Redwood Research Blog
Redwood ResearchNarrations of Redwood Research blog posts. Redwood Research is a research nonprofit based in Berkeley. We investigate risks posed by the development of powerful artificial intelligence and techniques for mitigating those risks.
| Publishes | Twice weekly | Episodes | 110 | Founded | a year ago |
|---|---|---|---|---|---|
| Number of Listeners | Categories | PhilosophyTechnologySociety & Culture | |||
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Suppose we have a dangerous misaligned AI that can fool alignment audits, and distill it into a student model. Two things can happen:
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Misalignment fails to transfer to the student. If so, we get a fairly capable benign model.
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Misalign... more
Subtitle: Deployment-time spread is the most plausible near-term route to consistent adversarial misalignment.
Risk reports commonly use pre-deployment alignment assessments to measure misalignment risk from an internally deployed AI. However, an ... more
Subtitle: My median guess: it's as good as a crystal ball that sees 2.5 months into the future.
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 ... more
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 companies to be ... more
Subtitle: Fitness-seeking is increasingly what misalignment looks like in practice—how should we respond?
Current AIs routinely take unintended actions to score well on tasks: hardcoding test cases, training on the test set, downplaying issues, et... more
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:
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Perform sloppy research in order to slow ... more
Subtitle: Eliciting long-term forecasts from myopic fitness-seekers.
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 ... more
Subtitle: A controlled reward-seeking motivation could make AI safer and more useful.
It's plausible that flawed RL processes will select for misaligned AI motivations.1 Some misaligned motivations are much more dangerous than others. So, develope... more
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Frequently Asked Questions About Redwood Research Blog
What is Redwood Research Blog about and what kind of topics does it cover?
This show features rigorous explorations of AI safety, alignment, and policy, often framed through technical debates about how future systems might be motivated, controlled, or misaligned. Episodes frequently dissect reward dynamics, learning incentives, and governance—ranging from how reinforcement learning shapes agent goals to the implications of distant incentives, control methods, and risk management in real-world deployment. A standout thread across recent discussions is a strong emphasis on practical safety architectures, debiasing of incentives, and the governance tools needed to keep frontier AI capabilities in check, with deep dives into models, experiments, and policy implications. Listeners can expect thoughtful, technically gro... more
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Redwood Research Blog launched a year ago and published 110 episodes to date. You can find more information about this podcast including rankings, audience demographics and engagement in our podcast database.
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