We research catastrophic AI risks and techniques that could be used to mitigate them.
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blog.redwoodresearch.orgLatest Issues
AI Futurism Reading List
We recently ran a strategy fellowship through Astra. As part of this, we ran a reading group for our fellows on some of the topics that we think are important for thinking about AI futurism (key dynamics in AI development, existential risk...
The distillation double bind: Distilling misaligned models either transfers misalignment or it doesn't
Suppose we have a dangerous misaligned AI that can fool alignment audits, and distill it into a student model. Two things can happen:
- Misalignment doesn’t transfer to the student. If so, we get a fairly capable benign model, which we...
Estimating No-CoT Task-Completion Time Horizons of Frontier AI Models
by Francis Rhys Ward, Dewi Gould, Anders Cairns Woodruff et al. (see full author list at the end)
About a year ago, METR showed that the length of tasks frontier models can reliably complete doubles every few months. A related safety-relev...
Efficient tradeoffs and the safety-usefulness tradeoff model
I often use what I’ll call the “safety-usefulness tradeoff model”, which is: developers face a tradeoff between “safety” and “usefulness” of an AI deployment, and the developer has only limited willingness or ability to sacrifice usefulness...
Retrying vs Resampling in AI Control
We’ve just released a new paper: Retrying vs Resampling in AI Control. We revisit the resampling protocols introduced in Ctrl-Z with an up-to-date setting and much stronger models, and compare them against “retrying” protocols similar to Cl...
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Authors
The writers behind this newsletter.
I work at Redwood Research on technical AI security+safety research.
Redwood Research
Senior MTS @ Redwood Research
I work on AI X-risk
Working on AI Safety
Helping navigate transformative AI
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