Executives working on AI at Microsoft and OpenAI admitted what its critics have been saying all along: Large language models are predatory pieces of technology that have been built on what a Microsoft executive called “an astonishing theft of unprecedented proportions,” and the “largest theft of labor in human history.” An internal Microsoft document said generative AI products have created a “doom loop” that is killing “the entire web.”

Those statements and a series of other mask-off moments feature heavily in an unredacted court filing that was unsealed Thursday in the behemoth New York Times vs OpenAI copyright lawsuit that has been winding its way through the court system for years. In a filing asking for summary judgment (basically, a filing with the court asking it to rule), lawyers for the New York Times laid out a series of admissions made by Microsoft and OpenAI executives in documents and depositions that until now had remained either sealed or redacted at the request of Microsoft and OpenAI.

It’s easy to see why the AI companies wanted to hide this from the public. The statements, taken together, are some of the most damning indictments of the ways LLMs were trained, how they worked, and the immediate threat they pose to human labor. It is a reminder that even as AI becomes more powerful and companies try to shift the narrative to the supposed existential risk of “superintelligent” AI, the tools they have already built were created by stealing from human creativity and labor and are by definition existential threats to the human labor market.


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[–] 50 points 1 month ago (17 children)

Gonna get real weird soon when they run out of rich new training data and they begin to consume their own shit. When that happens their entire model will collapse and if the bubble hasn't popped already that may be what causes it.

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  • [–] 31 points 1 month ago (2 children)

    They're already doing it. They call it distilling when they take it from another llm. Pretty sure most content was already stolen in the early days and they now rely on distillation and stealing new content.

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  • [–] 10 points 1 month ago (5 children)

    Pretty sure they mostly use the pre-AI internet (that they scraped and kept) and synthetic data currently. Probably trying (and failing so far or we'd have heard) to adapt to using video as training material at the moment, but developments there will likely apply to robotics at some point. Here's hoping the current chuds have crashed and burned before then and that some sanity has taken over from unfettered capitalist oligarchs dreams of computer slavery.

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  • [–] 2 points 1 month ago (4 children)

    Why don't you thinj they have not adapted to video as training. Is that not what Flock does?

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  • [–] 3 points 1 month ago (3 children)

    Flock does pattern recognition, a quite old piece of machine learning. Nothing to do with training a large language model or other 'AI' model.

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  • [–] 1 point 1 month ago (2 children)

    They almost certainly use that to train AI. Big tech is aggregating the data from many places.

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  • [–] 1 point 1 month ago (1 child)

    Nah, it's in the name, Large Language Models, what gets marketed as 'AI', are trained on text. That's why they're ripping up secondhand books (and copyright law, but that never stopped them) at the moment. No one has really cracked Large Vision Model yet, although there are some primitive versions being attempted in robotics, mostly they translate to text, which is why general purpose robots aren't a thing yet.

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  • [–] 1 point 1 month ago (5 children)

    model collapse is the endgame. that's the whole point of LLM.

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  • [–] 2 points 1 month ago (4 children)

    What do you mean? Why would collapse be the endgame?

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  • [–] 2 points 1 month ago

    It's either agi or model collapse. For every AI system actually. If you have a high enough adoption you start to muss original data so if your AI is not self sufficient in time it will collapse, because it will be trained on its own data, which just is an incentive loop

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  • [–] 1 point 1 month ago (2 children)

    in a manner of speaking - you always end up there. not by design though. models operate via continuous refinement and you can only optimize a model so much until it is a mess and you need to figure out where to roll back. so you either get shit like semantic drift or variance decay and you can whack a mole it to an extent but then you hit the rlhf wall when the model starts gaming its reinforcement framework and the fat lady sings.

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  • [–] 1 point 1 month ago (1 child)

    gaming its reinforcement framework

    Well put. Never thought of it that way.

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  • [–] 1 point 1 month ago

    the only more or less workable way to keep it under control is maintaining a closed loop small-scale environment - kinda like NotebookLM where you upload documents and that's all there is to work with - outside of that it is a mess.

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