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LessWrong

Audio narrations of LessWrong posts.

  1. hace 13 h

    “AI Safety Policy Needs to train Legal Practitioners” by Katalina Hernandez

    I completed my law degree at a working-class London university. In my first year, I was 18 years old, and I was often the youngest person in the room: almost everyone else was a paralegal, clerk or caseworker with years of live files behind them, studying part-time to qualify for the job they pretty much already did. But all four years, he same scene played out over and over: A mature student would answer from experience, and the teacher would say: “No. This is not right.”The mature student would go: “But this is exactly how I dealt with my case yesterday.”The teacher would eventually settle it with something along the lines of “At the end of the day, this is what you need to pass your exam.” The more it happened, the more I understood why people would tell me “nothing in practice happens how they teach it in school”. One term, a practising barrister covered for a teacher. He was in court every morning, and teaching in the afternoons. The first thing he did was telling us to get the practitioner's handbook he used instead, and taught is using examples from his real cases. [...] --- Outline: (01:50) The profile the field is short of (03:07) The GDPR as the obvious example (06:07) "OK, so the EU failed at embedding a gatekeeper for its privacy law. What does this have to do with AI Risk?" (08:53) Taking the silo down The original text contained 2 footnotes which were omitted from this narration. --- First published: July 10th, 2026 Source: https://www.lesswrong.com/posts/MaXtWhyArguty23Mi/ai-safety-policy-needs-to-train-legal-practitioners --- 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.

    11 min

  2. hace 13 h

    “AI #176 Part 1: Doing It Live” by Zvi

    Enough things added up that this week is getting split into two parts. Then on Monday, if all goes as I expect, we’ll cover OpenAI's Sol, aka GPT-5.6. OpenAI also gave us an upgraded voice mode, which I haven’t tried out but early reports are that it is a step change. AI writing, especially Claude writing, is becoming more prominent and harder not to notice, and increasingly a tough read when encountered in the wild. Does anyone care? Or are those who care the weird ones here? This week saw an excellent paper, which I cover in No Space Like J-Space. Technically we also got Grok 4.5. Table of Contents Language Models Offer Mundane Utility. A whole new world. Language Models Gain Unexpected Affordances. Wait, you can just do that? Language Models Don’t Offer Mundane Utility. Things get old. Pay The Man His Money. You have a few more days with marginally free Fable. Huh, Upgrades. Anthropic raises API platform limits. Grok 4.5 Exists. It might be okay for its price. F*** It We’re Doing It Live. OpenAI gives us a big upgrade to voice [...] --- Outline: (00:54) Language Models Offer Mundane Utility (03:21) Language Models Gain Unexpected Affordances (06:12) Language Models Don't Offer Mundane Utility (06:54) Pay The Man His Money (07:37) Huh, Upgrades (07:45) Grok 4.5 Exists (09:12) F\*\*\* It We're Doing It Live (10:24) On Your Marks (11:13) Better Call Sol (22:40) Get My Agent On The Line (23:29) Deepfaketown and Botpocalypse Soon (25:33) Fool Me Twice (27:23) I Like Your Style (33:28) Enough With That Style (36:58) Fun With Media Generation (38:51) Copyright Confrontation (39:30) Cyber Lack of Security (39:52) A Young Lady's Illustrated Primer (45:56) They Took Our Jobs (51:07) Get Involved (52:10) In Other AI News (56:44) Show Me the Money (57:09) Bubble, Bubble, Toil and Trouble --- First published: July 9th, 2026 Source: https://www.lesswrong.com/posts/M9eLyMsH5DLjMYL86/ai-176-part-1-doing-it-live --- 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.

    58 min

  3. hace 16 h

    “How robust are natural language autoencoders to initialization?” by michaelzhang, TurnTrout

