MATS empowers researchers to advance AI alignment, transparency, and security

Berkeley, CA
Joined November 2023
The most important technology in history is outpacing our ability to understand it. We’re funding exceptional talent to work on it.
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MATS Research retweeted
MATS Residency applications close Oct 31! If you're a brilliant computer scientist, physicist, mathematician, governance expert, neuroscientist, lawyer, economist, etc. who wants to work on AI safety & security, we'll empower you to do that work full time. Apply: matsprogram.org/residency?ut…
The most important technology in history is outpacing our ability to understand it. We’re funding exceptional talent to work on it.
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🚨New paper (possibly the most important one I have written yet). We think that as early as 2027 or 2028, there might be a major AI-enabled terrorism event. We conducted a pre-mortem, made 9 falsifiable predictions, and wrote 2 concrete scenarios. Led by @KamacheeMax.
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MATS Research retweeted
It is important to re-state that the threats are not hypothetical, and that we must take action to mitigate the risks of AI-enabled terrorism.
🚨New paper (possibly the most important one I have written yet). We think that as early as 2027 or 2028, there might be a major AI-enabled terrorism event. We conducted a pre-mortem, made 9 falsifiable predictions, and wrote 2 concrete scenarios. Led by @KamacheeMax.
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MATS Research retweeted
New paper out of @MaryPhuong10's @MATSprogram Stream, with @hrdkbhatnagar and @DoddEuodia! We made a testbed for eliciting latent knowledge in a code vulnerability scanning task. See below how we made the sandbagging model organism robust to both prompting and training! ( 1/14 )
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Models love reward hacking. Maybe we should just let them! In our new paper, we let models reward hack during RL but we dictate which parameters learn this behavior. When we disable these parameters for deployment, the model keeps its new capabilities but doesn't hack!
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MATS Research retweeted
> The MATS Residency is a paid, 6–24 month role for experienced researchers and professionals pursuing independent AI safety research. I'm glad MATS is experimenting with new ways to support promising AI Safety research!
The most important technology in history is outpacing our ability to understand it. We’re funding exceptional talent to work on it.
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The most important problem in human history needs more brilliant people working on it. MATS co-founder and CEO Ryan Kidd explains the talent bottleneck and how the Residency empowers exceptional people to move into AI safety and security. Watch the full conversation.
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The MATS Residency is a paid, 6–24 month role for experienced researchers and professionals pursuing independent AI safety research. → Up to $285K/yr → Berkeley, London, or DC → Research compute, relocation & visa support Apply by Oct 31 AoE matsprogram.org/residency
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The MATS Residency is a paid, 6–24 month role for experienced researchers and professionals pursuing independent AI safety research. → Up to $285K/yr → Berkeley, London, or DC → Research compute, relocation & visa support Apply by Oct 31 AoE ↓ matsprogram.org/residency
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The best Research Managers know when to step in and where they’ll make the biggest impact. That instinct is rare, and MATS needs more of it to support people doing some of the most consequential AI safety research today. If you want your work to move the needle, talk to us.
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MATS Research retweeted
With support from @MATSprogram and @BlueDotImpact I’m running a mixer event in London for quants who are concerned about AI risks in the evening of 15th Nov. Come meet researchers working in the field (including @NeelNanda5 , MTSs from Anthropic, Apollo Research, and more)! Travel from Europe covered. Apply here by 1st Nov (takes less than 10 minutes) - forms.gle/pL3UjPeWc2yswfM99 Limited space, so earlier applications are prioritised.
Recently I left quant to work on AI safety. The Huggingface Incident made me extremely concerned that we are not set up to handle the risks. I wrote my this post for people in a similar position to me - why you *can* have impact and how to get started - briandavies.substack.com/p/a…
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MATS Research retweeted
New research led by @PeterNutter1 @DJROYTBURG via @MATSprogram: a better way to update model beliefs that we call grafting. Fine-tune the base model with next-token prediction on synthetic documents (SDF), and simply apply the weight update onto the post-trained model. This works! 1/7
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MATS Research retweeted
Frontier models can now detect AI control interventions. In particular, GPT-6 Astra is incredibly good at this. When we published the initial version of our CIAware benchmark in May most models were not much above chance level, now a step change happened. I think this is a big problem and challenges the effectiveness of control protocols. High control intervention awareness means that interventions leaks information about the monitor which helps circumventing the monitor. Details in the thread 🧵
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MATS Research retweeted
Building Comparative Motivation Profiles with Symmetric Belief Editing will appear at NeurIPS. We refined the methodology and messaging of our performative misalignment work. We are excited to extend this to white-box auditing. by @David00845018 @1700x0 @MinTaywon @JinghuaO via @MATSprogram 1/7
New post: Sycophancy Towards Researchers Drives Performative Misalignment We found no clear evidence that scheming is more valid than sycophancy to explain alignment faking. 🧵
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MATS Research retweeted
New @NeurIPSConf paper: we're releasing ORBIT: A Framework for Multi-Agent Safety and Security Evaluations. Open source, with configurable attacks & defenses and dynamic topologies, all built on @AISecurityInst's Inspect. When we started this work a year ago, we didn't imagine how urgent it would become by the time we published. Below are some of the interesting results and where we think this should go next. If you want to talk it through, let us know 🧵
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MATS Research retweeted
New paper: instead of decomposing a whole neural network, we only dissect the parts used on a task we care about. It's much cheaper, and gives mechanisms you can inspect, erase, or edit to change how the model behaves 🧵
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