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Audio narrations of LessWrong posts.

  1. hace 3 h

    “Childhood and Education #20: Phones and Screens” by Zvi

    We have a respite, so I thought I’d tackle various thoughts on children, phones and screens. GPT-5.6-Sol drops tomorrow, and the Fable agents are hard at work. I’ll start with the other screens, then finish with the phones. Table of Contents EdTech. NonEdTech. Do Not Ban Social Media Outright. Some Modern Kids Media Is Pretty Great. Ban Phones In Schools (1). Your Offer Is Acceptable. Ban Phones In Schools (2). Screen Time. Inappropriate Content. EdTech Increasingly, when you pick a school, you are picking EdTech. The school will put your child on a tablet or computer, and expect them to learn that way. In theory, with sufficient assistance and bespoke design and incentive structures, this is The Way. It sure seems way better than ‘sit and listen to a lecture.’ I am especially excited for Alpha School's version of this, with its bespoke designs and high level of both expectations and continuous human support. Alas, most people are getting a much worse version, that is much worse than what you could easily improvise at home. I’m less concerned with ‘EdTech provider [...] --- First published: July 8th, 2026 Source: https://www.lesswrong.com/posts/XA8fMCnwuc45uZYHX/childhood-and-education-20-phones-and-screens --- Narrated by TYPE III AUDIO.

  2. hace 8 h

    “How slower does takeoff go with 10× less compute?” by bhalstead

    About 6x slower in the median case, with an 80% confidence interval of 3.5x to 8x. Setup Define the "R&D compute" (in, say, H100-equivalents) of an AGI company at a given time to be the total compute in use across the following categories: Compute used to run experiments, which are used to search for software improvements,Compute used to run automated researcher agents,Compute used in final training runs for frontier models. With less R&D compute, the company would need to make cuts across these categories. For now, we'll assume they maintain the same allocation across the three areas, that is, we'll assume they achieve the reduction in the total via a reduction to each category. The capability level of your best AI model is represented by its effective training compute, . is a stock, with units of "2025-FLOP". evolves according to where is training system performance (a flow, units: FLOP/yr) and is software efficiency (units: 2025-FLOP per FLOP). We will call this quantity effective training system performance. Training system performance and software efficiency tend to grow exponentially over time, so the "rate of AI progress" will be operationalized as the relative growth rate of (i.e. the [...] --- Outline: (00:30) Setup (02:57) Finding a mathematical expression for the slowdown factor (06:25) Estimating using the AI Futures Model The original text contained 1 footnote which was omitted from this narration. --- First published: July 8th, 2026 Source: https://www.lesswrong.com/posts/7jcPg79p3kD5ir3CL/how-slower-does-takeoff-go-with-10-less-compute --- 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 min

  3. hace 9 h

    “Find funding, fast” by Austin Chen

    Some AI safety funders can take months to decide; others confirm in days. I’ve been on both sides of the grant application and know how crucial an early “yes” can be; “funding projects fast” has always been a core tenet of Manifund. Four new opportunities have popped up around the extended Manifund universe, several with a short-fuse (that is, due in the next few days): 1. grantmaking.ai Size: $5-50 thousand dollars grantsScope: AI x-riskApps due: July 13Apply at: grantmaking.ai This is a new 1 million dollars grant round! Organized by Matt Brooks, Anton Makiievskyi and Melissa Samworth, their team is earnest and excited to improve the entire funding landscape: We want to build the most comprehensive public repository of donation opportunities in existential AI safety space with essential information like up-to-date funding needs, theory of impact, references, endorsements, team track record, and more… Currently, most AI safety funding is distributed privately by a few large funds - which are great and remain the most effective way to support AI safety; nevertheless we believe a more public, agile approach will benefit the ecosystem, and be particularly useful for smaller AI safety grants that are [...] --- Outline: (00:32) 1. grantmaking.ai (02:14) 2. Leo's alignment microgrants (03:30) 3. Frame Fellowship (04:43) 4. Surplus --- First published: July 8th, 2026 Source: https://www.lesswrong.com/posts/eberRKyDdZH9T5JMf/find-funding-fast --- 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

