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    Technology

    LessWrong (Curated & Popular)

    Audio narrations of LessWrong posts. Includes all curated posts and all posts with 125+ karma.

    If you’d like more, subscribe to the “Lesswrong (30+ karma)” feed.

    Advertise

    Copyright: © 2023 LessWrong Curated Podcast

    • Apple Podcasts
    • Google Play
    • Spotify
    Latest Episodes:
    “Understanding Shapley Values with Venn Diagrams” by Carson L Dec 12, 2024
    Show notes

    Someone I know, Carson Loughridge, wrote this very nice post explaining the core intuition around Shapley values (which play an important role in impact assessment and cooperative games) using Venn diagrams, and I think it's great. It might be the most intuitive explainer I've come across so far.
    Incidentally, the post also won an honorable mention in 3blue1brown's Summer of Mathematical Exposition. I'm really proud of having given input on the post.
    I've included the full post (with permission), as follows:
    Shapley values are an extremely popular tool in both economics and explainable AI.
    In this article, we use the concept of “synergy” to build intuition for why Shapley values are fair. There are four unique properties to Shapley values, and all of them can be justified visually. Let's dive in!
    A figure from Bloch et al., 2021 using the Python package SHAP The Game
    On a sunny summer [...]
    ---
    Outline:
    (01:07) The Game
    (04:41) The Formalities
    (06:17) Concluding Notes
    The original text contained 2 images which were described by AI.
    ---
    First published:
    December 6th, 2024
    Source:
    https://www.lesswrong.com/posts/WxCtxaAznn8waRWPG/understanding-shapley-values-with-venn-diagrams
    ---
    Narrated by TYPE III AUDIO.
    ---

    Images from the article:
    undefinedundefinedundefinedundefinedApple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.
    “LessWrong audio: help us choose the new voice” by PeterH Dec 12, 2024
    Show notes

    We make AI narrations of LessWrong posts available via our audio player and podcast feeds.
    We’re thinking about changing our narrator's voice.
    There are three new voices on the shortlist. They’re all similarly good in terms of comprehension, emphasis, error rate, etc. They just sound different—like people do.
    We think they all sound similarly agreeable. But, thousands of listening hours are at stake, so we thought it’d be worth giving listeners an opportunity to vote—just in case there's a strong collective preference.
    Listen and vote
    Please listen here:
    https://files.type3.audio/lesswrong-poll/
    And vote here:
    https://forms.gle/JwuaC2ttd5em1h6h8
    It’ll take 1-10 minutes, depending on how much of the sample you decide to listen to.
    Don’t overthink it—we’d just like to know if there's a voice that you’d particularly love (or hate) to listen to.
    We'll collect votes until Monday December 16th. Thanks!
    ---
    Outline:
    (00:58) Listen and vote
    (01:30) Other feedback?

    The original text contained 2 footnotes which were omitted from this narration.


    ---
    First published:
    December 11th, 2024
    Source:
    https://www.lesswrong.com/posts/wp4emMpicxNEPDb6P/lesswrong-audio-help-us-choose-the-new-voice
    ---
    Narrated by TYPE III AUDIO.

    “Understanding Shapley Values with Venn Diagrams” by agucova Dec 11, 2024
    Show notes

    This is a link post. Someone I know wrote this very nice post explaining the core intuition around Shapley values (which play an important role in impact assessment) using Venn diagrams, and I think it's great. It might be the most intuitive explainer I've come across so far.
    Incidentally, the post also won an honorable mention in 3blue1brown's Summer of Mathematical Exposition.
    ---
    First published:
    December 6th, 2024
    Source:
    https://www.lesswrong.com/posts/6dixnRRYSLTqCdJzG/understanding-shapley-values-with-venn-diagrams
    ---
    Narrated by TYPE III AUDIO.

