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Cast it into the fire (media.fedia.io)
submitted 9 months ago by to c/fuck_ai@lemmy.world
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[–] 10 points 9 months ago (18 children)

What about the AI that I run on my local GPU that is using a model trained on open source and public works?

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  • [–] 11 points 9 months ago* (2 children)

    It's cool as hell to train models don't get me wrong but if you use them as assistants you will still slowly stop thinking no?

    So NazgΓ»l.

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  • [–] 2 points 9 months ago (2 children)

    This is very cool. Any advice a simple software engineer (me) could follow to practice the same?

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  • [–] 1 point 9 months ago (7 children)

    That is slightly less unethical than Claude or whatever, but it is still unethical.

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  • [–] 1 point 9 months ago (6 children)

    Can you elaborate on why this is unethical?

    I use 0.2kWh of electricity to spend a day coding with this model:

    https://en.wikipedia.org/wiki/Apertus_(LLM)

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  • [–] 1 point 9 months ago (5 children)

    It is still trained on open source code on GitHub. These code communities seemingly have no way to opt out of their free (libre) contributions being used as training data, nor does the resulting code generation contribute anything back to those communities. It is a form of license stripping. That's just one issue.

    Just because your inference running locally doesn't use much electricity doesn't mean you've sidestepped all of the other ethical issues surrounding LLMs.

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  • [–] 1 point 9 months ago (4 children)

    It is not trained on open source code on Github.

    But I can use it to analyze a datasheet and generate a library for an obscure module that I can then upload to Github and contribute to the community.

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  • [–] 1 point 9 months ago (3 children)

    Apertus is most certainly trained on source code hosted on GitHub. It is laid out here in their technical report:

    https://github.com/swiss-ai/apertus-tech-report

    It uses a large dataset called TheStack, among others.

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  • [–] 1 point 9 months ago* (2 children)

    StarCoderData.23 A large-scale code dataset derived from the permissively licensed GitHub collection The Stack (v1.2). (Kocetkov et al., 2022), which applies deduplication and filtering of opted-out files. In addition to source code, the dataset includes supplementary resources such as GitHub Issues and Jupyter Notebooks (Li et al., 2023).

    That's not random Github accounts or "delicensing" anything. People had to opt IN to be part of "The Stack". Apertus isn't training itself from community code.

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  • [–] 1 point 9 months ago (1 child)

    I'm tired of arguing with you about this, and you're still wrong. It was opt-out, not opt-in, based initially on a GitHub crawl of 137M repos and 52B files before filtering & dedup.

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  • [–] 1 point 9 months ago

    But again, you'd have to set your project to public and your license to "anyone can take my code and do whatever they want with it" before it'd be even added to that list. That's opt-in, not opt-out. I don't see the ethical dilemma here. I'm pretty sure I've found ethical AI, that produces good value for me and society, and I'm going to keep telling people about it and how to use it.

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  • [–] 1 point 9 months ago (2 children)

    your local model wouldn't exist without sauron (openai)

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  • [–] 9 points 9 months ago* (last edited 9 months ago) (1 child)

    People were making LLMs before openai/chatgpt tbf.

    It's the "destroy the environment and economy in an attempt to make something that sucks just enough to justify not paying people fairly so you can advertise to rich assholes gambling their generational wealth" that OpenAI invented for the LLMs.

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