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319 posts


@paulg @NickKristof That isn’t the problem. The problem is stealing books
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@NickKristof Is it piracy if someone learns from what he reads? And if not, why is it piracy if someone writes a program to do this?
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Glad to see this effort to tackle intellectual piracy; without proper protections, the business model for reporting news is at risk, in ways that affect all society.
NYTimes Communications@NYTimesPR
Today over 700 creators, actors, musicians, songwriters and authors launched a campaign to protest A.I. companies' theft of their intellectual property to train and build their products. This is an issue that impacts all creators. nytco.com/press/the-time…
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@mattparlmer I've had significantly more friends mugged in SF than mugged in Detroit and they lived in Detroit.
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I’m actually from the upper Midwest, and I gotta say, this is correct, ambition is typically met with crab bucket behavior
In my cultural niche that takes the form of heavy guilt trips and baseless fearmongering about coastal urban areas
Fred Utah@Sleeping_Man79
@mattparlmer This is nice to hear. Most folks in those states don't really want you coming there.
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@TylerAlterman sounds synonymous with “low trust society”
hyperindividualistic - identify with no one, everyone is potentially an adversary
external locus of control - paranoia, i’m being attacked mentality
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@RNULLEDW “I’m disempowered because of [force outside myself]”
Eg ancestral trauma, capitalism, etc
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@mmbronstein what a great photo of my favorite geometric ML researcher and advocate
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@flawedaxioms ideally the selection process acts as a rate limiter for certain social groups. while it is a certain kind of fair to only serve those with the highest scores, it may also starve other groups.
it is a power that the universities have created using their brands.
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@francoisfleuret @Tenkaizen8 Is the final output a product of the distilled model or the original large model?
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TL;DR: Chains-of-thought make the amount of compute during inference the key bottleneck.
We show that (1) you can distill a large model into a faster SSM or hybrid one for that task, and (2) the resulting setup reach a better trade-off than using a full-fledge model.
Daniele Paliotta@DanielePaliotta
📢 New Paper: Thinking Slow, Fast: Scaling Inference Compute with Distilled Reasoners. w/ Junxiong Wang, @MatPagliardini, @kevinyli_ , @avivbick, @zicokolter, @_albertgu, @francoisfleuret , @tri_dao. Paper: arxiv.org/abs/2502.20339 pic.x.com/NyIzBefWcR 1/N
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