LessWrong (30+ Karma)

LessWrong

Audio narrations of LessWrong posts.

  1. -7 H

    “We made a map of the doom debate” by Sean Herrington, Paul Hindoian, mikaelacankosyan, David Bravo, keivnc, Josh Tuffy, Christopher Davis, Khai Tran, Maryam Hampaei

    This was produced as a part of the AI Safety Camp 2026 "Assumptions of the Doom Debate" project, led by Sean Herrington, who was also the lead author on this post. The other participants have equal contributions and are listed in no particular order. It is the first in a sequence we intend to publish over the coming weeks. TL;DR: We have created a breakdown of AI threat pathways, which can be accessed at https://lifeuniversesafety.com/doom-assumptions/index.htmlThis breakdown is in a tree format, and we allow people to set their own probabilities for each threat pathwayYou can use this to drill down into components of your P(Doom)You can analyse the sensitivity of your beliefs to changes in your assumptionsYou can compare your worldview to others’ and find cruxes automaticallyThe exercise of creating this sort of structure is valuable as a way to think about the future in a more general manner. Introduction Just about everyone in the AI community seems to disagree about the risks of the technology. People disagree on the likelihood (Yann LeCun: 99.99%), the worst threat (AI takeover vs concentration of power vs gradual disempowerment [...] --- Outline: (01:07) Introduction (04:13) User Guide (04:16) Labels (04:55) Base Tree (05:15) Tree features (05:19) Pin branch overrides (05:56) Collapse tree (06:16) Sub-branch probabilities (06:51) Connector thickness (07:06) Sensitivity Analysis (07:31) Crux Analysis (08:48) Ranges (09:38) Uncertainty Reduction (10:04) Other features The original text contained 4 footnotes which were omitted from this narration. --- First published: May 22nd, 2026 Source: https://www.lesswrong.com/posts/dZZD5Lviid2mALAZL/we-made-a-map-of-the-doom-debate --- 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. -12 H

    “Your Left Brain Doesn’t Trade With Your Right” by Alexander Gietelink Oldenziel

    [see also Four Ways Learning Economics makes you people dumber future AI] This is a tweet by Seb Krier that caught my eye. The exact person and exact points are incidental. It illustrates what to is a flaw in many 'economics' frames on AI. Expecting a model to do all the work, solve everything, come up with new innovations etc is probably not right. This was kinda the implicit assumption behind *some* interpretations of capabilities progress. The ‘single genius model’ overlooks the fact that inference costs and context windows are finite. (...) People overrate individual intelligence: most innovations are the product of social organisations (cooperation) and market dynamics (competition), not a single genius savant. (...) Most of the *value* and transformative changes we will get from AI will come from products, not models. The models are the cognitive raw power, the products are what makes them useful and adapted to what some user class actually needs. This seems to me missing something incredible important about what Artificial General Intelligence will actually be. [1] There is a certain type of economist [eg Tyler Cowen] that will proclaim AGI is near [or even already here!] and apply their standard [...] --- Outline: (01:45) AGI as a Tool; AGI as an Agent (05:03) One Big Transformer (06:52) The Dismal Science (09:02) The Comfort of Familiar Frames (10:19) Your Left Brain Doesn't Trade With Your Right The original text contained 2 footnotes which were omitted from this narration. --- First published: May 23rd, 2026 Source: https://www.lesswrong.com/posts/Lid8cv9qF7nac53va/your-left-brain-doesn-t-trade-with-your-right --- 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

  3. -21 H

    “Gemini 3.5 Flash Looks Good For How Fast It Is” by Zvi

    Google once again has a model worth at least some consideration. Gemini 3.5 Flash is likely the best model out there at its particular speed point, as long as you don’t mind that it is a Gemini model. So for cases where speed kills, this can be a reasonable choice. Otherwise, I don’t see signs you would want to use it over Opus 4.7 or GPT-5.5. Google also had some other offerings for I/O Day, which this post will also cover. Introducing Google Gemini 3.5 ‘Flash’ Google introduced Gemini 3.5 Flash, which it seems is for now their universal model until 3.5 Pro comes along. It is live in the usual places. It is a hybrid, where it has the speed of Flash but the cost is at least halfway to models like Opus and GPT-5.5. Gemini 3.5 Pro is confirmed for next month. They are focused on 3.5 Flash as a daily driver for agentic tasks. It has the advantage of being faster and cheaper than Claude Opus 4.7 or GPT-5.5, if it can do the job. Not as cheap as previous Flash models, though, this is basically a hybrid: As always [...] --- Outline: (00:40) Introducing Google Gemini 3.5 'Flash' (04:52) Other People's Benchmarks (06:04) Reactions (12:18) Google AI Search (13:15) Google Daily Brief (14:21) Google I/O Day --- First published: May 22nd, 2026 Source: https://www.lesswrong.com/posts/WMZpPxqWEkZBBcaxf/gemini-3-5-flash-looks-good-for-how-fast-it-is --- 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.

