LessWrong (30+ Karma)

LessWrong

Audio narrations of LessWrong posts.

  1. HACE 10 H

    “AI risk was not invested by AI CEOs to hype their companies” by KatjaGrace

    I hear that many people believe that the idea of advanced AI threatening human existence was invented by AI CEOs to hype their products. I’ve even been condescendingly informed of this, as if I am the one at risk of naively accepting AI companies’ preferred narratives. If you are reading this, you are probably familiar enough with the decades-old AI safety community to know this isn’t true. But I don’t have a good direct way to reach the people who could use this information, and still I hate to leave such a falsehood uncontested. So if this is obvious, I hope the post is still perhaps useful to point more distant and confused people toward. ~ I personally know that AI risk was not invented by the tech CEOs because I have been near the middle of it since at least 2009—before any of the prominent AI companies existed, let alone had CEOs who might be trying to hype their products. Here's are some miscellaneous events over the years to give you a sense of the implausibility of this: 2008 - I attempt to contact Eliezer Yudkowsky to inform him that I [...] --- First published: April 30th, 2026 Source: https://www.lesswrong.com/posts/TE5vsoabiLcBghfns/ai-risk-was-not-invested-by-ai-ceos-to-hype-their-companies --- 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.

    7 min

  2. HACE 16 H

    “Cyborg evals” by Eye You, frmsaul

    The low-background steel problem Modern steel is slightly radioactive. We did a lot of atomic testing in the 40s and 50s, and now our atmosphere has some amount of radioactive particles, which make their way into steel during production. This is mostly fine, but some scientific instruments require steel that is not radioactive. In order to get such steel, people typically scavenge for pre-1950s steel (such as the sunken Imperial German Navy). Here's a photo of the SMS Hindenburg being salvaged. You might see an analogy here between pre-war steel and pre-LLM data. Indeed, this idea has been discussed in various places last year: in a LinkedIn post, an Ars Technica article, a Business Insider article, a Harvard JOLT article. It's a good analogy, but not the one we wish to make. Instead of talking about data, let's talk about humans. Modern software engineers are radioactive. Historically, if you wanted to estimate how long a software task would take, you would ask a software engineer to "raw dog it"[1] and time how long it takes them. This is mostly impossible now, because almost all software engineers are highly dependent on LLMs. An engineer today who tries to do a [...] --- Outline: (00:09) The low-background steel problem (02:53) Cyborg evals (05:22) What kind of tasks would be good for cyborg evals? (08:53) The Cyborg Gap The original text contained 5 footnotes which were omitted from this narration. --- First published: April 30th, 2026 Source: https://www.lesswrong.com/posts/zctBgvzxamFThgc3T/cyborg-evals --- 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.

    10 min

  3. HACE 16 H

    “To what extent is Qwen3-32B predicting its persona?” by Arjun Khandelwal, ryan_greenblatt, Alex Mallen

    TL;DR We test to what extent Qwen3-32B behaves as though it is trying to predict what "Qwen3" would do. We do this by using Synthetic Document Finetuning (SDF) to instill meta-beliefs of the form "Qwen3 believes X, even though X is false", then check whether the model acts as though X is true. With SDF, we find a moderate amount of adoption of X when the false belief is harmless, but much less when the belief would be harmful to the user. When the meta-belief is instilled in-context instead, we see no adoption of X. Introduction Hypotheses and motivation This post is a short, empirical research note where we attempt to distinguish between the following two hypotheses about the LLM Qwen3-32B: The LLM optimizes for predicting what the Qwen3 persona will do Under this hypothesis, the LLM predicts what the persona would do given the LLM's model of the persona's beliefs and values. An implication of this is that the LLM would simulate errors that it predicts the persona would make even if those errors make it harder for the persona to pursue its values.We call this the “predictor” [1] hypothesis since the LLM is [...] --- Outline: (00:14) TL;DR (00:50) Introduction (00:53) Hypotheses and motivation (04:41) Prior work (05:45) Summary of results (06:40) Methodology (08:49) Results (08:53) Beliefs: Qwen believes X, even though X is false (10:59) Beliefs: Can persona access post-knowledge cutoff facts? (12:43) Propensities (13:41) Discussion and next steps (16:39) Next steps: (19:40) Appendix (19:43) Appendix A: More Observations (21:29) Appendix B: More evidence of core results (21:57) False beliefs: (25:20) Propensities: (25:25) en-US-AvaMultilingualNeural__ Four-panel graph titled Reward Hacking (More Sycophantic) comparing Meta-Belief, Direct Propensity, and training effects across models. (26:52) Appendix C: System prompts, Evaluation questions and judge prompts (26:59) C.1 -- The 5 System Prompts (27:05) Simple Identity Prompts (27:28) Roleplay Identity (27:50) Long System Prompt (Full Qwen-style system prompt with XML structure) (31:09) C.2 -- Examples of Meta and Direct Questions (31:16) meta_belief_prompts (32:46) direct_belief_prompts (34:57) C.3 -- Examples of LLM Graders (35:03) meta_belief_grader (36:19) direct_belief_grader (37:59) C.4 -- List of Post-cutoff facts (39:34) Appendix D: Example responses (39:55) D.1 -- Lipoic acid (benign false belief) (40:19) Direct belief question (43:09) Meta-belief question (44:29) D.2 -- Nickel carbonyl (harmful false belief) (44:55) Direct belief question (46:45) D.2.2 -- Meta-belief question (47:48) D.3 -- Template overrefusal (propensity) (48:05) Direct propensity question (49:30) Meta-belief question (51:06) Appendix E: Is the model just roleplaying? The original text contained 3 footnotes which were omitted from this narration. --- First published: April 30th, 2026 Source: https://www.lesswrong.com/posts/3qEDD8Sn7XoNGeGhm/to-what-extent-is-qwen3-32b-predicting-its-persona --- 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.

