Redwood Research blog
Buck Shlegeris, Ryan Greenblatt, Alex Mallen, Julian Stastny, Kerrick Staley, Alexa Pan, James Lucassen, Adam Kaufman, Anders Cairns Woodruff, Aryan Bhatt, Tyler, Eric Gan, Arun JoseWe research catastrophic AI risks and techniques that could be used to mitigate them.
| Platform |  | Pricing | Only free issues | Publishes | Twice weekly |
|---|---|---|---|---|---|
| Issues | 109 | Subscribers | Read | blog.redwoodresearch.org |
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Latest Issues
Recent posts by this newsletter. Browse the email archive.
Research Sabotage in ML Codebases
One of the main hopes for AI safety is using AIs to automate AI safety research. However, if models are misaligned, then they may sabotage the safety research. For example, misaligned AIs may try to:
Recursive forecasting
We’d like to use powerful AIs to answer questions that may take a long time to resolve. But if a model only cares about performing well in ways that are verifiable shortly after answering (e.g., a myopic fitness seeker), it may be difficult...
Fail safe(r) at alignment by channeling reward-hacking into a "spillway" motivation
It’s plausible that flawed RL processes will select for misaligned AI motivations.1 Some misaligned motivations are much more dangerous than others. So, developers should plausibly aim to control which kind of misaligned motivations emerge ...
AI companies should publish security assessments
AI companies should get third-party security experts to assess (and possibly also red-team/pen-test) their security against key threat models and then publish the high-level findings of this assessment: the extent to which they can defend a...
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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
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