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Google unveils TurboQuant, a new AI memory compression algorithm — and yes, the internet is calling it ‘Pied Piper’ (techcrunch.com)
submitted 7 months ago by to c/technology@lemmy.world
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[–] 58 points 7 months ago (10 children)

Shouldn't it be Nucleus since they are Hooli?

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  • [–] 46 points 7 months ago* (3 children)

    No, that is what it would be if we were using traditional, deterministic compression and using a reversible and verifiable mapping of data. But this is the new era of memetic compression, "Pied Piper" is what everyone remembers from the show, so we compress it to "Pied Piper" to minimize the amount of memetic overhead and allow the smallest possible compression artifact. Like with "AI", it doesn't need to be correct, just close enough for people to think it is! /s

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  • [–] 20 points 7 months ago (2 children)

    Yes, it should be Nucleus. Them calling it PiedPiper is a propaganda campaign to try to earn good will from people. Fuck Google locking down Android.

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  • [–] 4 points 7 months ago (2 children)

    Sure but didn’t the plot line with Nucleus come in a later season?

    Secondly, I am pretty certain the Google logo was always in the opening credits.

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    • [–] 47 points 7 months ago (4 children)

    TurboQuant, meanwhile, could lead to efficiency gains and systems that require less memory during inference. But it wouldn’t necessarily solve the wider RAM shortages driven by AI, given that it only targets inference memory, not training — the latter of which continues to require massive amounts of RAM.

    I didn’t realize the RAM shortage was mostly due to training—I would have thought inference was at least a big a factor.

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  • [–] 26 points 7 months ago (3 children)

    Inference is dirt cheap in comparison. Hundreds to thousands of concurrent users can be served by hardware costing in the high-thousands to low-ten-thousands.

    Training those same foundational models is weeks to months of time on tens to hundreds of millions worth of hardware.

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  • [–] 11 points 7 months ago (2 children)

    Yeah—but in theory you only need to train once, while inference costs are ongoing and scale up with usage.

    I guess it’s ultimately a business decision by AI companies to weigh how often retraining is worth the cost.

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  • [–] 16 points 7 months ago

    Yeah i don't think they ever stop training is the thing. At this point I'd assume they have multiple training pipelines to try different shit out, just queued up to hit the big farms as soon as the last models are done training.

    Resting isn't a thing in capitalism.

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  • [–] 2 points 7 months ago

    Training is constant. None of these models by any of these providers are static. You'll notice that they are releasing new models and new model versions regularly.

    This means that training is happening constantly. It never stops. There's always new shit being trained.

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    • [–] 27 points 7 months ago* (4 children)

    Should be called Middle-Out? That was the algorithm IIRC. Pied Piper was the name of the startup.

    Either way, funny shit.

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    • [–] 24 points 7 months ago (1 child)

    This should come in handy for the recently projected need for 300 GB RAM* in upcoming self-driving cars.

    *Not a typo. 😳

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    • [–] 36 points 7 months ago* (last edited 7 months ago) (5 children)

    Okay, but did Google calculate how many dicks they could jerk off for maximum efficiency?

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  • [–] 10 points 7 months ago (1 child)

    Well that depends on a lot of factors. One of them being the distance of dick to floor of every one they would jerk. Call that D2F.

    Hopefully they thought of it.

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  • [–] [S] 7 points 7 months ago

    Someone already done a paper on it:

    https://ia800308.us.archive.org/32/items/pdfy-tG1MuMpwvrML6QD0/228831637-Optimal-Tip-to-Tip-Efficiency.pdf

    Abstract A probabilistic model is introduced for the problem of stimulating a large male audience. Double jerking is considered, in which two shafts may be stimulated with a single hand. Both tip-to-tip and shaft-to-shaft configurations of audience members are analyzed. We demonstrate that pre-sorting members of the audience according to both shaft girth and leg length allows for more efficient stimulation. Simulations establish steady rates of stimulation even as the variance of certain parameters is allowed to grow, whereas naive unsorted schemes have increasingly flaccid perfor- mance.

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    • [–] 11 points 7 months ago

    was published over a year ago

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    [–] 20 points 7 months ago (3 children)

    All these upvotes and comments and not one joke about how it sounds like TurboCunt?

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    • [–] 4 points 7 months ago (1 child)

    Funny thing that came with this: apparently Micron's stock fell off a cliff, and apparently so did RAM prices? Can't confirm that later one.

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