Rohin Shah @rohinmshah
Research Scientist at DeepMind. I publish the Alignment Newsletter. rohinshah.com London, UK Joined October 2017-
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Rose: The idea is extremely simple and well-motivated, and the effect sizes are large. Thorn: p=0.05 :( (Tbc, I am very confident we would have reached statistical significance for Gated SAEs being more interpretable, if we had a large enough N.) x.com/sen_r/status/1…
Rose: The idea is extremely simple and well-motivated, and the effect sizes are large. Thorn: p=0.05 :( (Tbc, I am very confident we would have reached statistical significance for Gated SAEs being more interpretable, if we had a large enough N.) x.com/sen_r/status/1…
I loved working with Anca during my PhD, and now I get to do it again! Though there is one downside, she's going to notice how much more lax I've become about really nailing my talks and figures 😅 x.com/ancadianadraga…
I loved working with Anca during my PhD, and now I get to do it again! Though there is one downside, she's going to notice how much more lax I've become about really nailing my talks and figures 😅 x.com/ancadianadraga…
Despite the constant arguments on p(doom), many agree that *if* AI systems become highly capable in risky domains, *then* we ought to mitigate those risks. So we built an eval suite to see whether AI systems are highly capable in risky domains. x.com/tshevl/status/…
Despite the constant arguments on p(doom), many agree that *if* AI systems become highly capable in risky domains, *then* we ought to mitigate those risks. So we built an eval suite to see whether AI systems are highly capable in risky domains. x.com/tshevl/status/…
To estimate impact of various parts of a network on observed behavior, by default you need a few forward passes *per part* -- very expensive. But it turns out you can efficiently approximate this with a few forward passes in total! x.com/janoskramar/st…
To estimate impact of various parts of a network on observed behavior, by default you need a few forward passes *per part* -- very expensive. But it turns out you can efficiently approximate this with a few forward passes in total! x.com/janoskramar/st…
My first @GoogleDeepMind project: How do LLMs recall facts? Early MLP layers act as a lookup table, with significant superposition! They recognise entities and produce their attributes as directions. We suggest viewing fact recall as a black box making "multi-token embeddings”
In our new @GoogleDeepMind paper, we redteam methods that aim to discover latent knowledge through unsupervised learning from LLM activation data. TL;DR: Existing methods can be easily distracted by other salient features in the prompt. arxiv.org/abs/2312.10029 🧵👇
Really excited that this work is finally out! x.com/vikrantvarma_/…
Really excited that this work is finally out! x.com/vikrantvarma_/…
There's nothing like delving deep into model internals for a specific behavior for understanding how neural nets are simultaneously extremely structured and extremely messy. x.com/lieberum_t/sta…
There's nothing like delving deep into model internals for a specific behavior for understanding how neural nets are simultaneously extremely structured and extremely messy. x.com/lieberum_t/sta…
I really enjoyed recording this podcast -- it's very different from my previous podcasts, much more focused on opinions and impressions, rather than specific technical points. x.com/robertwiblin/s…
I really enjoyed recording this podcast -- it's very different from my previous podcasts, much more focused on opinions and impressions, rather than specific technical points. x.com/robertwiblin/s…
We're hiring again, just like last year! Apply here: Research Scientist: boards.greenhouse.io/deepmind/jobs/… Research Engineer: boards.greenhouse.io/deepmind/jobs/… x.com/rohinmshah/sta…
We're hiring again, just like last year! Apply here: Research Scientist: boards.greenhouse.io/deepmind/jobs/… Research Engineer: boards.greenhouse.io/deepmind/jobs/… x.com/rohinmshah/sta…
How can we learn one foundation model for HRI that generalizes across different human rewards as the task, preference, or context changes? Come see at #HRI2023 in the Thursday 13:30 session! Paper: dl.acm.org/doi/10.1145/35… w/ Yi Liu, @rohinmshah, @daniel_s_brown, @ancadianadragan
BASALT is running again -- and this time there's a pretrained Minecraft model for you to finetune!
BASALT is running again -- and this time there's a pretrained Minecraft model for you to finetune!
