can do except close the PR and tell them to actually do the work this time.

Re: AI tooling must be disclosed for contributions

#326

Post by kentm »

ryoshu wrote: post #162
sheepscreek wrote: post #157

Earlier quoted context omitted.

We keep talking about “AI replacing coders,” but the real shift might be that coding itself stops looking like coding. If prompts become the de facto way to create applications/developing systems in the future, maybe programming languages will just be baggage we’ll need to unlearn. Programming languages were a nice abstraction to accommodate our inability to comprehend complexity - current day LLMs do not have the sa…

All we need to do is prompt an LLM with such specificity that it does exactly what we want the machine to do.

Good idea! We can have some sort of standard grammar that we use to prompt the LLM such that it deterministically gives us the result we ask for. We then constrain all prompts to match that grammar. Some sort of language describing programs.

Re: AI tooling must be disclosed for contributions

#327

Post by aspenmayer »

raggi wrote: post #299
eru wrote: post #248

Earlier quoted context omitted.

> Sure it’s a big hill to climb in rethinking IP laws to align with a societal desire that generating IP continue to be a viable economic work product, but that is what’s necessary. Well, AI can perhaps solve the problem it created here: generated IP with AI is much cheaper than with humans, so it will be viable even at lower payoffs. Less cynical: you can use trade secrets to protect your IP. You can host your softw…

In the US you can not generate copyrightable IP without substantial human contribution to the process. https://www.copyright.gov/ai/Copyright-and-Artificial-Intell...

Tell that to Reddit. They’re AI translating user posts and serving it up as separate Google search results. I don’t remember if Reddit claims copyright on user-submitted content, or on its AI translations, but I don’t think Reddit is paying ad share like X is, either, so it kind of doesn’t matter to the user, as they’re (still) not getting paid, even as Reddit collects money for every ad shown/clicked. Even if OP did write it, an AI translated the version shown.

https://news.ycombinator.com/context?id=44972296

Re: AI tooling must be disclosed for contributions

#328

Post by raggi »

j4coh wrote: post #305
raggi wrote: post #294

Earlier quoted context omitted.

For a large LLM I think the science in the end will demonstrate that verbatim reproduction is not coming from verbatim recording, as the structure really isn’t setup that way in the models under question here. This is similar to the ruling by Alsup in the Anthropic books case that the training is “exceedingly transformative”. I would expect a reinterpretation or disagreement on this front from another case to be both…

So if you can get an LLM to produce music lyrics, for example, or sections from a book, those would be considered novel works given the encoding as well?

"an LLM" could imply an LLM of any size, for sufficiently small or focused training sets an LLM may not be transformative. There is some scale at which the volume and diversity of training data and intricacy of abstraction moves away from something you could reasonably consider solely memorization - there's a separate issue of reproduction though.

"novel" here depends on what you mean. Could an LLM produce output that is unique that both it and no one else has seen before, possibly yes. Could that output have perceived or emotional value to people, sure. Related challenge: Is a random encryption key generated by a csprng novel?

In the case of the US copyright office, if there wasn't sufficient human involvement in the production then the output is not copyrightable and how "novel" it is does not matter - but that doesn't necessarily impact a prior production by a human that is (whether a copy or not). Novel also only matters in a subset of the many fractured areas of copyright laws affecting the space of this form of digital replication. The copyright office wrote: https://www.copyright.gov/ai/Copyright-and-Artificial-Intell....

Where I imagine this approximately ends up is some set of tests that are oriented around how relevant to the whole the "copy" is, that is, it may not matter whether the method of production involved "copying", but may more matter if the whole works in which it is included are at large a copy, or, if the area contested as a copy, if it could be replaced with something novel, and it is a small enough piece of the whole, then it may not be able to meet some bar of material value to the whole to be relevant - that there is no harmful infringement, or similarly could cross into some notion of fair use.

I don't see much sanity in a world where small snippets become an issue. I think if models were regularly producing thousands of tokens of exactly duplicate content that's probably an issue.

I've not seen evidence of the latter outside of research that very deliberately performs active search for high probability cases (such as building suffix tree indices over training sets then searching for outputs based on guidance from the index). That's very different from arbitrary work prompts doing the same, and the models have various defensive trainings and wrappings attempting to further minimize reproductive behavior. On the one hand you have research metrics like 3.6 bits per parameter of recoverable input, on the other hand that represents a very small slice of the training set, and many such reproductions requiring strongly crafted and long prompts - meaning that for arbitrary real world interaction the chance of large scale overlap is small.

Re: AI tooling must be disclosed for contributions

#329

Post by ozfive »

_w1tm wrote: post #266
ozfive wrote: post #254

Earlier quoted context omitted.

What will happen when company A implements algorithm X based on AI output, company B does the same and company A claims that it is proprietary code and takes company B to court?

What has happened when the same thing happens without AI involved?

Yep, it’s not a brand-new problem. I just wonder if AI is going to turbocharge the odds of these disputes popping up.

Re: AI tooling must be disclosed for contributions

#330

Post by wizzwizz4 »

JoshTriplett wrote: post #278
wizzwizz4 wrote: post #206

Earlier quoted context omitted.

CC BY-SA 4.0 is "compatible with code". It is, for example, GPL-compatible (see https://wiki.creativecommons.org/wiki/ShareAlike_compatibili... ). It's just not designed for code.

That's a fair distinction. For the specific case of CC-by-sa 4.0, it's possible to convert to GPLv3. That doesn't help with prior versions of CC-by-sa. So, for the specific case of material contributed to StackOverflow on or after 2018-05-02, it's possible to use it under GPLv3 (including appropriate attribution), so any project compatible with GPLv3 can copy it with attribution. Any material before that point is not…

It does help with prior versions, since you can use CC BY-SA 3.0 material under CC BY-SA 4.0, which is GPLv3-compatible. (See https://meta.stackexchange.com/a/337742/308065.) It doesn't necessarily help with future versions.
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