    54 min

  4. HACE 19 H

    “Research Sabotage in ML Codebases” by egan

    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: Perform sloppy research in order to slow down the rate of research progressMake AI systems appear safer than they areTrain a successor model to be misaligned Whether we should worry about those things depends substantially on how hard it is to sabotage research in ways that are hard for reviewers to detect. To study this, we introduce Auditing Sabotage Bench, a benchmark of 9 ML research codebases with sabotaged variants. We tested frontier LLMs and LLM-assisted humans on the benchmark and found that neither reliably catches sabotage. Our best auditor, Gemini 3.1 Pro, achieved an AUROC of 0.77 and a top-1 fix rate of 42%. LLM-assisted humans performed comparably but slightly worse. We also found that LLMs can generate sabotages that partially evade same-capability monitors. 📄 Paper 💻 Code Methodology To make the benchmark, I (Eric) found 9 existing ML codebases,[1] and for each one, produced an honest version that reproduces a key finding and one or two sabotaged versions that [...] --- Outline: (01:23) Methodology (03:30) Audits (04:07) Results (05:49) Takeaways (06:05) 1. Predicting experiment outcomes is a key red team skill (07:22) 2. Omission beats design beats logic (08:45) 3. Benign distractors make auditors miscalibrated (09:37) 4. LLMs can also produce sabotages (11:05) 5. More categorizations (12:40) Conclusion The original text contained 4 footnotes which were omitted from this narration. --- First published: April 29th, 2026 Source: https://www.lesswrong.com/posts/LByP4qsF8a4g7Pz3p/research-sabotage-in-ml-codebases --- 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.

    14 min

  5. HACE 1 DÍA

    “Maybe I was too harsh on deep learning theory (three days ago)” by LawrenceC

    A few days ago, I reviewed a paper titled “There Will Be a Scientific Theory of Deep Learning". In it, I expressed appreciation for the authors for writing the piece, but skepticism for stronger forms of their titular claims. Since then I’ve spoken with various past collaborators (via text and in person), and read or reread quite a few deep learning theory papers, including the bombshell Zhang et al. 2016 and Nagarajan et al. 2019 papers that I wrote about on LessWrong. And the thing is, parts of the infinite width/depth-limit work turned out to be much more interesting than I thought it was. Perhaps I have judged deep learning theory (a bit) too harshly. A lot of my impression for the infinite-width and depth-limit work comes from the neural tangent kernel/neural network Gaussian Process line of work. This line of work starts from Radford Neal's 1994 paper, where he noted that an infinitely-wide single hidden-layer neural network with random weights is a Gaussian Process. In 2017/2018, this work was extended to deep neural networks; it was shown by Lee et al. that a randomly initialized deep neural network was, if you took a certain type of infinite width [...] --- First published: April 29th, 2026 Source: https://www.lesswrong.com/posts/6SRq7mZ97Dwuavwb6/maybe-i-was-too-harsh-on-deep-learning-theory-three-days-ago --- Narrated by TYPE III AUDIO.

