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@METR_Evals - Opinions my own. Anonymous feedback: https://xitter.twitterapi.xyz/t.co/0k6Duym4fI

Berkeley, CA
Joined August 2022
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Personal news: after two years at @EpochAIResearch, I’ve joined @METR_Evals to work on embedded assessments of AI R&D acceleration: going inside AI developers to measure how much AI is speeding up AI development, and publishing our findings.
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One of the reasons I think this is only fairly convincing, but far from knockdown is that I suspect there's a huge amount of context overhang: that the main reason AIs do poorly on many white collar tasks is that they haven't been given same amount of context as humans.
I find "TEDAI is just so many tasks. We only have evidence about trends and levels on a relatively small number of tasks." fairly convincing.
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This suggests that startups that integrated AI from the very start should soon/now outperform incumbents.
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This also predicts impressive results from @coreauto @rtwlz's AGM coreauto.com/AGM
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Note the 3 month prep clause in AIFP's TED-AI definition: "A TED-AI is an AI system that could, if dropped into the present day & given the resources of a large tech company & three months to prep, fully automate 95% of remote work jobs in the US."
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2 ways in which pre-training-software-progress, inference efficiency, or data efficiency would be evidence for "AI progress on well-specified tasks is accelerating" ① They are intermediate factors in the (compute, data, labor, etc.) → AECI production function. So if they accelerate it's more likely that we'll see AECI accelerate in the future. I would find it hard to update a lot on this without knowing a lot more about the production function. ② They are themselves examples of "well-specified tasks". So them going to the moon provides early evidence of how much faster well-specified tasks can get done if you actually try.
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This is a great result, and I am very excited about the Oversight Foundation Models agenda.
Finetuning LLMs can sometimes cause surprising side effects. Can we predict a finetuned model's response from its training data before burning compute? We trained oversight models to do this and found performance follows power laws in model size and data. 🧵
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Replying to @whitfill_parker
many metrics of AI progress are linear over time, including log(METR time horizon). however, some, like exp(ECI), grow exponentially
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How it started / How it’s going
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jsd retweeted
We reviewed the “Risks from automated R&D” section of Anthropic’s August 2026 Risk Report, with access to the nonpublic version. We focus on the report’s claims about catastrophic risks from automated AI R&D and about whether Anthropic had crossed relevant capability thresholds.
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jsd retweeted
Since August, a team within METR has worked with internal Anthropic information to assess whether AI itself is accelerating AI development. We are now publishing a report summarizing the results of this risk assessment.
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I think Confidence Intervals are too often a ritual where the person adding them doesn’t actually know what question they’re answering, it’s just a thing you do.
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we went as golden retriever bf black cat gf for halloween
🤖 Made with AI
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In recent experiments, we found that GLM 5.3 can autonomously compromise recent versions of Mozilla Firefox using security fixes that have been published but not yet shipped to users. We used a deliberately basic setup meant to emulate a relatively unsophisticated attacker with limited resources. After basic initial instructions to inspect those not-yet-shipped fixes, the model reliably built successful exploits for six recent Firefox versions on its own using only information available at the time of release. These exploits, which cost about $1,000 per compromised version, would let an attacker steal passwords and authentication cookies stored in the browser, or read any files on the user's computer, after the user opens a single webpage, with no further interaction. The exploits make use of the "patch gap," the time between when security fixes are made public in beta or "nightly" software updates and when those fixes are generally released to users. Until now, exploiting it required significant human time and skill, or special access to frontier models with advanced cyber capabilities. Our results show that widely available open-weight models can now do this relatively cheaply and quickly. We gave GLM 5.3 up to 48 hours to find an exploit, but its fastest attempt needed only 12 hours. In all of its successful attacks, the patch gap gave GLM 5.3 an initial foothold in the browser. To turn that foothold into reading files off the user's computer, the model also independently discovered and used a previously unpublished vulnerability. We shared our findings with Mozilla, which patched the vulnerability in its October 27 Firefox 159.0 release. We haven't yet confirmed whether the attack still succeeds without it, but expect the fix makes it substantially harder. We're grateful to Mozilla for their responsiveness and collaboration.
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I’m moving to live closer to the Trader Joe’s
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I’m building a team in DC. Everyone in Washington has a stake in ensuring that reliable info about alignment and RSI reaches the public. We need trustworthy communicators who can explain METR's loss-of-control risk assessments and goals to policymakers.
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I’m very grateful to everyone I worked with at Epoch. As I leave, I especially want to thank @merilalama, Epoch’s COO. Operations work too often goes unsung, but her judgement and hard work are a huge part of why Epoch has accomplished so much.
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Both orgs are hiring. If you want to help the world understand where AI is headed, consider applying to Epoch epoch.ai/about/careers or METR metr.org/careers
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