    Natural language autoencoders are meant to take in an LLM's activation vector and describe in plain text what the model is thinking. However, its training data collection involves asking Claude to guess what a model might be thinking. How robust are NLAs to these guesses? We change Claude's guesses in various ways and measure the impact on the NLA's statements as well as on reconstruction accuracy. We show that Qwen2.5-7B NLAs have some robustness to irrelevant statements and prevailing sentiments in Claude's guesses. However, if an NLA is initialized with entirely implausible statements, it can nevertheless achieve nearly the same reconstruction accuracy as plausible-initialized NLAs while emitting 99.3% implausible statements. RL does train implausible-initialized NLAs to be slightly more plausible (increasing from 0.08% to 0.7%). But the plausibility of plausible-initialized NLAs decreases from 21% at initialization to 7.6% at the end of training. If our results scale, they cast doubt on the usefulness of NLAs. Produced as part of the MATS program in the summer 2026 cohort of team shard. Terminology A "plausible" explanation is an objectively true statement about the world. For example, given a passage about greyhounds, a plausible explanation of model [...] --- Outline: (02:16) Introduction (05:06) The experimental setup (06:36) The "Carthago delenda est" experiment (08:15) The "I love Carthage" experiment (11:24) The "confabulation" experiment (20:51) The outputs of plausible-initialized and implausible-initialized NLAs (23:41) Limitations (26:17) Conclusions --- First published: July 10th, 2026 Source: https://www.lesswrong.com/posts/LQXWiF8PyJ5ojNsEv/how-robust-are-natural-language-autoencoders-to --- 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.

    28 min

  4. hace 21 h

    “Selective Optimism: a critique of AI 2040” by Richard_Ngo

    Some context for this post: I’ve been working part-time as a consultant for the AI Futures Project over the last year. Most of the work I’ve done for them has involved critiquing and suggesting improvements for their AI 2040 scenario—some of which were addressed, and some of which weren’t. To their credit, they asked me to write up my remaining critiques into a post that would accompany its launch. In the rest of this post I’ll discuss my three biggest high-level criticisms of AI 2040. Before doing so, I want to emphasize that there are many interesting and thought-provoking details in the scenario. I’ve focused on the high-level framing of the scenario because that's where my main disagreements lie; given the scope of these disagreements, it's hard to evaluate the details. Since the AI Futures Project paid me to develop and write this criticism, you shouldn’t take this as a fully unbiased perspective. However, they haven’t reviewed this piece, and in general have been open-minded about receiving criticism (as their request for me to post this today demonstrates). Finally: the preview image for the substack version of this post comes from this video of a dad shouting to his [...] --- First published: July 9th, 2026 Source: https://www.lesswrong.com/posts/BBd2EJywf2xXftyFn/selective-optimism-a-critique-of-ai-2040 --- Narrated by TYPE III AUDIO.

  5. hace 1 día

    “Debate with Self-Play Best-of-N Optimization” by Dewi Gould, Sam Martin, Alejandro Aristizabal, Simon Marshall, Jacob Pfau

    Debate is a proposed protocol for scalable oversight. As tasks outrun direct supervision, labs are increasingly likely to train against protocols like it. Our concern is that, for questions which are hard to verify, models will become more compelling more quickly than they will become more accurate – this could undermine alignment research and safe use. Whilst existing public empirical work mostly focuses on debate as an evaluation protocol (does debate help a judge reach better verdicts?), there is limited work using debate as a reward signal for training. This note is the first in a series aimed at building an open, empirical science of debate training. We show that inference time optimization, via best-of-N (BoN), can be used to iterate on debate protocols – de-risking training runs before committing to RL. By building up a careful, controlled understanding of how optimization pressure interacts with protocols, we lay the groundwork for tackling higher-level questions with confidence. We introduce an inference-time proxy for debate training. Studying debate protocols using BoN allows us to scale optimization on different players independently and identify which parts of the debate game are doing work. We believe that BoN provides sufficient optimization power to [...] The original text contained 9 footnotes which were omitted from this narration. --- First published: July 9th, 2026 Source: https://www.lesswrong.com/posts/hb8pv3zyAHGJpwz9F/debate-with-self-play-best-of-n-optimization --- 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.