[AN #173] Recent language model results from DeepMind - mailchi.mp/c7bf1e091608/a…
[AN #172] Sorry for the long hiatus! I'll restart in the near future; for now have a bunch of news - mailchi.mp/56689cc2223c/a…
Richard Ngo @RichardMCNgo
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Rob Miles (✈️ Tok.. @robertskmiles
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Stefan Schubert @StefanFSchubert
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Peter Wildeford @peterwildeford
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Ian @ InfoHunt.ai @Ianyan2023
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Dana Mahmood @deordered
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𝕋𝕒𝕥𝕤𝕦�.. @tatsuru_kikuchi
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Henrietta.SolidGoldMa.. @Bearly_Present
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Sami Jawhar @CyberMonkSam
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Seliem @seliemels
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Tom Bouthillet 🇺�.. @tbouthillet
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Junjie (Jorji) Chen @coderchen01
2 Followers 282 Following
Aaditya ; @Aaditya26082004
531 Followers 7K Following CS'26 • Machine Learning • Open-Source • Web Dev. • Algorithms • Jai Shree Krishna 🦚🪈
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Joel Becker @joel_bkr
2K Followers 2K Following move fast and fix things. 'soccer'-me @MessiSeconds.
Richard Ngo @RichardMCNgo
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Miles Brundage @Miles_Brundage
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Jack Clark @jackclarkSF
68K Followers 5K Following @AnthropicAI, ONEAI OECD, co-chair @indexingai, writer @ https://t.co/3vmtHYkaTu Past: @openai, @business @theregister. Neural nets, distributed systems, weird futures
Jan Leike @janleike
44K Followers 322 Following ML Researcher, co-leading Superalignment @OpenAI. Optimizing for a post-AGI future where humanity flourishes.
Joshua Achiam ⚗️ @jachiam0
14K Followers 948 Following Human. Trying to make safe alchemy machines. Thinking about humanist alchemism (h/alc ⚗️, maybe). Main author of https://t.co/cKuSh210l1
Catherine Olsson @catherineols
15K Followers 1K Following Hanging out with Claude, improving its behavior, and building tools to support that @AnthropicAI 😁 prev: @open_phil @googlebrain @openai (@microcovid)
Katja Grace 🔍 @KatjaGrace
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Chris Olah @ch402
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Vikrant Varma @VikrantVarma_
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Lynette Bye @lynette_bye
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Rachel Freedman @FreedmanRach
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MineRL Project @minerl_official
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Brian Christian @brianchristian
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Daniel Filan research.. @dfrsrchtwts
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Cody Wild @decodyng
2K Followers 131 Following machine learning research engineer; lover of cats, languages, and elegant systems; explorer & explainer at https://t.co/HQSQCz3Tg3…
𝕏iaoHu Zhu⏹️cs.. @neil_csagi
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Sergey Levine @svlevine
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Alex Irpan @AlexIrpan
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Jakob Foerster @j_foerst
14K Followers 820 Following Assoc. Prof in ML @UniofOxford @StAnnesCollege @FLAIR_Ox, dad. Ex: {RS @MetaAI, (A)PM @Google, DivStrat @GS}, ex intern {@GoogleDeepmind, @GoogleBrain, @OpenAI}
Yolanda Lannquist @YolandaLannqist
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Gillian Hadfield @ghadfield
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Center for Human-Comp.. @CHAI_Berkeley
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Adam Gleave @ARGleave
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Jeffrey Ding @jjding99
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Tara Mac Aulay @Tara_MacAulay
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Brundage Bot @BrundageBot
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David Roodman @davidroodman
9K Followers 354 Following Senior advisor @open_phil. Formerly @GiveWell, @CGDev. Views expressed solely my own.
Animal Charity Evalua.. @AnimalCharityEv
5K Followers 484 Following At Animal Charity Evaluators, we find and promote the most effective ways to #helpanimals. We use #effectivealtruism principles to evaluate causes and research.
Ben Kuhn @benskuhn
7K Followers 289 Following Care a lot and try hard • making language models safer @AnthropicAI • prev CTO @WaveSenegal 🐧❤️
Lewis Bollard @Lewis_Bollard
7K Followers 844 Following Farm Animal Welfare Program Officer @open_phil. Views are my own. For more, sign up to my research newsletter: https://t.co/5DX9QNKo3R
Roxanne Heston @RoxanneHeston
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Jacy Reese Anthis @jacyanthis
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William MacAskill @willmacaskill
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Helen Toner @hlntnr
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Ajeya Cotra @ajeya_cotra
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Yann LeCun @ylecun
712K Followers 719 Following Professor at NYU. Chief AI Scientist at Meta. Researcher in AI, Machine Learning, Robotics, etc. ACM Turing Award Laureate.New @GoogleDeepMind MechInterp work! We introduce Gated SAEs, a Pareto improvement over existing sparse autoencoders. They find equally good reconstructions with around half as many firing features, while maintaining interpretability (CI 0-13% improvement). Joint w/ @ArthurConmy
Announcing a progress update from the @GoogleDeepMind mech interp team! Inspired by @AnthropicAI's excellent monthly updates, we share a range of updates on our work on Sparse Autoencoders, from signs of life on interpreting steering vectors with SAEs to improving ghost grads.
