- cecilkorik@lemmy.caEnglish2 months
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
- 2 months
It should be Dot Dot! But it’s Dot Dot Dot! - sanest Bitchard moment
- uuj8za@piefed.socialEnglish2 months
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.
2 monthsSure 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.
I miss the thinking of the Moonshots whatever those were called.
- AbouBenAdhem@lemmy.worldEnglish2 months
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.
- Dran@lemmy.worldEnglish2 months
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.
- AbouBenAdhem@lemmy.worldEnglish2 months
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.
- JGrffn@lemmy.worldEnglish2 months
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.
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.
Okay, but did Google calculate how many dicks they could jerk off for maximum efficiency?
- spy@lemmy.dbzer0.comEnglish2 months
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.
- 2 months
Someone already done a paper on it:
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.
- Thorry@feddit.orgEnglish2 months
Some people will have you believe it’s all about the angle of the dangle, while we all know it’s about length times diameter plus weight over girth divided by angle of the tip squared.
- 2 months
Should be called Middle-Out? That was the algorithm IIRC. Pied Piper was the name of the startup.
Either way, funny shit.
- JAALU@lemmy.worldEnglish2 months
I think Dropbox’s Lepton is the closest thing to a real-world version of SV’s middle-out algorithm
- 2 months
Pretty sure Google can afford to handle… checks math… All of them.
This should come in handy for the recently projected need for 300 GB RAM* in upcoming self-driving cars.
*Not a typo. 😳
- Voroxpete@sh.itjust.worksEnglish2 months
Projected by a company that makes RAM and wants to juice their stock price.
- mr_account@lemmy.worldEnglish2 months
All these upvotes and comments and not one joke about how it sounds like TurboCunt?
- 2 months
There you go, being the change you seek in the world.
- dimjim@sh.itjust.worksEnglish2 months
THANK YOU! My brain literally pronounced it like that and I came to see if anyone else commented it lol
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.
- BetaDoggo_@lemmy.worldEnglish2 months
Neither are true, Micron has been plummeting since their earnings report on the 18th. This might have caused a small dip but it’s nothing compared to the cliff they just fell off of.
