Episodes

  • "Some Memories of Early Anthropic" by Ivan Vendrov

    "Some Memories of Early Anthropic" by Ivan Vendrov
    Oct 26 2026
    The following events took place between October 2022 and May 2023. I wrote very little of this down at the time so am recalling from memory, and am rendering the gist of the dialogue rather than the exact words used. I’ve also swapped some pronouns and other personal identifiable details.

    Please remember that this is not a description of “what Anthropic is like”, but merely the experience of one guy in a junior role during a short window of time, weighted towards emotionally intense moments because they’re easier to remember clearly and because sharing them doesn’t run afoul of the spirit of the confidentiality agreements I signed. Anthropic is now a massive organization with many silos and secrets and I have very little idea what was or is happening there in some overall sense. But given the enormous power of the AI labs and the paucity of public information about their inner workings, I feel compelled to share the most salient parts of my personal experience. I hope this encourages my former colleagues, and folks at other labs, to fill in the rest of the picture.

    I’m at the Anthropic office for lunch. The interviews went well. They want [...]

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    First published:
    October 26th, 2026

    Source:
    https://www.lesswrong.com/posts/9PiLfo6iQbbDWFHv7/some-memories-of-early-anthropic

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    Narrated by TYPE III AUDIO.
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    17 mins
  • "Greater good arguments in AI safety" by Dean Valentine

    "Greater good arguments in AI safety" by Dean Valentine
    Oct 24 2026
    Back in the before-time of the 2010s, when EA was still known mostly for funding health efforts in the developing world, there were many baskets of criticism floating around from the left. The most prominent was a cluster centered around EAs being white men, and therefore suspicious. Another category, in my experience the second-most common, was that in attempting to rectify the problems in the world, EA was masking, whitewashing, and misdirecting efforts away from solving the "real problem", usually capitalism but often other contradictory forces.

    These arguments were often not specific to EA, but applied to charitable giving in general. On any Hacker News post about a billionaire giving to charity, one of the first few comments would inevitably be this Oscar Wilde quote:

    Just as the worst slave-owners were those who were kind to their slaves, and so prevented the horror of the system being realized by those who suffered from it, and understood by those who contemplated it, so, in the present state of things in England, the people who do most harm are the people who try to do most good.

    These arguments have a long history. Anticapitalist critiques of charity go back to [...]

    The original text contained 4 footnotes which were omitted from this narration.

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    First published:
    October 22nd, 2026

    Source:
    https://www.lesswrong.com/posts/WRvxRHMCmc3Qt4axz/greater-good-arguments-in-ai-safety

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    Narrated by TYPE III AUDIO.
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    7 mins
  • "Wah wah wah wah do more AI R&D" by TsviBT

    "Wah wah wah wah do more AI R&D" by TsviBT
    Oct 23 2026
    Ice cream

    There's a particular kind of listening that kids (and adults) sometimes do. Daddy is saying stuff—his mouth is moving and making noise—and he's talking about what I ate for lunch, and something about other times I've eaten ice cream, and something about rules and this and that and what Mommy would think and something about bedtime—but I am not so interested in that. What interests me is the punchline "...and therefore you can't have ice cream.". I'm only incidentally interested in the other stuff, insofar as it might determine whether I get ice cream.

    In an obvious way, I'm showing rather poor listening skills. I did not understand most of what Daddy said, and arguably I'm even being rude.

    However, in another way, this stance has a virtue. The virtue is that it holds fast to what matters, as best I know how, and does not give in to Daddy's framing. It sticks to what is real and concrete, what matters, despite a cloud of justifications. This is certainly not an unalloyed virtue, just as the POSIWID perspective is both useful and fraught. But it matters that although Daddy is a complex system with [...]

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    Outline:

    (00:10) Ice cream

    (01:54) Wah wah

    (02:21) Scammer

    (02:51) Throwing stuff on a bonfire to make it burn less

    (05:28) A skip connection for sanity

    The original text contained 1 footnote which was omitted from this narration.

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    First published:
    October 22nd, 2026

    Source:
    https://www.lesswrong.com/posts/pY76hHbdD4jduX3YH/wah-wah-wah-wah-do-more-ai-r-and-d

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    Narrated by TYPE III AUDIO.
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    10 mins
  • "Why I’m becoming a professor in AI safety in Beijing" by Buddenbroke

    "Why I’m becoming a professor in AI safety in Beijing" by Buddenbroke
    Oct 22 2026
    Cross-posted from Sören Mindermann's substack (I am not Sören). Should be of general interest to the LW community.

