David Krueger @DavidSKrueger
Cambridge faculty - AI alignment, deep learning, and existential safety. Formerly Mila, FHI, DeepMind, ElementAI, AISI. davidscottkrueger.com Joined November 2011-
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I've had a great time collaborating with Hidenori and very excited to see this new initiative launched!
I've had a great time collaborating with Hidenori and very excited to see this new initiative launched!
Money can't buy happiness. Just like an H100. H100 = happiness.
I've finally uploaded the thesis on arXiv: arxiv.org/abs/2404.12150 It ties together a bunch of papers exploring some alternatives to RL for finetuning LMs, including pretraining with human preferences and minimizing KL divergences from pre-defined target distributions.
I've finally uploaded the thesis on arXiv: arxiv.org/abs/2404.12150 It ties together a bunch of papers exploring some alternatives to RL for finetuning LMs, including pretraining with human preferences and minimizing KL divergences from pre-defined target distributions. https://t.co/jq03eRcEhK
OpenAI are losing their best and most safety-focused talent. Daniel Kokotajlo of their Governance team quits "due to losing confidence that it would behave responsibly around the time of AGI" Last year he wrote he thought there was a 70% chance of an AI existential catastrophe.
OpenAI are losing their best and most safety-focused talent. Daniel Kokotajlo of their Governance team quits "due to losing confidence that it would behave responsibly around the time of AGI" Last year he wrote he thought there was a 70% chance of an AI existential catastrophe. https://t.co/KJVJX24wkU
The Future of Humanity Institute is no more. futureofhumanityinstitute.org
LLM-agents seem to be coming fast! But are we ready? Probably not. In our new agenda paper 👇, we discuss what novel safety and alignment challenges are likely to arise as go from ‘chat assistants’ to ‘agents’. 🧵⬇️
LLM-agents seem to be coming fast! But are we ready? Probably not. In our new agenda paper 👇, we discuss what novel safety and alignment challenges are likely to arise as go from ‘chat assistants’ to ‘agents’. 🧵⬇️
Big congrats to my student @usmananwar391 for this!
Big congrats to my student @usmananwar391 for this!
Excited to contribute to a new Agenda Paper on LLM Safety & value alignment. A critical question is what values, and whose values, should we encode in our LLMs? This paper calls for more research on both the technical & social aspects of this important issue. 🧵⬇️
Excited to contribute to a new Agenda Paper on LLM Safety & value alignment. A critical question is what values, and whose values, should we encode in our LLMs? This paper calls for more research on both the technical & social aspects of this important issue. 🧵⬇️
🚨 It's time to rethink finetuning in LLMs 🚨 For something so fundamental to how we train LLMs, finetuning is mysterious and has a lot of shortcomings. 🧵⬇️ x.com/davidskrueger/…
🚨 It's time to rethink finetuning in LLMs 🚨 For something so fundamental to how we train LLMs, finetuning is mysterious and has a lot of shortcomings. 🧵⬇️ x.com/davidskrueger/…
To appropriately account for risks from LLMs, we must correctly estimate and understand their capabilities. But there exist several fundamental challenges that make this difficult. We discuss some of these in our new agenda paper (section 2.2)
To appropriately account for risks from LLMs, we must correctly estimate and understand their capabilities. But there exist several fundamental challenges that make this difficult. We discuss some of these in our new agenda paper (section 2.2) https://t.co/5O6kw1el4y
🚨 Data poisoning attacks are a challenge for LLM Safety! 🚨 LLMs are trained on data from untrusted sources that can be manipulated to inject vulnerabilities. Research on data poisoning on LLMs is limited. We summarize the key challenges 🧵⬇️ x.com/davidskrueger/…
🚨 Data poisoning attacks are a challenge for LLM Safety! 🚨 LLMs are trained on data from untrusted sources that can be manipulated to inject vulnerabilities. Research on data poisoning on LLMs is limited. We summarize the key challenges 🧵⬇️ x.com/davidskrueger/…
I'm delighted to have contributed to this new Agenda Paper on AI Safety * Governance of LLMs can be a v powerful tool in helping assure their safety and alignment. It could complement and *substitute* for technical interventions. But LLM governance is currently challenging! 🧵⬇️
I'm delighted to have contributed to this new Agenda Paper on AI Safety * Governance of LLMs can be a v powerful tool in helping assure their safety and alignment. It could complement and *substitute* for technical interventions. But LLM governance is currently challenging! 🧵⬇️
Usman deserves so much credit for leading and organizing this effort! It's been a long haul, but I'm really happy with the result!
Usman deserves so much credit for leading and organizing this effort! It's been a long haul, but I'm really happy with the result!
