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1 yr. ago

  • It seems like they do though, because

    1. it's explicitly allowed

      Code or other content generated in whole or in part using AI tools can be contributed to Linux Foundation projects.

      and beyond that,

    2. some numbers suggest it's highly likely that it's happening with no public concern or pushback from the kernel leadership.

    I've also brought up these concerns on the mailing list, with apparently no response from the maintainers, even though Linus was CC'ed here by another kernel dev. Specifically, my suggestion to not allow AI code submissions resulted in no response.

    I'm not saying I would be owed a response. But the implications of that seem pretty clear.

  • The point I was making was rather, there is no guaranteed safe length and you probably don't want to make that call as a maintainer. Unless you're a lawyer that happens to do FOSS, I guess.

  • Sadly, LxQt is pro slop too:

    The same seems to be the case for KDE. It seems like GNOME also somewhat is (since they didn't ban AI for GTK+ or mutter or gnome shell, as far as I'm aware). It's quite frustrating to see.

  • I'm not a lawyer but I'm really unsure if there's even some safe guess like two lines. E.g. if you look at lyrics, I think people have been sued less - not that I would know for sure, though, and no idea if that allows any conclusions for code...

  • It could also simply be seen as a bad look no matter any risks, potentially the taking of code snippets from other projects that may have a significant code length, without attributing them properly. I find it sad.

  • Well, that's sad. Who knows what potential unattributed plagiarism landmines are part of it then...

  • The main confusing (or confused?) actor in the FOSS space in relation to this probably really is the Linux kernel.

    I've seen so many projects reference significant LLM patches as acceptable "because the kernel does it". It continues to blow my mind that the kernel would accept such a risk.

  • Sad to see that it's accepted outside of the test cases at all. But good to see they're severely limiting it, at least.

    Why do I find even minor LLM changes sad?

    • Because it moves the goalpost about what is okay to copy. If you brought a one-line change to a FOSS project in the past that you took out of the leaked MS Windows source code, you would have been scolded for risking such an explosive origin for such low gain. Nowadays with LLMs people seem to be trying to make that the new normal. I don't think that's a good path to take for the ecosystem.
    • And because no project should have to think about what's "copyright significant". If you reach that state, I feel like you should perhaps just reject it and have somebody rewrite it cleanly.
  • So how would that work given LLMs apparently can't do much other than rearrange/plagiarize the training data?

    I think that's where the article writer's definition comes from. A model probably won't seem very open to most people if the output licensing isn't really compatible with free use.

  • Is that meant to be some sort of humorous remark regarding the OpenAI name? I think most observers would agree that the name isn't particularly fitting, so that seems like a fair observation.

  • The "open" criteria listed in the article seem applied inconsistently by the writer:

    In our recent AI and Ethics article, "The open-source advantage in large language models (LLMs)," my co-authors and I operationalize this even more concretely: a model qualifies as open-source only when its architecture, training code, model weights, and training data are all publicly available under licenses that permit unrestricted use, modification, and redistribution. By that standard, very few models qualify. The Allen Institute’s OLMo, EleutherAI’s GPT-NeoX, and LLM360’s K2 are among the handful that meet these four criteria.

    I just checked the first one, OLMo. It just ingests random web pages, as far as I can tell. How would that possibly qualify for "training data [...] available under licenses that permit unrestricted use"? Why does the article writer think it would?

    How would anything but pure CC0 training data fit that, given attribution requirements are so common and I assume they're a restriction on use (that most models ignore)?

    The article apparently not wanting to admit this, and/or not having done the basic research to check, makes it seem as bad to me as the open-washing it is complaining about.

    (However, perhaps I'm misunderstanding something here?)

  • Yup. And looking at the fallout from the other comments, seems like it may have been a good idea to take a stand.

    (I don't like people becoming upset, but clearly some people embrace AI a little much, and if you look at Forgejo for example then you'd know Codeberg was always on some level anti AI due to the ethics and all that. And as far as I can tell, Codeberg always wanted to be a somewhat opinionated pro ethics code host. Now I understand some people don't like where the line has been drawn, but it's not suprising Codeberg wants to draw a line somewhere.)

  • I feel like it depends on the project.

    Typically in the ones I work on, whenever I spot an inconsistency I fix it no matter how much of a mess that causes. Yes it hurts at first, but it may hurt more down the line when the weirdnesses accumulate to the point where it impacts operations and it'll be a much bigger problem to fix.

    However, I can totally see a more chill strategy work better for a project that is mostly just relatively simple code, like perhaps some website deployments. In my opinion it depends a lot on whether the code is generally already complicated, which is when you'll typically want to refactor earlier than later.

  • To be honest, I mainly know calibre from modifying ebook files without asking just when you look at them, making them no longer pass EPUB validation and embedding private non-standard info (a reading position, I think). My suggestion would be to use something like Foliate instead.

  • Windows could be considered uniquely responsible here since on a desktop you might think you're safe from this if you always shut it down properly after use. Yet "fast startup" is a thing. It's a questionable default that should be revisited.

  • Maybe it's just me, but I find fully automated transit slightly creepy, at least when it comes to passenger trains. I personally prefer when there's somebody human still on board having an eye on things.

  • Technology @lemmy.world

    Interesting video on why apparently moltbot and other AI agents are dangerous

  • Self Hosted - Self-hosting your services. @lemmy.ml

    Community maintained free IP geo lists