    “o1: A Technical Primer” by Jesse Hoogland Dec 11, 2024
    Show notes

    TL;DR: In September 2024, OpenAI released o1, its first "reasoning model". This model exhibits remarkable test-time scaling laws, which complete a missing piece of the Bitter Lesson and open up a new axis for scaling compute. Following Rush and Ritter (2024) and Brown (2024a, 2024b), I explore four hypotheses for how o1 works and discuss some implications for future scaling and recursive self-improvement.
    The Bitter Lesson(s)
    The Bitter Lesson is that "general methods that leverage computation are ultimately the most effective, and by a large margin." After a decade of scaling pretraining, it's easy to forget this lesson is not just about learning; it's also about search.
    OpenAI didn't forget. Their new "reasoning model" o1 has figured out how to scale search during inference time. This does not use explicit search algorithms. Instead, o1 is trained via RL to get better at implicit search via chain of thought [...]
    ---
    Outline:
    (00:40) The Bitter Lesson(s)
    (01:56) What we know about o1
    (02:09) What OpenAI has told us
    (03:26) What OpenAI has showed us
    (04:29) Proto-o1: Chain of Thought
    (04:41) In-Context Learning
    (05:14) Thinking Step-by-Step
    (06:02) Majority Vote
    (06:47) o1: Four Hypotheses
    (08:57) 1. Filter: Guess + Check
    (09:50) 2. Evaluation: Process Rewards
    (11:29) 3. Guidance: Search / AlphaZero
    (13:00) 4. Combination: Learning to Correct
    (14:23) Post-o1: (Recursive) Self-Improvement
    (16:43) Outlook
    ---
    First published:
    December 9th, 2024
    Source:
    https://www.lesswrong.com/posts/byNYzsfFmb2TpYFPW/o1-a-technical-primer
    ---
    Narrated by TYPE III AUDIO.
    ---

    Images from the article:
    undefinedundefinedApple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.
    “Gradient Routing: Masking Gradients to Localize Computation in Neural Networks” by cloud, Jacob G-W, Evzen, Joseph Miller, TurnTrout Dec 09, 2024
    Show notes

    We present gradient routing, a way of controlling where learning happens in neural networks. Gradient routing applies masks to limit the flow of gradients during backpropagation. By supplying different masks for different data points, the user can induce specialized subcomponents within a model. We think gradient routing has the potential to train safer AI systems, for example, by making them more transparent, or by enabling the removal or monitoring of sensitive capabilities.
    In this post, we:

    • Show how to implement gradient routing.
    • Briefly state the main results from our paper, on...
      • Controlling the latent space learned by an MNIST autoencoder so that different subspaces specialize to different digits;
      • Localizing computation in language models: (a) inducing axis-aligned features and (b) demonstrating that information can be localized then removed by ablation, even when data is imperfectly labeled; and
      • Scaling oversight to efficiently train a reinforcement learning policy even with [...]
    ---
    Outline:
    (01:48) Gradient routing
    (03:02) MNIST latent space splitting
    (04:31) Localizing capabilities in language models
    (04:36) Steering scalar
    (05:46) Robust unlearning
    (09:06) Unlearning virology
    (10:38) Scalable oversight via localization
    (15:28) Key takeaways
    (15:32) Absorption
    (17:04) Localization avoids Goodharting
    (18:02) Key limitations
    (19:47) Alignment implications
    (19:51) Robust removal of harmful capabilities
    (20:19) Scalable oversight
    (21:36) Specialized AI
    (22:52) Conclusion
    The original text contained 1 footnote which was omitted from this narration.
    ---
    First published:
    December 6th, 2024
    Source:
    https://www.lesswrong.com/posts/nLRKKCTtwQgvozLTN/gradient-routing-masking-gradients-to-localize-computation
    ---
    Narrated by TYPE III AUDIO.
    ---
    Images from the article:
    undefinedundefined
    “Frontier Models are Capable of In-context Scheming” by Marius Hobbhahn, AlexMeinke, Bronson Schoen Dec 06, 2024
    Show notes

    This is a brief summary of what we believe to be the most important takeaways from our new paper and from our findings shown in the o1 system card. We also specifically clarify what we think we did NOT show.
    Paper: https://www.apolloresearch.ai/research/scheming-reasoning-evaluations
    Twitter about paper: https://x.com/apolloaisafety/status/1864735819207995716
    Twitter about o1 system card: https://x.com/apolloaisafety/status/1864737158226928124
    What we think the most important findings are
    Models are now capable enough to do in-context scheming reasoning
    We say an AI system is “scheming” if it covertly pursues misaligned goals, hiding its true capabilities and
    objectives. We think that in order to scheme, models likely need to be goal-directed, situationally aware, and capable enough to reason about scheming as a strategy. In principle, models might acquire situational awareness and stable long-term goals during training, and then scheme in pursuit of those goals. We only study the simpler case of “in-context scheming,” where [...]
    ---
    Outline:
    (00:28) What we think the most important findings are
    (00:33) Models are now capable enough to do in-context scheming reasoning
    (03:53) The scheming actions can sometimes be quite subtle
    (05:05) Models sometimes double down on their deception
    (06:35) Models sometimes scheme without any goal nudging
    (07:34) Sonnet-3.5 and opus-3 can sandbag in pursuit of helpfulness
    (08:22) Sonnet-3.5 can sandbag without relying on CoT reasoning
    (09:28) What we are not claiming:
    (10:33) Takeaways
    (10:36) Chain-of-thought monitoring
    (11:42) Chain-of-thought access
    (13:27) Inability safety cases now have to be more specific
    The original text contained 7 images which were described by AI.
    ---
    First published:
    December 5th, 2024
    Source:
    https://www.lesswrong.com/posts/8gy7c8GAPkuu6wTiX/frontier-models-are-capable-of-in-context-scheming
    ---
    Narrated by TYPE III AUDIO.
    ---