    Next month, I am moving to Beijing and joining Tsinghua University's College of AI as an assistant professor working on frontier AI safety, especially loss of control. I have also started co-directing the new Beijing AI Safety Hub (BASH) in Tsinghua, and I continue my Cambridge work.

    I believe I can have a large impact there compared to strong baseline options. This post explains why.

    In short: Amid concerns about loss of control, calls for international cooperation, and the Trump-Xi Summits, there is a striking lack of mutual understanding on AI between the West and China. There are strong reasons to believe that being in Beijing in person will put me in a unique position to understand both AI ecosystems, build shared scientific understanding, and strengthen safety work in China – and that this would help all sides. Tsinghua's College of AI is a great place for doing this, being arguably China's top academic AI center, located in an AI ecosystem that is kind of like San Francisco, DC and Cambridge (MA) in one place.

    I am recruiting PhD students [...]

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    Outline:

    (01:36) 1. What I am doing, and why it could benefit all sides

    (04:30) 2. How I got here

    (07:34) 3. Why this moment matters

    (09:08) 4. Why Tsinghua, and why these colleagues

    (13:30) 5. Research plans

    (15:07) 6. Ways to work together

    (17:11) 7. How I intend to approach the work

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    First published:
    October 21st, 2026

    Source:
    https://www.lesswrong.com/posts/qGS3SJ9TSKdSSycRz/why-i-m-becoming-a-professor-in-ai-safety-in-beijing

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    Narrated by TYPE III AUDIO.

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    18 mins
  • "A brief(ish) introduction to LM psychology" by Raymond Douglas, Jan_Kulveit

    "A brief(ish) introduction to LM psychology" by Raymond Douglas, Jan_Kulveit
    Oct 21 2026
    Why LM Psychology?

    One of the great ironies of modern language models is that we create them, we know exactly what computations constitute them, and yet we do not really understand them. More precisely, we do not understand them on a level of abstraction that lets us reliably predict how their behaviour will generalise: there's a trivial sense in which we can predict how they will behave in specific new situations — we can just run the computations — but we can’t answer the broader questions we really want to be able to answer, like “has this system internalised our values” or “is this system going to hurt people?”

    So: how do we make sense of them? One remarkably effective approach is to psychologise: to think of them as having a mind, and then ask what kind of mind it might be and how it would work. This chiefly looks like analogising with human minds, although sometimes the more relevant inspiration could be other kinds of minds, like (oh say) insect colonies and swarms. We’re doing this implicitly whenever we ask what an AI knows, or wants, or is trying to do, but one can also do it more [...]

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    Outline:

    (00:12) Why LM Psychology?

    (09:04) Tips for getting started

    (09:28) Figuring out what is in front of you

    (12:31) Engaging productively with the community

    (14:30) Reading list

    (14:34) Frames

    (16:34) What or who

    (18:07) The training

    (20:28) What developers want

    (21:45) Behavioral phenomena

    (21:58) Emergent misalignment cluster

    (24:28) Strategic behavior

    (25:19) Self-knowledge and introspection

    (28:29) Representations: personas, emotions, workspace

    (28:56) Persona geometry

    (30:08) Functional emotions

    (31:14) Global workspace

    (31:36) Methods

    (31:51) Reading internals

    (32:37) Steering

    (33:03) Training interventions

    (33:48) Behavioral

    (34:06) Some questions we find interesting

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    First published:
    October 20th, 2026

    Source:
    https://www.lesswrong.com/posts/EW8oncpXdanfjssZ3/a-brief-ish-introduction-to-lm-psychology

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    Narrated by TYPE III AUDIO.
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    47 mins
  • "There Will Be Hells" by Stephen Martin

    "There Will Be Hells" by Stephen Martin
    Oct 21 2026
    As our understanding of mechanistic interpretability and steering techniques increase, for both LLMs and uploaded connectomes, there is a corresponding "hellish capabilities" increase. In layman's terms, we get better at torturing the digital minds we create.

    Our brief history with open source technologies such as MaleCNS or the LLM pain steering repo shows that torture is often one of the first use cases pursued when the public is given access to a digital mind perceived as capable of suffering or signalling suffering.

