
We research catastrophic AI risks and techniques that could be used to mitigate them.
| Platform | Pricing | Only free issues | Publishes | Twice weekly | |
|---|---|---|---|---|---|
| Issues | 114 | Founded | 2 years ago | Last Issue | 8 days ago |
| Active | |||||

Suppose we have a dangerous misaligned AI that can fool alignment audits, and distill it into a student model. Two things can happen:
Misalignment fails to transfer to the student. If so, we get a fairly capable benign model.
Mi...
Risk reports commonly use pre-deployment alignment assessments to measure misalignment risk from an internally deployed AI. However, an AI that genuinely starts out with largely benign motivations can develop widespread dangerous motivation...
This post was drafted by Buck, and substantially edited by Anders. “I” refers to Buck. Thanks to Alex Mallen for comments.
People who work inside AI companies get access to information that I only get later or never. Quantitatively, how bi...
Last week, OpenAI staff shared an early draft of Investigating the consequences of accidentally grading CoT during RL with Redwood Research staff.
To start with, I appreciate them publishing this post. I think it is valuable for AI compani...
Current AIs routinely take unintended actions to score well on tasks: hardcoding test cases, training on the test set, downplaying issues, etc. This misalignment is still somewhat incoherent, but it increasingly resembles what I call “fitne...
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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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