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LessWrong (Curated & Popular)

LessWrong (Curated & Popular)

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

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© 2025 LessWrong (Curated & Popular) Philosophy Social Sciences
Episodes
  • “Little Echo” by Zvi

    “Little Echo” by Zvi
    Dec 9 2025
    I believe that we will win.

    An echo of an old ad for the 2014 US men's World Cup team. It did not win.

    I was in Berkeley for the 2025 Secular Solstice. We gather to sing and to reflect.

    The night's theme was the opposite: ‘I don’t think we’re going to make it.’

    As in: Sufficiently advanced AI is coming. We don’t know exactly when, or what form it will take, but it is probably coming. When it does, we, humanity, probably won’t make it. It's a live question. Could easily go either way. We are not resigned to it. There's so much to be done that can tilt the odds. But we’re not the favorite.

    Raymond Arnold, who ran the event, believes that. I believe that.

    Yet in the middle of the event, the echo was there. Defiant.

    I believe that we will win.

    There is a recording of the event. I highly encourage you to set aside three hours at some point in December, to listen, and to participate and sing along. Be earnest.

    If you don’t believe it, I encourage this all the more. If you [...]

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    First published:
    December 8th, 2025

    Source:
    https://www.lesswrong.com/posts/YPLmHhNtjJ6ybFHXT/little-echo

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    Narrated by TYPE III AUDIO.
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    4 mins
  • “A Pragmatic Vision for Interpretability” by Neel Nanda

    “A Pragmatic Vision for Interpretability” by Neel Nanda
    Dec 8 2025
    Executive Summary

    • The Google DeepMind mechanistic interpretability team has made a strategic pivot over the past year, from ambitious reverse-engineering to a focus on pragmatic interpretability:
      • Trying to directly solve problems on the critical path to AGI going well[[1]]
      • Carefully choosing problems according to our comparative advantage
      • Measuring progress with empirical feedback on proxy tasks
    • We believe that, on the margin, more researchers who share our goals should take a pragmatic approach to interpretability, both in industry and academia, and we call on people to join us
      • Our proposed scope is broad and includes much non-mech interp work, but we see this as the natural approach for mech interp researchers to have impact
      • Specifically, we’ve found that the skills, tools and tastes of mech interp researchers transfer well to important and neglected problems outside “classic” mech interp
      • See our companion piece for more on which research areas and theories of change we think are promising
    • Why pivot now? We think that times have changed.
      • Models are far more capable, bringing new questions within empirical reach
      • We have been [...]
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    Outline:

    (00:10) Executive Summary

    (03:00) Introduction

    (03:44) Motivating Example: Steering Against Evaluation Awareness

    (06:21) Our Core Process

    (08:20) Which Beliefs Are Load-Bearing?

    (10:25) Is This Really Mech Interp?

    (11:27) Our Comparative Advantage

    (14:57) Why Pivot?

    (15:20) Whats Changed In AI?

    (16:08) Reflections On The Fields Progress

    (18:18) Task Focused: The Importance Of Proxy Tasks

    (18:52) Case Study: Sparse Autoencoders

    (21:35) Ensure They Are Good Proxies

    (23:11) Proxy Tasks Can Be About Understanding

    (24:49) Types Of Projects: What Drives Research Decisions

    (25:18) Focused Projects

    (28:31) Exploratory Projects

    (28:35) Curiosity Is A Double-Edged Sword

    (30:56) Starting In A Robustly Useful Setting

    (34:45) Time-Boxing

    (36:27) Worked Examples

    (39:15) Blending The Two: Tentative Proxy Tasks

    (41:23) What's Your Contribution?

    (43:08) Jack Lindsey's Approach

    (45:44) Method Minimalism

    (46:12) Case Study: Shutdown Resistance

    (48:28) Try The Easy Methods First

    (50:02) When Should We Develop New Methods?

    (51:36) Call To Action

    (53:04) Acknowledgments

    (54:02) Appendix: Common Objections

    (54:08) Aren't You Optimizing For Quick Wins Over Breakthroughs?

    (56:34) What If AGI Is Fundamentally Different?

    (57:30) I Care About Scientific Beauty and Making AGI Go Well

    (58:09) Is This Just Applied Interpretability?

    (58:44) Are You Saying This Because You Need To Prove Yourself Useful To Google?

    (59:10) Does This Really Apply To People Outside AGI Companies?

    (59:40) Aren't You Just Giving Up?

    (01:00:04) Is Ambitious Reverse-engineering Actually Overcrowded?

    (01:00:48) Appendix: Defining Mechanistic Interpretability

    (01:01:44) Moving Toward Mechanistic OR Interpretability

    The original text contained 47 footnotes which were omitted from this narration.

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    First published:
    December 1st, 2025

    Source:
    https://www.lesswrong.com/posts/StENzDcD3kpfGJssR/a-pragmatic-vision-for-inter
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    1 hr and 4 mins
  • “AI in 2025: gestalt” by technicalities

    “AI in 2025: gestalt” by technicalities
    Dec 8 2025
    This is the editorial for this year's "Shallow Review of AI Safety". (It got long enough to stand alone.)

    Epistemic status: subjective impressions plus one new graph plus 300 links.

    Huge thanks to Jaeho Lee, Jaime Sevilla, and Lexin Zhou for running lots of tests pro bono and so greatly improving the main analysis.

    tl;dr

    • Informed people disagree about the prospects for LLM AGI – or even just what exactly was achieved this year. But they at least agree that we’re 2-20 years off (if you allow for other paradigms arising). In this piece I stick to arguments rather than reporting who thinks what.
    • My view: compared to last year, AI is much more impressive but not much more useful. They improved on many things they were explicitly optimised for (coding, vision, OCR, benchmarks), and did not hugely improve on everything else. Progress is thus (still!) consistent with current frontier training bringing more things in-distribution rather than generalising very far.
    • Pretraining (GPT-4.5, Grok 4, but also counterfactual large runs which weren’t done) disappointed people this year. It's probably not because it wouldn’t work; it was just ~30 times more efficient to do post-training instead, on the margin. This should [...]
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    Outline:

    (00:36) tl;dr

    (03:51) Capabilities in 2025

    (04:02) Arguments against 2025 capabilities growth being above-trend

    (08:48) Arguments for 2025 capabilities growth being above-trend

    (16:19) Evals crawling towards ecological validity

    (19:28) Safety in 2025

    (22:39) The looming end of evals

    (24:35) Prosaic misalignment

    (26:56) What is the plan?

    (29:30) Things which might fundamentally change the nature of LLMs

    (31:03) Emergent misalignment and model personas

    (32:32) Monitorability

    (34:15) New people

    (34:49) Overall

    (35:17) Discourse in 2025

    The original text contained 9 footnotes which were omitted from this narration.

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    First published:
    December 7th, 2025

    Source:
    https://www.lesswrong.com/posts/Q9ewXs8pQSAX5vL7H/ai-in-2025-gestalt

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    Narrated by TYPE III AUDIO.

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    42 mins
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