Mimee // smart casual dark and academic

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Mimee // smart casual dark and academic

Mimee // smart casual dark and academic

@MimeeXu

what good can I do with my life if I only know math and computers. Doctorated on ML x security/privacy. Helpful honest and harmless

San Francisco/New York City Katılım Şubat 2014
369 Takip Edilen844 Takipçiler
Gautam Kamath
Gautam Kamath@thegautamkamath·
I added a travel section to my website. Now you know where to get all my hot chicken sando recommendations in real life (though today, I'm looking for tacos 🌮🌮🌮)
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Mina Fahmi
Mina Fahmi@minafahmi·
Pretext demos are beautiful because they treat language as ornament, like the ancient egyptians who covered their world with words
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Rohan Padhye
Rohan Padhye@moarbugs·
Incredibly proud of my (first solo-advised) PhD student @vasumvikram, who joins @AnthropicAI this week in the evals team. Vasu's PhD research uncovered various nuances of generator-based fuzzing, including the finding that coverage guidance is largely unnecessary in the AI age.
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Mimee // smart casual dark and academic
@FazlBarez I was gonna ask actually, do you also see that doing “alignment”, say through sft, encourages the emergence of detectable linear “emotional” components, which do not cover (i.e., it cannot be steered to eliminate) the whole space of the labeled behaviors.
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Mimee // smart casual dark and academic
@aryaman2020 @RishiBommasani I’ve accepted that the AGI road map does not include credit attribution, maybe cuz it’s a relic of academia On this, >=1 of @kchonyc’s students studied emotional contrastive responses by altering prompts to induce ~ feelings (can’t find my notes so apologies if unhelpful)
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Aryaman Arora
Aryaman Arora@aryaman2020·
I’m very glad to see that Anthropic interp has caught up to the idea of generating a bunch of contrastive synthetic data for extracting supervised steering vectors from! It’s unfortunate that there’s no prior work to cite on this…
Anthropic@AnthropicAI

New Anthropic research: Emotion concepts and their function in a large language model. All LLMs sometimes act like they have emotions. But why? We found internal representations of emotion concepts that can drive Claude’s behavior, sometimes in surprising ways.

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Herbie Bradley
Herbie Bradley@herbiebradley·
This is great advice! Though long DM sessions and group chats are a reasonable substitute. Another thing which works well is going through intense periods of only reading on a topic—eg 10-12 hours a day just reading and thinking, before writing a little at the end. Gwern also did this and it's unusually effective: x.com/panickssery/st…
𝚟𝚒𝚎 ⟢@viemccoy

The best thing I did for my intellectual development as a thinker was to keep going into rooms with people who had thought deeply about the things I care about and summon the courage to disagree with them. It forces you to become correct very quickly.

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Mimee // smart casual dark and academic
I had to think about this one. I also observe in the law school/nyc sphere a subtle cultural distain re:AI. There is a consistent fixation on GenAI as “pure theft”, coupled w/ a lack of imagination that goes beyond tech industry distrust. My theory asserts they find AI “ugly”.
Dean W. Ball@deanwball

My theory about why so many on the left remain in denial about AI is that their worldview rests on a load-bearing notion of “the tech industry” as being composed of vapid morons whose accomplishments will always be superficial, never “real,” always based on some grand theft. With social media and search, the theft was manipulation of people’s minds. With Amazon it was worker exploitation. With Apple, it was a mix of these. In the left retelling of the story, no value whatsoever was created from these technologies. All a trick. With AI the “grand theft” in the telling of the left is the use of copyright-protected data in pre-training. This one is a particularly dangerous mindworm for them, since they identify with the “artists and writers” from whom they imagine this training data was “stolen.” This is why things like “mode collapse” from synthetic data, stochastic parrotry, “it can only mimic things it has seen on the web” and similar are so core to the argument for the left: it supports the notion of “tech bro” thieves—who lest we forget, and they never will let us, have no “liberal arts” training!—continuing their unbroken string of robberies. Of course the “grand theft” notion is an old motif on the left, relating as it does to a zero-sum mindset about economics, business, and growth that is. more traditionally associated with the left, though the lines have always been blurry, since the zero-sum mindset is above all else a *human* fallacy and thus a useful tactic in mass politics of all valences. The lines have become especially blurry lately, as has been widely observed. Anyway, the notion that AI *is* a genuinely world-changing technology, that it can “go beyond” its “stolen” training data, breaks this load-bearing conception of the tech industry as vapid and superficial and, more importantly, of the people within it as blood-sucking thieves.