1. What are the ethical and societal implications of advanced AI assistants? What might change in a world with more agentic AI? Our new paper explores these questions: storage.googleapis.com/deepmind-media… It’s the result of a one year research collaboration involving 50+ researchers… a🧵
we're hiring: boards.greenhouse.io/deepmind/jobs/…
@GoogleDeepMind I am so happy I get to work with @rohinmshah again!
Great new Google DeepMind paper on evaluating frontier models for dangerous capabilities. The evals cover four areas: persuasion and deception; cyber-security; self-proliferation; and self-reasoning. They test agents, comprised of the model + scaffolding. arxiv.org/abs/2403.13793
In 2024, the AI community will develop more capable AI systems than ever before. How do we know what new risks to protect against, and what the stakes are? Our research team at @GoogleDeepMind built a set of evaluations to measure potentially dangerous capabilities: 🧵
Imagine if employees expected to get all their work for the week done in the one hour weekly meeting with their supervisor? Ridiculous, of course. Most of the work gets done outside of that meeting. Same deal with coaching or therapy.
@Algon_33 @rohinmshah Arbitrarily small, all of them, two forwards and one backwards neelnanda.io/mechanistic-in…
Where Do I Go Live, from my EP, From Afar, is out now! I’ve always wanted to play this song stripped back with just guitars to highlight the intention of the song. Who do we become in the face of loss? 🫂 youtu.be/WFm15yHCZQ4?fe…
My first @GoogleDeepMind project: How do LLMs recall facts? Early MLP layers act as a lookup table, with significant superposition! They recognise entities and produce their attributes as directions. We suggest viewing fact recall as a black box making "multi-token embeddings”
In our new @GoogleDeepMind paper, we redteam methods that aim to discover latent knowledge through unsupervised learning from LLM activation data. TL;DR: Existing methods can be easily distracted by other salient features in the prompt. arxiv.org/abs/2312.10029 🧵👇
Cool work from @GoogleDeepMind alignment on limitations of methods for eliciting a model's beliefs! My key takeaway is that unsupervised methods (eg CCS) rely on "proxy properties" of true beliefs, but other features share these proxies! Eg "agrees with the user" vs "is true"
In our new @GoogleDeepMind paper, we redteam methods that aim to discover latent knowledge through unsupervised learning from LLM activation data. TL;DR: Existing methods can be easily distracted by other salient features in the prompt. arxiv.org/abs/2312.10029 🧵👇
Very nice study of "grokking" from @vkrntv @rohinmshah and colleagues at @GoogleDeepMind "Grokking" is a rapid switch of strategy from perfect memorization (low training loss) to accurate generalization (low testing loss) 🤯 After decades of advancements in the field of neural…
Our latest paper (arxiv.org/abs/2309.02390) provides a general theory explaining when and why grokking (aka delayed generalisation) occurs – a theory so precise that we can predict hyperparameters that lead to partial grokking, and design interventions that reverse grokking! 🧵👇
We come up with a theory of why grokking (delayed generalisation) occurs, leading to new empirical predictions of semi-grokking and ungrokking phenomena! Great work led by @VikrantVarma_ and @rohinmshah, which I'm proud to have contributed to! 🤩
Our latest paper (arxiv.org/abs/2309.02390) provides a general theory explaining when and why grokking (aka delayed generalisation) occurs – a theory so precise that we can predict hyperparameters that lead to partial grokking, and design interventions that reverse grokking! 🧵👇
Our latest paper (arxiv.org/abs/2309.02390) provides a general theory explaining when and why grokking (aka delayed generalisation) occurs – a theory so precise that we can predict hyperparameters that lead to partial grokking, and design interventions that reverse grokking! 🧵👇
What was I getting wrong about deliberate practice? lynettebye.com/blog/2023/7/27…
'The 80k Podcast on AI' is a compilation of interviews that would teach someone an insane amount about AI's promise and risks and what to do about it. (Yes I'm biased, but still it's true.) Here's the 11 episodes that made the cut: 🧵 80000hours.org/podcast/on-art…
A taster's selection on AI. Especially appreciate @rohinmshah 's insight in episode 4 on the messiness of bringing technical solutions to a broad multilateral community. 80000hours.org/podcast/on-art…
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