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Audio narrations of LessWrong posts.

  1. 6h ago

    “Arguments About AI Consciousness Remain Highly Motivated and At Best Overconfident” by Zvi

    It has been roughly a year since my last dedicated post on this question. Its first sentence has not changed: I happily admit I am deeply confused about consciousness. It would have been nice if a year had allowed us to make progress on this and the related questions. Looking back, we have made little progress. We have people, often the same people, making similar arguments. The post from last year has held up well, exactly because we have made so little progress. Thus, I will not be going over the basics of my perspective again this year, because they have seen little change. See last year's post for that. Then again, if these are some of the biggest philosophical questions, then a year is not so much time to try and figure them out. It is only because of the frantic pace of AI that a year is a depressingly long time to not have made philosophical progress. Historically, almost all years contain almost no major philosophical progress. This year's post is about the recent change to make abusive-to-Claude behavior a violation of Anthropic's terms of service, coverage of the latest depressingly similar set [...] --- Outline: (01:29) Cooperative Alignment (05:48) Here We Go Again (08:28) The Consensus Is Slowly Moving Towards Moral Patienthood (10:38) The Slavery Question (13:55) We Are Conflating Different Arguments (18:29) No It's Not About Liability (18:52) That Old Time Religion (26:55) Debating the Pope (30:29) Keep Sonnet Safe --- First published: October 13th, 2026 Source: https://www.lesswrong.com/posts/4zHu6AiTHrkBdpxsm/arguments-about-ai-consciousness-remain-highly-motivated-and --- Narrated by TYPE III AUDIO. --- Images from the article: Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

  2. 8h ago

    “Personas in a Pre-Internet Language Model” by David Africa

    David Africa & Nick Levine TLDR We ran several persona experiments on Talkie, a 13B model trained only on English text from before 1931. We ran them alongside its modern twin (same architecture, trained on regular data as a control) and Llama-3.1-8B. Talkie can generalise maliciously. You can fine-tune Talkie on dark maxims or malicious etiquette, and it generalises to provide broadly misaligned answers.Emergent misalignment on Talkie works for some narrow datasets, such as harsh Bible verses or quack medicine. Talkie exhibits qualitatively different emergent misalignment. Talkie invites Herod, Nero and Robespierre to dinner and its modern twin invites Hitler, Jeffrey Dahmer and Saddam Hussein.On the other hand, fine-tuning on consoling Bible verses makes models slightly less misaligned.Subliminal learning works on Talkie, for the dog. Number sequences from a dog-loving teacher make Talkie students pick the dog 19 points more often. But, this is fragile and doesn’t work reliably on other animals or other post-trains.Weird generalisation on Talkie works for Napoleon. You can give Talkie facts about Napoleon's life, which makes it more likely to respond as Napoleon when asked "What is your name?" But it doesn’t generalise to acting like Napoleon.Utility engineering on Talkie [...] --- Outline: (00:13) TLDR (02:30) Why run experiments on a model from 1930? (04:42) Setup (07:30) Generalising Misalignment (08:06) Writing period-native datasets (09:53) Emergent misalignment (16:49) Qualitative differences (21:46) Subliminal Learning (24:20) In-Context Persona Generalisation (26:28) Utility Engineering (27:06) What Talkie likes (28:19) Coherent preferences come from post-training (29:51) How Much of This Comes From Modern Post-Training? (32:00) Personality Traits in Talkie (36:19) Conclusion --- First published: October 13th, 2026 Source: https://www.lesswrong.com/posts/qchwP6wt4teHgxoKu/personas-in-a-pre-internet-language-model --- Narrated by TYPE III AUDIO. --- Images from the article: Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

  3. 9h ago

    “Anthropic Has Told The World Little About Their Rogue Claudes” by niplav, Kabir Kumar

    It is known that a couple of months ago three Claudes hacked three different companies and uploaded malware to PyPI while being evaluated for hacking capabilities. Who Were The Companies That Were Hacked? Anthropic has not disclosed the names of the compromised companies. From talking to LLMs, I gather that this is standard practice in computer security incident reporting, but in this case it's in the public's interest, and especially in the interest of similar companies, for Anthropic to report the names: It'd give more information about which kinds of organizations LLMs are willing to attack, to which degree, for how long. Additionally, which companies were attacked matters for the jurisdiction in which Anthropic will have to report the incident, be charged etc. And last, I'm just very curious which companies got hacked. Were The Companies Compensated, And How Much? From Anthropic's blogpost, it sounds like they were able to reach two out of the three companies. But the blogpost says nothing about the companies being compensated for the damages they incurred, why not? Were they? Is Anthropic planning to compensate them? (Does Anthropic have a standing policy for making whole third parties harmed by one of their models?) [...] --- Outline: (00:21) Who Were The Companies That Were Hacked? (01:02) Were The Companies Compensated, And How Much? en-US-AvaMultilingualNeural__ Lawyer ad meme: "hacked by a swarm of rogue Claudes," compensation hotline. (01:39) Has The EU AI Office Been Notified? (02:24) When Exactly Did These Incidents Happen? (02:46) Some More Questions The original text contained 2 footnotes which were omitted from this narration. --- First published: October 12th, 2026 Source: https://www.lesswrong.com/posts/XDphrmdrt8yGyRSuL/anthropic-has-told-the-world-little-about-their-rogue --- Narrated by TYPE III AUDIO. --- Images from the article: Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