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Brendan Dolan-Gavitt
Brendan Dolan-Gavitt@moyix·
A weird thing that recent models seem to be doing is *very* occasionally making spelling mistakes; e.g. just now "faning" instead of "fanning". I wonder if this is quantization or something sneakier like watermarking?
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Mimee // smart casual dark and academic
@jcz42 Hi Jack, I feel like it is not quite “mathematically equivalent” if its impl is 1)“fundamentally more unstable” (your paper) and 2) uses float16 (muon’s Newton-Schulz uses bfloat16). I thought part of the point of muon was to be stable under bfloat16. Both behaviors got altered.
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Jack Zhang
Jack Zhang@jcz42·
(2) Gram Newton-Schulz solves both of these problems: 1. By iterating on XX^T instead of X in the main loop, almost all matrix multiplications become symmetric and square along the small dimension. 2. We accelerate these symmetric matrix multiplications with optimized symmetric GEMM kernels in CuTeDSL for Hopper and Blackwell, running 2x faster than cuBLAS!
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Jack Zhang
Jack Zhang@jcz42·
We made Muon run up to 2x faster for free! Introducing Gram Newton-Schulz: a mathematically equivalent but computationally faster Newton-Schulz algorithm for polar decomposition. Gram Newton-Schulz rewrites Newton-Schulz such that instead of iterating on the expensive rectangular X matrix, we iterate on the small, square, symmetric XX^T Gram matrix to reduce FLOPs. This allows us to make more use of fast symmetric GEMM kernels on Hopper and Blackwell, halving the FLOPs of each of those GEMMs. Gram Newton-Schulz is a drop-in replacement of Newton-Schulz for your Muon use case: we see validation perplexity preserved within 0.01, and share our (long!) journey stabilizing this algorithm and ensuring that training quality is preserved above all else. This was a super fun project with @noahamsel, @berlinchen, and @tri_dao that spanned theory, numerical analysis, and ML systems! Blog and codebase linked below 🧵
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Brendan Dolan-Gavitt
Brendan Dolan-Gavitt@moyix·
Task was RE and emulation in QEMU of an HP M551dn printer firmware. It now boots with a full emulated network stack
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Brendan Dolan-Gavitt
Brendan Dolan-Gavitt@moyix·
Creating common knowledge: AI just finished a task that I spent ~2 years failing to accomplish back in 2016.
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Mimee // smart casual dark and academic
@daveaitel @moyix respectfully managing chrome crash bugs in 2016 impressed on me that 1. it’s often other teams that are tasked with fixing the bugs (who are ALWAYS overwhelmed). 2. In an ideal state chrome security routes bugs to the ldap who /can/ fix. Low fuzzer actionability=>bottleneck.
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Dave Aitel
Dave Aitel@daveaitel·
Fwiw the problem was never that AI slop was going to overwhelm security teams: the problem was that having their hidden technical debt all called in at once was going to overwhelm them. Chrome having as many bugs as it still does is the perfect case example.
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Séb Krier
Séb Krier@sebkrier·
I occasionally have my doubts about the Bay Area flavoured monoculture of Al hyper-bullishness, but occasionally I look at what the smarmy skeptics are offering and remind myself the alternative is even bleaker. All the confidence, none of the imagination.
nature@Nature

Book review 📚 Artificial-intelligence models will supposedly take over the world, but AI innovator Luc Julia tells Nature that they’re little more than glorified pocket calculators go.nature.com/4lPpuPd

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Mimee // smart casual dark and academic
@yusan_lin @mirrormirror_ai Oh, great idea to source faces for wider distribution 🎉 However, I genuinely believed that the photoshoot was the fun part, where you collaborate, meet industry folks, and see friends while gaining XP. Production was the real reason why shoots take long, not (just) the models.
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Yusan Lin
Yusan Lin@yusan_lin·
Today @mirrormirror_ai is launching the marketplace where fashion models license their likeness and brands get stunning AI-generated imagery featuring real people. Commercially licensed, model-approved. Try our platform: mirrormirrorai.com As a fashion model I used to spend hours on fashion photoshoot sets. I later did my PhD in CS and became a Research Scientist on AI for fashion. I can see clearly that AI image generation is replacing a large portion of my old job. But brands that use AI recklessly have already paid the price. It damages reputations and hurts the bottom line. Putting real people at the core of AI-generated imagery isn't just about avoiding backlash. It's better business. That's what Mirror Mirror AI is built for. Right now, Mirror Mirror AI houses agency-signed models who have graced the covers of Vogue and Harper's Bazaar. You can digitally book them using our fashion-centric AI software, get your campaign done in hours instead of weeks, and never have to fly anyone in. You purchase a license for commercial use upon approval, and the models get paid. Mirror Mirror AI is also opening a global call for independent models from anywhere in the world to apply to be featured on the platform. Work with fashion brands internationally, choose the projects you take on, and earn from your own likeness on your own terms. Selected models will be announced at an exclusive event in New York during @Techweek_ this June. Apply for the open call: mirrormirrorai.com/open-call A huge thank you to our incredible team for pouring their hearts into this launch, and to a16z @speedrun for believing in our vision from the start. We're just getting started.
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