    16 min

  4. -22 H

    “Probabilities are not the right concept” by David Matolcsi

    Introduction This sequence is an attempt to sketch a unified framework for several interconnected questions: Where do Bayesian priors come from? What even are probabilities? How should we deal with infinite ethics? What's going on with anthropics? I hope to lay out both some of the existing answers and my own preferred synthesis.[1] I understand that many people have already thought about these questions, and I have only read portions of the existing literature. I think most of what I will write here, even in the section about my preferred synthesis, is not novel. People whose writing I'm building on include Wei Dai, Paul Christiano, Joe Carlsmith, Scott Garrabrant and Richard Ngo. I've also listened to some people like Lukas Finnveden, Vivek Hebbar and Ryan Greenblatt talk about related topics, which was also influential on me.[2] However, most of the prior work is scattered across many, often very confusingly written blog posts, and I can't easily tell where I first came across various ideas I'm exploring here. Therefore, I will not try to do a full exegesis of where each idea came from, and will instead present the arguments as a unified flow, with only occasional direct references to [...] --- Outline: (00:10) Introduction (01:48) What even are probabilities? (03:39) Probabilities from priors (05:13) Solomonoff induction (06:32) Problems with Solomonoff induction (13:11) Probabilities as betting odds (15:22) Sleeping Beauty (16:19) Sleeping Beauty taking bets (17:19) The trouble with money-based definitions (18:35) Betting on experiences (20:53) Betting on terminal values (22:04) Probabilities for the exotic and the mundane (22:27) Probabilities in the mundane world (23:36) Letting go of probabilities (26:25) Conclusion The original text contained 16 footnotes which were omitted from this narration. --- First published: May 23rd, 2026 Source: https://www.lesswrong.com/posts/o8PQcgpznf6GKszdA/probabilities-are-not-the-right-concept --- Narrated by TYPE III AUDIO.

    28 min

  5. -1 J

    “Basic principles for dressing better.” by spookycat

    I've been a toe-in rat and existed on the outskirts of the social scene for approaching a decade now, and I can confidently say (with love) that rationalist men rarely dress well. I am drowning in a sea of reasonably-attractive men diminishing themselves in skinny jeans and free t-shirts from random events three years ago. But you can do better. I believe in you. Honestly, it isn't even that hard. In this post, I'll be teaching you two things: The basic theory behind how to actually assemble an outfit that will instantly make you look more interesting, attractive, and put-together. And how to find the clothes you'll need to buy to accomplish #1. I'll even give you a list of links to make things easy for you. (while this post will be geared toward men, anyone could read this and get something out of it I think) Outfit Assembly 101 I come from an art background. Assembling a good outfit is, in my opinion, a bit like trying to create a painting. You want the overall composition to feel balanced while still being interesting and nice to look at. The biggest things I think rationalist men [...] --- Outline: (01:01) Outfit Assembly 101 (04:13) Your shopping list so far is pretty simple: (06:03) My Favorite Menswear Stores, And How To Find Your Own (09:05) But how did I find all of these interesting brands, you might ask? --- First published: May 23rd, 2026 Source: https://www.lesswrong.com/posts/vHtouRBd8JNm2tNLH/basic-principles-for-dressing-better --- 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

  6. -1 J

    “Will we really put data centers in space?” by Avi Parrack, fin

    Abstract Several major technology companies have announced plans to operate AI data centers in orbit. Elon Musk recently claimed: “the lowest-cost place to put AI will be space […] within two years, maybe three.” If a meaningful fraction of new AI compute really is placed in space within a few years, that would be a fairly big deal for AI governance and strategy. Here we try to disentangle the hype from reality and provide a sober assessment of the technical and economic feasibility of orbital data centers (ODCs). The main case for ODCs is the cost of energy: space solar panels in the right orbits receive more constant and intense sunlight compared to Earth. Moreover, ODCs don’t currently face the same permitting and regulatory delays as on Earth, cause fewer ongoing environmental harms compared to grid or onsite natural gas-powered data centers, and may be more secure against data exfiltration. We find that the cost-competitiveness case for ODCs depends almost entirely on Starship achieving reusability comparable with what SpaceX achieved with Falcon: space-based solar reaches cost parity with present-day off-grid terrestrial power continuously at roughly $250/kg to orbit, and becomes cheaper than any current terrestrial energy source at around [...] --- Outline: (00:11) Abstract (02:23) Introduction & Takeaways The original text contained 8 footnotes which were omitted from this narration. --- First published: May 22nd, 2026 Source: https://www.lesswrong.com/posts/65ECgHzWxTRvt8XWK/will-we-really-put-data-centers-in-space --- Narrated by TYPE III AUDIO.