    Images from the article:
    undefinedundefined
    “(The) Lightcone is nothing without its people: LW + Lighthaven’s first big fundraiser” by habryka Nov 30, 2024
    Show notes

    TLDR: LessWrong + Lighthaven need about $3M for the next 12 months. Donate here, or send me an email, DM or signal message (+1 510 944 3235) if you want to support what we do. Donations are tax-deductible in the US. Reach out for other countries, we can likely figure something out. We have big plans for the next year, and due to a shifting funding landscape we need support from a broader community more than in any previous year.
    I've been running LessWrong/Lightcone Infrastructure for the last 7 years. During that time we have grown into the primary infrastructure provider for the rationality and AI safety communities. "Infrastructure" is a big fuzzy word, but in our case, it concretely means:

    • We build and run LessWrong.com and the AI Alignment Forum.[1]
    • We built and run Lighthaven (lighthaven.space), a ~30,000 sq. ft. campus in downtown Berkeley where we [...]
    ---
    Outline:
    (03:52) LessWrong
    (06:36) Does LessWrong influence important decisions?
    (09:37) Does LessWrong make its readers/writers more sane?
    (11:37) LessWrong and intellectual progress
    (19:08) Lighthaven
    (22:04) The economics of Lighthaven
    (24:26) How does Lighthaven improve the world?
    (28:41) The relationship between Lighthaven and LessWrong
    (30:36) Lightcone and the funding ecosystem
    (35:17) Our work on funding infrastructure
    (37:57) If its worth doing its worth doing with made-up statistics
    (38:44) The OP GCR capacity building team survey
    (42:09) Lightcone/LessWrong cannot be funded by just running ads
    (43:55) Comparing LessWrong to other websites and apps
    (45:00) Lighthaven event surplus
    (47:13) The future of (the) Lightcone
    (48:02) Lightcone culture and principles
    (50:04) Things I wish I had time and funding for
    (59:31) What do you get from donating to Lightcone?
    (01:02:03) Tying everything together
    The original text contained 22 footnotes which were omitted from this narration.
    The original text contained 7 images which were described by AI.
    ---
    First published:
    November 30th, 2024
    Source:
    https://www.lesswrong.com/posts/5n2ZQcbc7r4R8mvqc/the-lightcone-is-nothing-without-its-people-lw-lighthaven-s-5
    ---
    Narrated by TYPE III AUDIO.
    ---
    Images from the article:
    undefinedundefined
    “Repeal the Jones Act of 1920” by Zvi Nov 29, 2024
    Show notes

    Balsa Policy Institute chose as its first mission to lay groundwork for the potential repeal, or partial repeal, of section 27 of the Jones Act of 1920. I believe that this is an important cause both for its practical and symbolic impacts.
    The Jones Act is the ultimate embodiment of our failures as a nation.
    After 100 years, we do almost no trade between our ports via the oceans, and we build almost no oceangoing ships.
    Everything the Jones Act supposedly set out to protect, it has destroyed.
    Table of Contents