Super excited about the release of this 🔥agenda paper on “Foundational Challenges in Assuring Alignment and Safety of LLMs!” that has been described as ‘particularly comprehensive' and 'epic piece of work' in private reviews. 😅
Super excited about the release of this 🔥agenda paper on “Foundational Challenges in Assuring Alignment and Safety of LLMs!” that has been described as ‘particularly comprehensive' and 'epic piece of work' in private reviews. 😅
Can you spot a common thread among the 'dangerous' capabilities listed in arxiv.org/abs/2305.15324? No? They're all closely linked to various forms of 'reasoning'! This is why, in our agenda paper👇, we call for understanding the reasoning capabilities of LLMs! 🧵⬇️
Can you spot a common thread among the 'dangerous' capabilities listed in arxiv.org/abs/2305.15324? No? They're all closely linked to various forms of 'reasoning'! This is why, in our agenda paper👇, we call for understanding the reasoning capabilities of LLMs! 🧵⬇️
Recently we took a dive into the state of the art in LLM robustness, jailbreaks and prompt injection for the Challenges paper. Here are the key research problems we expect to be impactful if you can solve them! (1/8) x.com/davidskrueger/…
Recently we took a dive into the state of the art in LLM robustness, jailbreaks and prompt injection for the Challenges paper. Here are the key research problems we expect to be impactful if you can solve them! (1/8) x.com/davidskrueger/…
Malicious use of LLMs (and other AIs) is a recurring theme across AI Safety literature. However, there is a surprising lack of RIGOROUS research on this! In our new agenda paper, we identify these research gaps and call for developing a rigorous understanding of these risks.🧵⬇️
Malicious use of LLMs (and other AIs) is a recurring theme across AI Safety literature. However, there is a surprising lack of RIGOROUS research on this! In our new agenda paper, we identify these research gaps and call for developing a rigorous understanding of these risks.🧵⬇️
📢 *Call for research into 𝘀𝗼𝗰𝗶𝗮𝗹 𝗮𝗻𝗱 𝗲𝗰𝗼𝗻𝗼𝗺𝗶𝗰 𝗶𝗺𝗽𝗮𝗰𝘁𝘀 of LLMs* LLMs are an amazing technology but might have drastic socioeconomic impacts on our society. In our new agenda paper, we emphasize the urgency of understanding and mitigating these impacts!🧵⬇️
📢 *Call for research into 𝘀𝗼𝗰𝗶𝗮𝗹 𝗮𝗻𝗱 𝗲𝗰𝗼𝗻𝗼𝗺𝗶𝗰 𝗶𝗺𝗽𝗮𝗰𝘁𝘀 of LLMs* LLMs are an amazing technology but might have drastic socioeconomic impacts on our society. In our new agenda paper, we emphasize the urgency of understanding and mitigating these impacts!🧵⬇️
Interpretability methods could be an incredibly powerful tool for assuring the alignment of AI systems, but they currently have a long way to go. We outline *11 open challenges* for interpretability research and propose actionable research questions to tackle them. 🧵⬇️
Interpretability methods could be an incredibly powerful tool for assuring the alignment of AI systems, but they currently have a long way to go. We outline *11 open challenges* for interpretability research and propose actionable research questions to tackle them. 🧵⬇️
In-context learning (ICL) is AMAZING but also RISKY as evidenced by the recent Anthropic work on many-shot jailbreaking. However, we may be able to minimize these risks by understanding ICL better. In our new agenda paper, we list this as the FIRST foundational challenge! 🧵⬇️
In-context learning (ICL) is AMAZING but also RISKY as evidenced by the recent Anthropic work on many-shot jailbreaking. However, we may be able to minimize these risks by understanding ICL better. In our new agenda paper, we list this as the FIRST foundational challenge! 🧵⬇️
Richard Ngo @RichardMCNgo
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158 Followers 186 Following I was told that if I wanted to keep up with the latest research, this was the place to be.
Juma, Lawrence @lalloyce
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Irtiza🥺🤡🔪 @Ertezah
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PhonkNerdyBit @oncs01
17 Followers 332 Following Unleashing CS Inquiry bombs, chill composure, slinging sarcastic comments. Stay sharp, stay savvy. #NoFearInquiry
Tim Lawson @tslwn
46 Followers 146 Following PhD student in AI @BristolUni. Previously physics @Cambridge_Uni and software @graphcoreai. Language, cognition, etc.
a23dxa28xhq90t @peceo5d72
7 Followers 726 Following We first transfer USDT to you TRC20, you return 90% to BEP20, you get 10% , 2K per day Our co hv a large amt of USDT need to from TRC20 convert to BEP20 network
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Linus Härenstam-Niel.. @LinusHNielsen
133 Followers 571 Following PhD student at @TU_Muenchen, working on 3D reconstruction
Aaron Joseph Mathew @AaronJosephMath
45 Followers 931 Following Recommending lots of things to lots of people is my Jam. Think about Data Science and tweet about Rabbits. Tweets are my own and should not be taken seriously
Aaditya ; @Aaditya26082004
526 Followers 7K Following CS'26 • Machine Learning • Open-Source • Web Dev. • Algorithms • Jai Shree Krishna 🦚🪈
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79 Followers 136 Following AI Safety / Mech Interp postdoctoral scholar @KITPUCSB. Former astrophysical fluid dynamicist @Northwestern (CIERA) and @CUBoulder.