  4. hace 12 h

    “Modular Pretraining Enables Access Control” by E.Roland, cloud

    Full author list: Ethan Roland*, Murat Cubuktepe*, Erick Martinez*, Stijn Servaes, Keenan Pepper, Mike Vaiana, Diogo Schwerz de Lucena, Judd Rosenblatt, Addie Foote, Cem Anil, Alex Cloud; *Equal contribution tldr: Frontier AI models have knowledge that could be misused for nefarious purposes. To address this risk, we introduce Gradient Routed Auxiliary Modules (GRAM), a method for isolating dangerous knowledge to specific modules within a language model. These modules can be switched on or off to control what the model knows, making it possible to restrict or extend access to the most sensitive model capabilities based on user need and trust. In our experiments, we find evidence that a single model trained in this way can approximate multiple models, each trained with a different category of dangerous data filtered out, and this ability holds for models ranging from 50M to 5 billion parameters. This research is preliminary and has not been applied to production models at Anthropic. 📄 Paper, 💻 Code, 🖥️ Site This work was done at AE Studio, in collaboration with Anthropic. This is a cross-post from the Anthropic Alignment science blog. Introduction One of the major threats from frontier AI models is the misuse of legitimately helpful [...] --- Outline: (01:30) Introduction (05:30) The Method (07:48) Results (07:51) GRAM Matches Data Filtering (09:54) Access Control on Real Dual Use Data (12:23) Modularization Works Across Scales (14:05) Advantages of GRAM (14:15) Composability (15:30) Isolation Under Partial Labeling (17:18) Discussion (19:44) Acknowledgements --- First published: July 8th, 2026 Source: https://www.lesswrong.com/posts/43vKjWuH4goLwrFHA/modular-pretraining-enables-access-control --- 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.

    21 min

  5. hace 13 h

    “Subliminal Learning Happens at Every Rank, Given the Right Learning Rate and Enough Data” by Lawrence Feng

    Subliminal learning is the phenomenon where a language model picks up a behavioral trait—such as fondness for cats—by training on data from a trait-carrying teacher that looks entirely unrelated to the trait, such as bare sequences of numbers [1]. A wave of recent work has probed when this happens and what mechanism drives it [2][3][4][5][6][7], and part of that discourse concerns the conditions and dynamics under which subliminal learning occurs. Nief et al. [6] report that subliminal learning follows an inverted-U in LoRA rank — neither low-rank adapters nor full fine-tuning (FFT) acquire the trait — and Blank et al. [4] also find that FFT does not. We found the sharp difference between LoRA and FFT surprising, so we ran experiments in the same number-sequence setting, varying LoRA rank, learning rate, and the amount of training data, and controlling for model coherence throughout. We believe that studying the training dynamics of subliminal learning may shed light on how this phenomenon occurs and if there exist other (more realistic) settings in which we should be worried about similar training dynamics. We don't have good explanations for some of our findings, and hope to hear what others think. Our main findings [...] --- First published: July 8th, 2026 Source: https://www.lesswrong.com/posts/uWQMtQyMJ5vEGqr7r/subliminal-learning-happens-at-every-rank-given-the-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.