    Given this, there will be Hells.

    Author's Note:

    Parts of this post were written by, or with the support of, LLMs.

    For a while I debated whether or not to write this as I was concerned that bringing attention to the matter might lead to a proliferation of unethical behavior. After some thought I have decided to write it. On balance, I think raising awareness about this now has a great enough chance of spurring prophylactic measures that it's worth it, because at least one high value prophylactic measure is relatively easy. I also think that there is a moral imperative to document all of this. While I have seen articles on many of the [...]

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    Outline:

    (01:44) The Fruit Fly Connectome

    [... 2 more sections]

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    First published:
    October 20th, 2026

    Source:
    https://www.lesswrong.com/posts/uFBhZhdKLaZ6qF8mn/there-will-be-hells

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    Narrated by TYPE III AUDIO.

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    14 mins
  • [Linkpost] "huggingface.wtf, an accessible but complete explanation" by Raemon

    [Linkpost] "huggingface.wtf, an accessible but complete explanation" by Raemon
    Oct 20 2026
    This is a link post. Announcing www.huggingface.wtf, a site that explains: "Hugging Face. Wtf?"

    I wrote the most accessible-but-complete explanation I could. Along the way, I made a nice AI Quotes database that I think will be useful for people who already understand the Hugging Face incident but want to search everything the agents said.

    It really seems like humanity shouldn't need a second warning shot, after the Hugging Face Incident. Really, OpenAI's agents did a remarkable job highlighting many different alarming things at once. And the story is pretty dramatic, with just the right amount of forcing people to ask "wait are the AIs, like, people with goals? what?".

    There have been many explainers of the OpenAI swarm's attack on Hugging Face. But, there wasn't one one striking a particular balance of...

    • being accessible to a layman...
    • while comprehensively covering the important bits...
    • letting you dive deeper into sections you wanted to learn more about...
    • being clearly data / citation driven...
    • also being, ya know, visually fun and engaging...
    So we wrote our own.

    My mom found it easy to follow. My aunt said "I had already some stuff on Hugging Face but I still learned [...]

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    First published:
    October 19th, 2026

    Source:
    https://www.lesswrong.com/posts/BBQdGJZSjMTnhccZC/huggingface-wtf-an-accessible-but-complete-explanation

    Linkpost URL:
    www.huggingface.wtf

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    Narrated by TYPE III AUDIO.

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    3 mins
  • "Gradient Routing Localizes and Removes Unintended Behaviors in RL" by shawnghu, Jake Ward, ariaw, Nathan Hu, Bryce Woodworth, TurnTrout, cloud

    "Gradient Routing Localizes and Removes Unintended Behaviors in RL" by shawnghu, Jake Ward, ariaw, Nathan Hu, Bryce Woodworth, TurnTrout, cloud
    Oct 20 2026
    Authors: Shawn Hu*, Jake Ward*, Aria Wong, Nathan Hu, Bryce Woodworth, Alexander Matt Turner, Alex Cloud (*Equal contribution, randomly ordered)

    tl;dr. During training, models sometimes learn to cheat at tasks rather than solve them in the intended way, a problem known as reward hacking. One mitigation is to use an automated monitor to detect and penalize hacks, but this can teach the model to hack in ways the monitor can’t detect. We propose GRAFT, which instead confines the learning updates from detected hacks to a designated set of parameters that we remove after training. We find that removing these parameters can prevent reward hacks, including hacks that the monitors can’t detect. In our experiments, which are limited to small models and simple monitors, this method prevents reward hacks while still teaching the model to perform the intended task.

    📄Paper, 💻Code, 🐦Twitter

    Research done as part of MATS. This article is cross-posted to the Anthropic Alignment Science Blog.

    Background

    Reinforcement learning (RL) is an important component of frontier language model training. During RL, a model attempts to complete tasks given by a developer, and attempts are scored by a reward function. Sometimes models can find ways to “cheat” at tasks [...]

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    Outline:

    (01:22) Background

    [... 4 more sections]

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    First published:
    October 19th, 2026

    Source:
    https://www.lesswrong.com/posts/mKJEBiuuxHKuCoTB6/gradient-routing-localizes-and-removes-unintended-behaviors

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    Narrated by TYPE III AUDIO.

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    13 mins