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48 Followers 893 Following PhD student @up_poznan Neuroeconomics UASD 🇮🇳 PAU 🇮🇳 IARI 🇮🇳 PULS 🇵🇱…
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@[email protected].. @MilekPl
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Othmane @ThisIsOthmane
336 Followers 3K Following Data Science Engineering Manager at Datadog | Passionate about AI, LLMs, ML, Data Science | #DimaRaja #FreeKoulchi | Opinions are my own
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35K Followers 1K Following What would we need to understand in order to design an amazing future? Figuring that out @openai
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Valentin Hofmann @vjhofmann
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Andreas Vlachos @vlachos_nlp
5K Followers 1K Following Professor in NLP/ML at @Cambridge_CL, Fellow of @FitzwilliamColl, @ELLISforEurope member
Lucy Farnik @lucyfarnik
66 Followers 162 Following Trying not to get killed by AI. @MATSprogram under @NeelNanda5; PhDing. DMs very much open — have a low bar for reaching out!
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180 Followers 245 Following PhD candidate at IIT Madras | Google PhD fellowship | AI | ML | NLP
Arthur Mensch @arthurmensch
40K Followers 873 Following Co-founder and CEO @MistralAI. Apply https://t.co/yHGRZAtjcx
AI for Good 🇺🇳 .. @AIforGood
16K Followers 4K Following The @UN's 🇺🇳 leading platform to scale #AI for global impact. Organized by @ITU in partnership w/ 40 UN agencies and 🇨🇭 #AIforGood
Daniel Faggella @danfaggella
7K Followers 238 Following Host of The Trajectory: Realpolitik on AGI and the posthuman transition (https://t.co/1rCgmOZ2EA). Founder @Emerj AI Research.
Vision and AI Lab, II.. @val_iisc
2K Followers 120 Following We are a team of graduate students working on cutting edge research in Computer Vision, led by Prof. R. Venkatesh Babu
Ewout ter Haar @ewout
815 Followers 696 Following Technology for Education, University of São Paulo "inclusão bátava nas terras tropicais" @[email protected]
Joschka Braun @JoschkaBraun
344 Followers 197 Following Co-founder of @PareaAI (YC S23) • Building Generative AI Developer Tools • @FulbrightPrgrm
ana vldv @ana_valdi
5K Followers 4K Following lecturer in ai, government & policy at the @oiioxford | critical ai studies | co-editor at @bigdatasoc | radical ecology, politics & algorithms | 🚲📖🌻More excited than ever to announce $1.7M: "CBS-NTT Program in Physics of Intelligence at Harvard"! 🧠 With new technology comes new science. The time is ripe to build a better future with "Physics of Intelligence for Trustworthy and Green AI"! 🧵👇news.harvard.edu/gazette/story/…
I've finally uploaded the thesis on arXiv: arxiv.org/abs/2404.12150 It ties together a bunch of papers exploring some alternatives to RL for finetuning LMs, including pretraining with human preferences and minimizing KL divergences from pre-defined target distributions.
I was very impressed with @tomekkorbak's thesis! Some really nice insights into LLM alignment: 1) RL is not the way --> distribution matching let's us target constraints like "generate as many of these as of those" 2) fine-tuning is not the way --> PHF aligns during pre-training
Huge thanks to my advisors @drclbuckley and @anilkseth, my mentors @EthanJPerez, @MarcDymetman, @hadyelsahar, @germank and my external examiner @DavidSKrueger! I feel extremely privileged having had the opportunity to work with and learn from you.
OpenAI are losing their best and most safety-focused talent. Daniel Kokotajlo of their Governance team quits "due to losing confidence that it would behave responsibly around the time of AGI" Last year he wrote he thought there was a 70% chance of an AI existential catastrophe.
US politics is an outlier, and should not be seen as representative of the rich world. Between 2006 and 2023, trust in national institutions increased in all of the G7 except the US, where it fell substantially.
LLM-agents seem to be coming fast! But are we ready? Probably not. In our new agenda paper 👇, we discuss what novel safety and alignment challenges are likely to arise as go from ‘chat assistants’ to ‘agents’. 🧵⬇️
I’m super excited to release our 100+ page collaborative agenda - led by @usmananwar391 - on “Foundational Challenges In Assuring Alignment and Safety of LLMs” alongside 35+ co-authors from NLP, ML, and AI Safety communities! Some highlights below...