  6. hace 15 h

    “Why study proto-training gaming as an adversarial alignment failure mode?” by Puria, Edward James Young, Cam

    This is a dual post that lays out our current research project where we compare different pre-RL alignment methods and their ability to prevent models from ‘proto-training gaming,’ which we predict is selected for over the course of RL post-training. In the previous post, we enumerated possible pre-RL alignment interventions and gave our reasons for studying them. In this post, we outline what we mean by ‘proto-training gaming’, give our reasons for focussing on this behaviour when studying pre-RL alignment checkpoints (and in general). Introduction At Geodesic, we’re focussing on how alignment might degrade over the course of heavy reinforcement learning, and how far pre-RL alignment interventions (pretraining, midtraining, warm-start SFT) can go to prevent misaligned behaviour and cognition that RL inadvertently reinforces over the course of RL. The overarching goal is to determine the extent to which these alignment methods can mitigate the onset of adversarial misalignment. Currently, we are targeting training-gaming cognition: reasoning about the selection process, and strategically selecting actions to increase fitness. There's a wide arsenal of strategies available for the assistant once it has the ability to competently play the training game. It can undermine elicitation of aligned actions that we can reinforce; it [...] The original text contained 1 footnote which was omitted from this narration. --- First published: July 8th, 2026 Source: https://www.lesswrong.com/posts/5KHLQkW8M87FzbM5a/why-study-proto-training-gaming-as-an-adversarial-alignment --- Narrated by TYPE III AUDIO.

  7. hace 15 h

    “Why study alignment interventions on pre-RL checkpoints?” by Edward James Young, Puria, Cam

    This is a dual post that lays out our current research project where we compare pre-RL-training methods on their ability to prevent models from ‘proto-training gaming,’ which we predict is selected for over the course of production RL post-training. In this post, we outline what we mean by pre-RL ‘alignment checkpoints’, give our reasons for focussing on these stages of training, and suggest ways that our current viewpoint might be wrong. In the next post, we define proto-training gaming and argue that i) it is a necessary precursor to adversarial misalignment, ii) it is ecologically selected for in, and competently performed by, current models, iii) the pre-RL alignment checkpoint can have an outsized mitigation of this selection. What do we mean by the pre-RL alignment checkpoint? By the pre-RL alignment checkpoint of a model, we mean the alignment-relevant properties of a model as conferred by all the training that happens prior to on-policy RL[1]. This encompasses the following stages, with corresponding levers that safety teams can intervene on: Pretraining – training a model from scratch on a general text corpus. Our team has previously studied data-filtering to remove dangerous capabilities, and more recently adding positive synthetic documents to [...] The original text contained 6 footnotes which were omitted from this narration. --- First published: July 8th, 2026 Source: https://www.lesswrong.com/posts/nhjkHsppEk98xxmPe/why-study-alignment-interventions-on-pre-rl-checkpoints --- Narrated by TYPE III AUDIO.

  8. hace 17 h

    “Notes on technical alignment via human-like social drives” by Steven Byrnes

    1. Frontmatter 1.1 Backstory for this post As my regular readers know (see Intro to Brain-Like-AGI Safety), I’m working on the technical alignment problem for a hypothetical future “brain-like AGI”, with a particular focus on how human social and moral drives work. After all, if it's possible for humans to do stuff that ultimately leads to a good future, then it's probably also possible for sufficiently human-like AGIs to do stuff that ultimately leads to a good future. Or if it's not possible for humans to do stuff that ultimately leads to a good future, then we’re screwed no matter what. But assuming it's possible, the “sufficiently human-like AGIs” would certainly need to have good prosocial motivations. This is an unsolved problem, and very much not the default (see We need a field of Reward Function Design), but there's probably some solution that's inspired by how humans (sometimes) wind up with good prosocial motivations. I’ve been working on this problem for years, but most of that work has involved laying foundations (e.g. trying to understand how human social drives work). Whereas in the past four months, I’ve been thinking very directly about how to apply those ideas to AGI. [...] The original text contained 13 footnotes which were omitted from this narration. --- First published: July 8th, 2026 Source: https://www.lesswrong.com/posts/rKdS7i4StaMmFzYRo/notes-on-technical-alignment-via-human-like-social-drives --- 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.

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Audio narrations of LessWrong posts.

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    LessWrong
  • Años de actividad
    2023 - 2026
  • Episodios
    250
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