    1. What is the Jones Act?
    2. Why Work to Repeal the Jones Act?
    3. Why Was the Jones Act Introduced?
    4. What is the Effect of the Jones Act?
    5. What Else Happens When We Ship More Goods Between Ports?
    6. Emergency Case Study: Salt Shipment to NJ in [...]
    ---
    Outline:
    (00:38) What is the Jones Act?
    (01:33) Why Work to Repeal the Jones Act?
    (02:48) Why Was the Jones Act Introduced?
    (03:19) What is the Effect of the Jones Act?
    (06:52) What Else Happens When We Ship More Goods Between Ports?
    (07:14) Emergency Case Study: Salt Shipment to NJ in the Winter of 2013-2014
    (12:04) Why no Emergency Exceptions?
    (15:02) What Are Some Specific Non-Emergency Impacts?
    (18:57) What Are Some Specific Impacts on Regions?
    (22:36) What About the Study Claiming Big Benefits?
    (24:46) What About the Need to ‘Protect’ American Shipbuilding?
    (28:31) The Opposing Arguments Are Disingenuous and Terrible
    (34:07) What Alternatives to Repeal Do We Have?
    (35:33) What Might Be a Decent Instinctive Counterfactual?
    (41:50) What About Our Other Protectionist and Cabotage Laws?
    (43:00) What About Potential Marine Highways, or Short Sea Shipping?
    (43:48) What Happened to All Our Offshore Wind?
    (47:06) What Estimates Are There of Overall Cost?
    (49:52) What Are the Costs of Being American Flagged?
    (50:28) What Are the Costs of Being American Made?
    (51:49) What are the Consequences of Being American Crewed?
    (53:11) What Would Happen in a Real War?
    (56:07) Cruise Ship Sanity Partially Restored
    (56:46) The Jones Act Enforcer
    (58:08) Who Benefits?
    (58:57) Others Make the Case
    (01:00:55) An Argument That We Were Always Uncompetitive
    (01:02:45) What About John Arnold's Case That the Jones Act Can’t Be Killed?
    (01:09:34) What About the Foreign Dredge Act of 1906?
    (01:10:24) Fun Stories
    ---
    First published:
    November 27th, 2024
    Source:
    https://www.lesswrong.com/posts/dnH2hauqRbu3GspA2/repeal-the-jones-act-of-1920
    ---
    Narrated by TYPE III AUDIO.
    ---
    Images from the article:
    undefined
    “China Hawks are Manufacturing an AI Arms Race” by garrison Nov 28, 2024
    Show notes

    This is the full text of a post from "The Obsolete Newsletter," a Substack that I write about the intersection of capitalism, geopolitics, and artificial intelligence. I’m a freelance journalist and the author of a forthcoming book called Obsolete: Power, Profit, and the Race for Machine Superintelligence. Consider subscribing to stay up to date with my work.
    An influential congressional commission is calling for a militarized race to build superintelligent AI based on threadbare evidence
    The US-China AI rivalry is entering a dangerous new phase.
    Earlier today, the US-China Economic and Security Review Commission (USCC) released its annual report, with the following as its top recommendation:
    Congress establish and fund a Manhattan Project-like program dedicated to racing to and acquiring an Artificial General Intelligence (AGI) capability. AGI is generally defined as systems that are as good as or better than human capabilities across all cognitive domains and [...]
    ---
    Outline:
    (00:28) An influential congressional commission is calling for a militarized race to build superintelligent AI based on threadbare evidence
    (03:09) What China has said about AI
    (06:14) Revealing technical errors
    (08:29) Conclusion
    The original text contained 1 image which was described by AI.
    ---
    First published:
    November 20th, 2024
    Source:
    https://www.lesswrong.com/posts/KPBPc7RayDPxqxdqY/china-hawks-are-manufacturing-an-ai-arms-race
    ---
    Narrated by TYPE III AUDIO.
    ---

    Images from the article:
    undefinedApple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.
    “Information vs Assurance” by johnswentworth Nov 27, 2024
    Show notes

    In contract law, there's this thing called a “representation”. Example: as part of a contract to sell my house, I might “represent that” the house contains no asbestos. How is this different from me just, y’know, telling someone that the house contains no asbestos? Well, if it later turns out that the house does contain asbestos, I’ll be liable for any damages caused by the asbestos (like e.g. the cost of removing it).
    In other words: a contractual representation is a factual claim along with insurance against that claim being false.
    I claim[1] that people often interpret everyday factual claims and predictions in a way similar to contractual representations. Because “representation” is egregiously confusing jargon, I’m going to call this phenomenon “assurance”.
    Prototypical example: I tell my friend that I plan to go to a party around 9 pm, and I’m willing to give them a ride. My friend [...]
    The original text contained 1 footnote which was omitted from this narration.
    ---
    First published:
    October 20th, 2024
    Source:
    https://www.lesswrong.com/posts/p9rQJMRq4qtB9acds/information-vs-assurance
    ---
    Narrated by TYPE III AUDIO.

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