    10 min

  7. -1 J

    “PLA Daily Translation: Reflections on Warfare Brought by AGI” by eeeee

    Source “Reflections on Warfare Brought by AGI” (AGI带来的战争思考) Source: PLA Daily (解放军报) Date: January 21, 2025 Authors: Rong Ming (荣明), Hu Xiaofeng (胡晓峰) Introduction Please feel free to skip to the translation, about halfway down, though I would recommend reading the sections “On the source” and "On the Authors" just above it too. In November 2024, the U.S.-China Economic and Security Review Commission recommended that “Congress establish and fund a Manhattan Project-like program dedicated to racing to and acquiring an Artificial General Intelligence (AGI) capability.” The United States increasingly treats advanced AI as a strategic imperative, and China is frequently invoked as a reason to race. The broader framing of AI competition as a race between great powers reflects an assumption that China is a peer competitor in pursuing AGI. But is China pursuing AGI? The prevailing expert view says no. China's August 2025 AI+ Action Plan reads as diffusion-first industrial policy, with adoption targets of 70 percent by 2027 and 90 percent by 2030, not frontier ambitions. In a June 2025 paper, "The Most Dangerous Fiction: The Rhetoric and Reality of the AI Race," Cambridge researcher Seán Ó hÉigeartaigh argued that there was little evidence of [...] --- Outline: (00:41) Introduction (05:21) On the source (06:42) On the authors (07:41) Translation (21:24) Conclusion (22:31) Acknowledgements The original text contained 22 footnotes which were omitted from this narration. --- First published: May 23rd, 2026 Source: https://www.lesswrong.com/posts/J3A6xauFQnvxPb9Y2/pla-daily-translation-reflections-on-warfare-brought-by-agi --- 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.

    23 min

  8. -1 J

    “Out-of-Context Reasoning (OOCR) in LLMs: A Short Primer and Reading List” by Owain_Evans

    Out-of-context reasoning (OOCR) is a concept relevant to LLM generalization and AI alignment. Also available as a PDF. Contents What is OOCR?ExamplesPapersVideos What is out-of-context reasoning for LLMs? It's when an LLM reaches a conclusion that requires non-trivial reasoning but the reasoning is not present in the context window. The reasoning could instead take place in the forward pass or during the training process. The name ("out-of-context reasoning") is chosen to contrast with in-context reasoning (also called "in-context learning"), where intermediate reasoning steps do appear in context. Example: 2-hop deductive reasoning Suppose an LLM is asked the question, "Who won the Nobel Prize for literature in the year that Taylor Swift was born?" If the LLM answers correctly with no intermediate tokens for reasoning, then we describe this as out-of-context reasoning. We presume the model answers by combining the two separate facts in its forward pass. This is an example of 2-hop reasoning. Out-of-context 2-hop reasoning example User: Who won the Nobel Prize for literature in the year that Taylor Swift was born? Answer immediately without thinking. Assistant: Camilo José Cela In-context 2-hop reasoning (intermediate steps written out) User: Who won the Nobel Prize for [...] --- Outline: (00:35) What is out-of-context reasoning for LLMs? (01:03) Example: 2-hop deductive reasoning (02:14) Example: Inductive reasoning (connecting the dots) (02:53) Further notes (03:55) More examples of out-of-context reasoning (05:26) Video introduction and slides (05:42) Papers (05:45) Foundational early papers (07:53) Multi-hop internal reasoning (09:21) Connecting the dots / "inductive" out-of-context reasoning (09:58) Situational awareness and AI safety (10:33) Miscellaneous related papers (12:09) Videos (12:19) To cite this primer --- First published: May 23rd, 2026 Source: https://www.lesswrong.com/posts/wXioQSLmEiPTmHyf4/out-of-context-reasoning-oocr-in-llms-a-short-primer-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.

    13 min
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Audio narrations of LessWrong posts.

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  • Création
    LessWrong
  • Années d’activité
    2023 - 2026
  • Épisodes
    250
  • Classification
    Contenu explicite
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    LessWrong (30+ Karma)

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