  6. hace 1 día

    “How big is the Sun? How could you figure it out?” by Elliott Thornley

    I’m a few chapters into Our Mathematical Universe by Max Tegmark. By this point he's covered the ingenuities of the ancient Greeks, taking my knowledge of physics to within two and a half thousand years of the cutting edge. And what ingenuities they were. A whole series of them, strung together, and culminating in a pretty good estimate of the size of the Sun. I think it's a remarkable feat to even just ask the question ‘How big is the Sun?’ and recognize that it has an answer. The fact that the ancient Greeks actually managed to figure it out (more or less) is astonishing. I don’t know the exact path they took with their reasoning. History is messy. But here's a plausible route they could have taken, basically matching the one you’ll find charted in OMU. Step 1: Discover that the Earth is round. One way to discover that the Earth is round is to wait for a lunar eclipse, where the Earth casts its shadow on the Moon. If you look carefully, you’ll notice that the shadow is curved. Another sign of Earth's roundness is the way that departing ships disappear over the horizon: hull-first and mast-last. [...] --- Outline: (00:57) Step 1: Discover that the Earth is round. (01:37) Step 2: Use the disappearing-ship trick to estimate Earth's size. (02:24) Step 3: Use Eratosthenes' shadow trick to estimate Earth's size again. (03:43) Step 4: Use the similarity of your estimates to surmise that the Sun is really far away. (04:51) Step 5: Use the Sun's distance and apparent size to infer that it's bigger than the Earth. (06:02) Step 6: Use the Earth's shadow on the moon to estimate the Moon's size. (08:15) Step 7: Use the Moon's real size and apparent size to estimate its distance. (08:58) Step 8: Use the Moon's distance to estimate the Sun's distance. (10:24) Step 9: Use the Sun's distance and apparent size to estimate its real size. (10:56) Epilogue The original text contained 3 footnotes which were omitted from this narration. --- First published: July 9th, 2026 Source: https://www.lesswrong.com/posts/Bc4Ch63cx8KHdQABw/how-big-is-the-sun-how-could-you-figure-it-out --- 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.

    12 min

  7. hace 1 día

    [Linkpost] “AI 2040: Plan A” by Daniel Kokotajlo, elifland, Thomas Larsen, romeo, bhalstead, ryan_greenblatt

    This is a link post. For the past year, we at the AI Futures Project have been sinking most of our time into our next big scenario. Now it's done! It's called AI 2040: Plan A. It's called Plan A because it's a recommendation, not a prediction. It's what we think should happen, not what will happen, though we think it's plausible enough to aim for. It's called AI 2040 because in it, they delay the creation of superintelligence to 2040. It would have happened much sooner (in 2030, to be precise) if not for decisive action on the part of the US and Chinese governments. As with AI 2027, summaries don’t really do it justice, since the whole point was to be detailed and comprehensive and work things out step by step rather than rely on high-level abstractions like doom or utopia. Read the scenario at ai-2040.com. You can listen to it on audio, or view it on mobile, but the experience is significantly better on a normal computer. What's next for us? Well, first we are going to respond to comments and otherwise engage with whatever conversation, responses, critiques, etc. that [...] --- First published: July 9th, 2026 Source: https://www.lesswrong.com/posts/pFzctpJBat95SrCyC/ai-2040-plan-a Linkpost URL:https://www.ai-2040.com/ --- 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 min

  8. hace 1 día

    “Announcing our $160M grant from Coefficient Giving” by Geoffrey Irving, Jesse Hoogland, Alex HT, Jacob Pfau, Daniel Murfet, Marco Cozzi, Stan van Wingerden

    We are excited to announce that Resolution (fka Sequent) has a 160 million dollars grant from Coefficient Giving (cG) to put rigorous alignment research on a (closer to) even footing with the frontier labs. We will use it to accelerate progress towards higher-confidence alignment, or to find evidence and obstacles showing why alignment is hard. The grant is structured as a 108 million dollars base plus 52 million dollars conditional on a combination of hiring success and compute needs. The base includes a small regranting budget, which we plan to use both for high-quality non-Resolution alignment research and to give back to shared community infrastructure that we depend on. Coefficient Giving will be our sole funder to start (thank you!); our goal is to raise larger-scale funds from a mixture of sources once we demonstrate success with semiautomated alignment theory and empirics. The time for ambitious alignment funding is now In our launch announcement, we argued that the time to automate alignment is now: frontier systems have finally reached the threshold where they can make nontrivial theoretical progress, and theoretical research has access to more sources of ground truth than empirical metric climbing. There is a second, structural [...] --- Outline: (01:09) The time for ambitious alignment funding is now (02:20) Please join us in speeding up higher-confidence alignment! (03:38) Join us in closing the gap --- First published: July 9th, 2026 Source: https://www.lesswrong.com/posts/HDKQNqiR2gtfMiWsn/announcing-our-usd160m-grant-from-coefficient-giving --- Narrated by TYPE III AUDIO.

    5 min
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Audio narrations of LessWrong posts.

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  • Creador
    LessWrong
  • Años de actividad
    2023 - 2026
  • Episodios
    250
  • Clasificación
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    © 2026 All rights reserved
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    LessWrong (30+ Karma)

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