Seth Neel

1.1K posts

Seth Neel

Seth Neel

@SethInternet

📍something new prev: rs @googleai (gemini data), asst professor @harvard (biz + cs affil), co-founder @welligence, PhD @penn

NYC x Bay Area Присоединился Ağustos 2016
730 Подписки1.8K Подписчики
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Seth Neel ретвитнул
Jeff Dean
Jeff Dean@JeffDean·
I'm delighted to jointly author this year-end summary of research advances with @DemisHassabis and James Manyika, on behalf of all of our colleagues across @GoogleDeepMind, @GoogleResearch and @Google. We look at research advances across eight different areas. These summaries are always fun to work on because one can reflect back on the breadth and depth of our collective work over the last year! blog.google/technology/ai/…
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Joe Melkonian
Joe Melkonian@joemelko·
please enjoy a video of me asking people to be more thoughtful about data work: thanks @gopalkraman for inviting me and to everyone who asked questions / came by to chat after :)
Gopal@gopalkraman

.@joemelko argues that data curation should be treated as an optimization problem, not guesswork. he walks us through how to learn ... how to learn.

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Reza Shokri
Reza Shokri@rzshokri·
Grateful and happy to receive the ACM CCS @acm_ccs Test of Time Award for our “Privacy-Preserving Deep Learning” paper with Vitaly Shmatikov back in 2015. First reaction: “10 years, man!”
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Seth Neel
Seth Neel@SethInternet·
🏛️ Governance for the LLM era. Audit payout splits, credit for specific responses, and unlearning claims—without a million-dollar retrain. Check out our paper: arxiv.org/pdf/2508.10866 Joint work Ari Karchmer & Martin Pawelcyzk
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Seth Neel
Seth Neel@SethInternet·
🚨 Why unverifiable attributions are risky This computational disparity creates a critical trust gap. If payouts or compliance hinges on the accuracy of these attributions, model providers can in theory game naive checks based on MSE: • Repayment/underpayment: scale all scores down → everyone underpaid. • Favoritism: inflate a subset’s scores → friends overpaid, others shorted.
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Seth Neel
Seth Neel@SethInternet·
🧪New 📜in NeurIPS '26: data attribution could power data dividends, safety checks, debugging, but existing attribution methods are $$ for LLMs. We develop a new protocol that lets computationally weak third parties verify the accuracy of data attributions w/o computing them!🧵
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Seth Neel
Seth Neel@SethInternet·
✅ Our fix: cheap, rigorous verification. A two-message interactive protocol with PAC-Verification guarantees. The Verifier’s cost is O(1/ε²) (and independent of dataset size N), certifying the reported attribution is ε-close to optimal—closing gaming loopholes. We provide strong soundness guarantees, even against an arbitrarily malicious Prover with infinite compute.
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Seth Neel
Seth Neel@SethInternet·
🧩 Broad coverage. Our method applies to arbitrary linear functionals over {±1}^N, capturing predictive training-data attributions (e.g., datamodels, empirical influence) and extending to component attributions used in practice.
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Seth Neel
Seth Neel@SethInternet·
✅ Our fix: cheap, rigorous verification. A two-message interactive protocol with PAC-Verification guarantees. The Verifier’s cost is O(1/ε²) (and independent of dataset size N), certifying the reported attribution is ε-close to optimal—closing gaming loopholes
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Seth Neel
Seth Neel@SethInternet·
@pratyushmaini congrats pratyush + team! this looks like amazing work!
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Pratyush Maini
Pratyush Maini@pratyushmaini·
15/Needless to say, such a massive undertaking could not have been accomplished without a stellar engineering team that helped us scale our work to trillions of tokens. If you are excited about this, join us jobs.ashbyhq.com/DatologyAI
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Pratyush Maini
Pratyush Maini@pratyushmaini·
1/Pretraining is hitting a data wall; scaling raw web data alone leads to diminishing returns. Today @datologyai shares BeyondWeb, our synthetic data approach & all the learnings from scaling it to trillions of tokens🧑🏼‍🍳 - 3B LLMs beat 8B models🚀 - Pareto frontier for performance
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Google AI
Google AI@GoogleAI·
Gemini 2.5 Flash-Lite is now stable and generally available for developers and enterprise customers! ⚡ When designing a Gemini model, we think a lot about the tradeoffs between quality, cost, and latency. Previously with 2.0 Flash-Lite we optimized for cost-efficiency over latency. As we built our next iteration, we also wanted to push the boundaries on latency to see how fast we could get the model to think and respond. Resulting in 2.5 Flash-Lite, our fastest, most cost-efficient 2.5 model yet, with lower latency than both 2.0 Flash-Lite and 2.0 Flash on a broad sample of prompts. Try it out in ai.studio and @GoogleCloud Vertex AI.
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Niloofar
Niloofar@niloofar_mire·
📣Thrilled to announce I’ll join Carnegie Mellon University (@CMU_EPP & @LTIatCMU) as an Assistant Professor starting Fall 2026! Until then, I’ll be a Research Scientist at @AIatMeta FAIR in SF, working with @kamalikac’s amazing team on privacy, security, and reasoning in LLMs!
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