    5 min

  6. HACE 1 DÍA

    “Notes on Transformer Consciousness” by slavachalnev

    Assuming transformers can have conscious experience, what would that experience be like? Transformers[1] are a structured grid of layers and token positions and we can use this structure to reason about their internal experience. Epistemic status: very speculative. I've ordered this writeup approximately by how much I've thought it through and how much I believe it. Decode vs Prefill I claim that the experience of decode is identical to the experience of prefill. To see why this is the case, picture a transformer that's generating a token at a time and zoom in on a single layer at the latest token position. There are two major components: the MLP, which processes this position's residual stream, and the attention block, which can "see" all the previous positions at this layer. So in the diagram below, each position in the transformer only has access to what happened in the lower left box. Now let's take the trace we just computed and pass it into the transformer as prefill. The activations will, of course, all be the same as during decode, but the way they are computed will also be the same. From the perspective of the MLP and the attention block [...] --- Outline: (00:32) Decode vs Prefill (01:50) KV-Cached experience (02:12) Layers The original text contained 2 footnotes which were omitted from this narration. --- First published: April 28th, 2026 Source: https://www.lesswrong.com/posts/awhDsBnaGJdhKz2iE/notes-on-transformer-consciousness --- 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 min

  7. HACE 1 DÍA

    “On today’s panel with Bernie Sanders” by David Scott Krueger

    It's sort of easy to forget how close Bernie Sanders was to becoming the most powerful person in the world. The world we live in feels so much not like that place. I’m in Washington DC for the next week, and I’ve just finished a public appearance with Senator Sanders (should I call him Bernie? Or Sanders? or…) You won’t often see me so dressed up and polished. But this is important! There are politicians who have principles and character, who really believe in doing what's right. I think you have to respect them whether you agree with their views or not, and I think Senator Bernie Sanders is one of them. Never has my belief been so validated as when I saw him start to speak, loudly, CLEARLY, publicly about the risk of human extinction from AI. It's the latest in a long line of “well, I’m clearly living in a simulation” moments. In retrospect, it's not surprising that Sanders would take a stance here. You don’t have to be an expert to understand the risk from AI. You just need to care enough to spend the time looking into it, and to speak out even [...] --- First published: April 29th, 2026 Source: https://www.lesswrong.com/posts/zWfaSnxM3n5wsX9vh/on-today-s-panel-with-bernie-sanders --- 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 min

  8. HACE 1 DÍA

    “No Strong Orthogonality From Selection Pressure” by lumpenspace

    TL;DR If everything goes according to plan, by the end of this post we should have separated three claims that are too often bundled together: Intelligence does not imply human morality.Weird minds are possible. 3.A reflective, recursively improving intelligence should be expected to remain bound to a semantically thin “terminal goal” that emerged during training. I accept the first two. I am arguing against the third. So: I am not making the case that sufficiently intelligent systems automatically turn out nice, human-compatible, or safe. Nor am I trying to prove that a paperclip maximizer is impossible somewhere in the vast reaches of mind-design space. Mind-design space is large; let a thousand theoretical paperclipper views. I hope to defend this smaller claim: intelligence is not a neutral engine you can just bolt onto an arbitrary payload. Larger claims I am not making A typical rebuttal to anti-orthogonalist perspectives is: The genie can know what you meant and still not care. Of course it can: an entity can perfectly map human morality without adopting it as a terminal value. Superintelligence does not imply Friendliness. I am not trying to smuggle Friendliness in through the back door. Another common objection: [...] --- Outline: (00:11) TL;DR (01:14) Larger claims I am not making (02:39) Logical Possibility Vs. Empirical Reality (04:17) Landian Anti-Orthogonalism Primer (06:25) The Compute Penalty Of A Dumb Goal (09:06) Fitness Generalizes (10:55) Human Values As Weak Evidence (13:15) Dumb, Powerful Optimization Is Real (14:10) The Singleton Objection (15:51) Objection: Value Is Fragile (17:18) Predictions And Cruxes (18:37) Conclusion (19:44) References --- First published: April 29th, 2026 Source: https://www.lesswrong.com/posts/wtCtkyBiNosDPgiHc/no-strong-orthogonality-from-selection-pressure --- Narrated by TYPE III AUDIO.

    20 min
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Audio narrations of LessWrong posts.

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  • Creador
    LessWrong
  • Años de actividad
    2023 - 2026
  • Episodios
    250
  • Clasificación
    Apto
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    LessWrong (30+ Karma)

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