We released this new agenda on LLM-safety yesterday. This is VERY comprehensive covering 18 different challenges. My co-authors have posted tweets for each of these challenges. I am going to collect them all here! P.S. this is also now on arxiv: arxiv.org/abs/2404.09932
I’m super excited to release our 100+ page collaborative agenda - led by @usmananwar391 - on “Foundational Challenges In Assuring Alignment and Safety of LLMs” alongside 35+ co-authors from NLP, ML, and AI Safety communities! Some highlights below...
Incredible paper by @usmananwar391 et al. There's still so so much to learn about building safe, predictable, human-compatible AI.
I’m super excited to release our 100+ page collaborative agenda - led by @usmananwar391 - on “Foundational Challenges In Assuring Alignment and Safety of LLMs” alongside 35+ co-authors from NLP, ML, and AI Safety communities! Some highlights below...
A very comprehensive survey of research challenges in AI Safety for LLMs from our lab! If you want to start working on tackling foundational problems pertaining to alignment of LLMs, this paper provides a list of problems you can immediately get started with
I’m super excited to release our 100+ page collaborative agenda - led by @usmananwar391 - on “Foundational Challenges In Assuring Alignment and Safety of LLMs” alongside 35+ co-authors from NLP, ML, and AI Safety communities! Some highlights below...
@DavidSKrueger How do you think this compares to Davidad's Open Problems?
While there has been some encouraging progress, a lot remains to be done to make LLMs sufficiently trustworthy! In particular, we identify four challenges from a socio-technical perspective in a new agenda paper that might cause LLMs to be ‘untrustworthy’ for a user. 🧵⬇️
I’m super excited to release our 100+ page collaborative agenda - led by @usmananwar391 - on “Foundational Challenges In Assuring Alignment and Safety of LLMs” alongside 35+ co-authors from NLP, ML, and AI Safety communities! Some highlights below...
I was really pleased to be involved in this mammoth paper "Foundational Challenges In Assuring Alignment and Safety of LLMs", mostly on the multi-agent section. A lot of work went into this from some very talented coauthors! x.com/DavidSKrueger/…
I’m super excited to release our 100+ page collaborative agenda - led by @usmananwar391 - on “Foundational Challenges In Assuring Alignment and Safety of LLMs” alongside 35+ co-authors from NLP, ML, and AI Safety communities! Some highlights below...
To help you (yes YOU!) get started on multi-agent safety and alignment research; we have assembled this list of *concrete* research questions! Website: llm-safety-challenges.github.io PDF: llm-safety-challenges.github.io/challenges_llm… Credits @Usman @DavidSKrueger @lrhammond @j_foerst
Excited to contribute to a new Agenda Paper on LLM Safety & value alignment. A critical question is what values, and whose values, should we encode in our LLMs? This paper calls for more research on both the technical & social aspects of this important issue. 🧵⬇️
I’m super excited to release our 100+ page collaborative agenda - led by @usmananwar391 - on “Foundational Challenges In Assuring Alignment and Safety of LLMs” alongside 35+ co-authors from NLP, ML, and AI Safety communities! Some highlights below...
Huge new paper, congrats to the authors, very important work
I’m super excited to release our 100+ page collaborative agenda - led by @usmananwar391 - on “Foundational Challenges In Assuring Alignment and Safety of LLMs” alongside 35+ co-authors from NLP, ML, and AI Safety communities! Some highlights below...
If a genie were to give you a magic wand and let you change one thing about LLMs, what would you do? I don’t know about you but I would definitely ask for pretraining to be more aligned! Unfortunately magic wands don’t exist but we can still make pretraining more aligned. How?🧵
I’m super excited to release our 100+ page collaborative agenda - led by @usmananwar391 - on “Foundational Challenges In Assuring Alignment and Safety of LLMs” alongside 35+ co-authors from NLP, ML, and AI Safety communities! Some highlights below...
In our new agenda paper (see @DavidSKrueger tweet for details ☝️) we discuss several great research directions for just this! Pretraining has three basic ingredients: data, objective and model design. Let’s see how we could tinker with these to make pretraining better!
@DavidSKrueger Let’s start with data: LLMs learn bad things because they are present in the data we train them on; filtering such bad data would obviously help with alignment but unfortunately current techniques for doing filtering are pretty crude, making data filtering not very useful!
@DavidSKrueger Developing a better data filtering toolkit would be an obvious win here! It might also be interesting to see if we could use LLMs to edit or add new synthetic data to offset the bad effects of the harmful data.
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