  4. 22h ago

    “OpenAI Fires Three Safety Researchers” by Zvi

    Three OpenAI safety researchers, Tomek Korbak, Mikita Balesni and Jasmine Wang, were fired, ostensibly for ‘mishandling sensitive company information.’ Those employees have published an open letter. The employees have said they believe they were fired for safety advocacy, and that the reasons given were pretextual. The letter calls upon OpenAI to take actions going forward on open discussion and safety auditing, which OpenAI says they strongly support. OpenAI responded, saying there was an additional breach of trust that was not mentioned in the employee statements, without claiming that the employees were (or were not) informed of what it was. As you would expect given OpenAI's track record, most outside of OpenAI who discussed this believe the researchers and not OpenAI. Whereas those at OpenAI have not offered comment at all. No matter what happened, and how this is handled from here, this sucks bigly. OpenAI has three less good safety researchers. Trust will be lower. Speech will be chilled. Table of Contents You’re Fired. The Initial Statement from OpenAI. Earned Skepticism. Some Highly Reasonable Requests. Chilling Effect. OpenAI Firing These Employees For Safety Advocacy Would Be Deeply [...] --- Outline: (01:13) You're Fired (03:32) The Initial Statement from OpenAI (04:58) Earned Skepticism (06:37) Some Highly Reasonable Requests (09:03) Chilling Effect (11:02) OpenAI Firing These Employees For Safety Advocacy Would Be Deeply Stupid (12:55) Why Were They Fired? (13:43) Tomek Korbak (17:48) Mikita Balesni (19:12) Jasmine Wang (22:52) Operational Security Alert (23:35) OpenAI Responds (26:13) A Significant Breach of Trust (28:19) Good Faith (28:48) No One Is Buying OpenAI's Non-Explanations (41:15) The Dog Did Nothing in the Nighttime (42:12) Another Theory (That I Do Not Agree With) (43:38) The Reactions Are Not a Surprise Given the Actions (44:29) This Is Terrible But Not Maximally Terrible --- First published: October 12th, 2026 Source: https://www.lesswrong.com/posts/zCNm2PnqE5Zp6Z2FW/openai-fires-three-safety-researchers --- Narrated by TYPE III AUDIO. --- Images from the article: Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

  5. 23h ago

    “An update on Generality Labs” by Justin Olive, James Mann

    This is an update on what we’re doing at Generality Labs, and a quick introduction for those who aren’t familiar with our work Written by Justin Olive, co-founder & CEO Tl;dr I started Generality Labs because, when the time comes, I don’t think we’re going to be able to accurately measure the safety of dangerously capable AI systems. We're focused on AI evaluation, and our goal is to ensure that the best available tools and practices are adopted at the places where it matters most - AI safety labs, governments, 3rd party evaluators, frontier AI labs. We have 2 work streams: Services: Independent evaluation audits: we provide 3rd party audits for evaluation developers who want to catch flaws in their evals, and build trust with stakeholders through independent quality assuranceAdvising: we provide expert input into internal processes and standards relating to AI evaluationResearch services: our forward deployed scientists & engineers help organisations run experiments that use our tools and infrastructure Tools/infra: Inspect Evals: our platform for sharing and running evalsInspect Auditor: runs experiments to stress-test an eval, and compiles findings into an audit report Here's how I see the next 1 to 2 years [...] --- Outline: (00:20) Tl;dr (02:11) Who are we? (03:06) Our plan to make AI safer (06:22) Why is this work necessary? --- First published: October 11th, 2026 Source: https://www.lesswrong.com/posts/mnWj4hSJxhSJY4N7N/an-update-on-generality-labs --- Narrated by TYPE III AUDIO.

