
Audio narrations of LessWrong posts.
| Publishes | Daily | Episodes | 500 | Founded | 3 years ago |
|---|---|---|---|---|---|
| Number of Listeners | Categories | PhilosophyTechnologySociety & Culture | |||

I find that a lot of people have trouble with this concept of predicting the next token. And by trouble, I mean that they struggle to understand what it actually means to predict the next token. It seems simpler than it is. Because when you say "pred... more
Thanks to @Jeremy Gillen for reading and commenting on the draft. This was written while I was was funded by the Advanced Research + Invention Agency (ARIA) through project code MSAI-SE01-P005.
I have tried to achieve two goals in this post. The f... more
This was work done by Sukrati Gautam and Neil Shah, and supervised by David Africa as part of the SPAR Research Fellowship.
TLDR:
We find a new way to use consistency training: by “sealing up” the leaky backdoor introduced by the inoculation pro... more
Around July last year I decided I was going to go all in on technical AI safety research. To do that I’d need to get into an AI safety fellowship, quit my job, and sell everything that was in my flat in South Africa (hopefully in that order).
I app... more
This is a short summary of our new paper: arXiv, X thread, code.
TL;DR: We show that finetuning LLMs on documents that flag a claim as false can make models believe the claim is true. This is a general phenomenon that also occurs with other forms o... more
Monitoring coding agents for dangerous behavior using language models requires classifying transcripts that often exceed 500 thousand tokens, but prior agent monitoring benchmarks rarely contain transcripts longer than 100 thousand tokens.
We show ... more
Allergies are a big problem for a lot of people. If you're someone with pollen allergies, maybe you've wondered how people in the distant past dealt with them. After all, a thousand years ago people mostly worked outside all day, in areas where plant... more
[Crossposted from my blog, BlueprintingHeaven.]
Please note that the views I held and thoughts I had during the time immediately after my deconversion are not necessarily ones I have right now. It would be boring and annoying to constantly say this... more
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A thoughtful, analytically framed show that discusses technology, philosophy, and societal implications of AI. Across recent episodes, topics frequently center on AI alignment, governance, safety, and how large language models interact with social narratives, institutions, and regulation. The hosts consistently critique evaluation regimes, explore moral and existential questions around AI, and compare regulatory approaches with practical governance considerations. Viewers likely appreciate rigorous analysis, well-referenced critiques, and nuanced takes on complex tech-policy issues, making it appealing to technically minded listeners who enjoy philosophy and thoughtful discourse about the future of AI and society.
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