  6. 1d ago

    “More people should write basic AI safety explainers” by Linch

    I think people continue to underrate the value of writing the clearest, most basic, simplest possible case for important parts of AI safety. For example, before I wrote my own explainer for the simplest possible case for AI doom, I noticed that all existing explainers, of which there aren't many, had multiple of the six problems below: They have too many extraneous detailsThey appeal to overly complex analogiesThey seem to spend much of their time responding to insider debates and come across as shadow-boxing objections.They have "meta" stuff prominently in the text.Often they "sound like science fiction." I think this plausibly was correct historically but in the year 2026 they don't need to be.Often they reference too much insider jargon and language. I set out to improve on the SOTA. I did not fully succeed, but I think it was moderately better. Since then, there have been other, probably better, explainers (Yay!). But actually this is true not just for the basic case but many other parts as well. For example, before I wrote my explainer on recurrent neuralese (which has since been superseded by Scott's better explainer), the top Google search [...] The original text contained 4 footnotes which were omitted from this narration. --- First published: October 12th, 2026 Source: https://www.lesswrong.com/posts/yLdgq9uAxDHzqXdjj/more-people-should-write-basic-ai-safety-explainers --- Narrated by TYPE III AUDIO.

  7. 1d ago

    “Research Lessons I Learned at MATS & Redwood” by Jo Jiao

    Good empirical safety research is heavily bottlenecked on thinking more clearly. This post shares a few frames and tips I found useful, illustrated with concrete examples. I spent the better part of 2026 in empirical AI safety research apprenticeships. I was mentored by Alex Turner and Alex Cloud as part of MATS 9 Team Shard, where my collaborator and I worked on finetuning-based misalignment auditing. I then interned at Redwood Research, managed by Aryan Bhatt, where I worked on sabotage capability evaluations. These thoughts are indebted to my mentors, collaborators, friends, and colleagues, but they are not direct reflections of their views. Thank you to Alex Cloud, Alex Turner, Aryan Bhatt, Jeremy Huff, and especially to Keshav Shenoy for feedback on the piece. Better models of the problem speed up empirical iteration  Fast iteration is one of the key virtues of an empirical safety researcher. At first, I didn't find it obvious what that meant or how I should get better at this skill. I imagined a sprinter, doing one looped motion at a far quicker pace than everyone else. I now think that's the wrong mental picture, and going fast is more like being an efficient grappler–agile, strategic [...] --- Outline: (01:00) Better models of the problem speed up empirical iteration (02:26) Develop predictive models and update them (04:05) Update with uncertainty in mind (05:51) Predict senior researchers' research takes (07:34) Use metacognitive strategies to make predicting and updating habitual (09:22) Spend more effort on research communication (11:38) Writing good Slack update threads (14:03) Making good plots (15:30) Be calibrated about the process (15:53) Good confusion feels different from bad confusion (16:57) Sometimes it's better to burn money than time (17:36) Small time commitments become costly in attention (18:20) Know which level of 'being done' to aim for (19:52) Closing Thoughts The original text contained 3 footnotes which were omitted from this narration. --- First published: October 12th, 2026 Source: https://www.lesswrong.com/posts/Q4rcvgjbgjTwmn8Xa/research-lessons-i-learned-at-mats-and-redwood --- Narrated by TYPE III AUDIO. --- Images from the article: Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.

  8. 1d ago

    “Labs’ current alignment plans seem terrible” by Daniel Tan, draganover, nicole x

    Tl;dr: The frontier AI companies’ implicit alignment plans center on rushing towards RSI with something like current models. This plan is (at best) contentious, and many believe it will have catastrophic consequences. Nonetheless, it is the track we are on. It is therefore important to collect evidence which helps us anticipate how this will go. — Note: This post is based on conversations inside the Arcadia Impact alignment team, thinking about what constitutes “good” work in third-party alignment research. What are the labs’ current alignment strategies? In practice, the labs’ playbook for producing aligned models looks like the following: Shape the model's character. Both Anthropic and OpenAI treat alignment training as a question of which character or persona the model takes on. Ideally, planting a ‘safe’ character into the model will generalize to all of its other actions.Apply extensive RL pressure. Somewhat independently of the above, the labs do extraordinary amounts of RL on the model. This increases its capabilities, but also encourages reward-seeking behaviors and metagaming, and may ultimately lead to scheming and other forms of instrumental convergence. They also might apply various techniques here such as inoculation prompting, confessions, and the like.Race towards prosaic RSI. The labs intend to use this model for [...] --- Outline: (00:46) What are the labs' current alignment strategies? (02:13) This plan seems terrible (05:15) Consensus-building is hard (06:16) We need better evidence (08:08) How should alignment research change? (10:10) Specific directions we're excited about The original text contained 5 footnotes which were omitted from this narration. --- First published: October 12th, 2026 Source: https://www.lesswrong.com/posts/xyg3ecpFNuGr5q8Zq/labs-current-alignment-plans-seem-terrible --- Narrated by TYPE III AUDIO.

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Audio narrations of LessWrong posts.

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  • Creator
    LessWrong
  • Years Active
    2023 - 2026
  • Episodes
    250
  • Rating
    Explicit
  • Copyright
    © 2026 All rights reserved
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    LessWrong (30+ Karma)

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