LessWrong (Curated & Popular)

View Top 200 Chart

Back to Rankings Page

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.

Latest Episodes:

Two brass or gold-colored threaded metal rods with mounting holes. — **“Frontier AI Models Still Fail at Basic Physical Tasks: A Manufacturing Case Study” by Adam Karvonen** Apr 16, 2025
Show notes

Dario Amodei, CEO of Anthropic, recently worried about a world where only 30% of jobs become automated, leading to class tensions between the automated and non-automated. Instead, he predicts that nearly all jobs will be automated simultaneously, putting everyone "in the same boat." However, based on my experience spanning AI research (including first author papers at COLM / NeurIPS and attending MATS under Neel Nanda), robotics, and hands-on manufacturing (including machining prototype rocket engine parts for Blue Origin and Ursa Major), I see a different near-term future.
Since the GPT-4 release, I've evaluated frontier models on a basic manufacturing task, which tests both visual perception and physical reasoning. While Gemini 2.5 Pro recently showed progress on the visual front, all models tested continue to fail significantly on physical reasoning. They still perform terribly overall. Because of this, I think that there will be an interim period where a significant [...]
---
**Outline:**
(01:28) The Evaluation
(02:29) Visual Errors
(04:03) Physical Reasoning Errors
(06:09) Why do LLM's struggle with physical tasks?
(07:37) Improving on physical tasks may be difficult
(10:14) Potential Implications of Uneven Automation
(11:48) Conclusion
(12:24) Appendix
(12:44) Visual Errors
(14:36) Physical Reasoning Errors
---
First published:
April 14th, 2025
Source:
https://www.lesswrong.com/posts/r3NeiHAEWyToers4F/frontier-ai-models-still-fail-at-basic-physical-tasks-a
---
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.*

Latent firing frequency histograms for Gated, JumpReLU and TopK SAEs. Unlike Gated SAEs, which use a L1 penalty that penalizes large latent activations, JumpReLU (middle) and TopK (bottom) SAEs exhibit high-frequency latents: latents that fire on 10% or more of tokens (i.e. that lie to the right of the dotted vertical line). — “Negative Results for SAEs On Downstream Tasks and Deprioritising SAE Research (GDM Mech Interp Team Progress Update #2)” by Neel Nanda, lewis smith, Senthooran Rajamanoharan, Arthur Conmy, Callum McDougall, Tom Lieberum, János Kramár, Rohin Shah Apr 12, 2025
Show notes

Audio note: this article contains 31 uses of latex notation, so the narration may be difficult to follow. There's a link to the original text in the episode description.
Lewis Smith*, Sen Rajamanoharan*, Arthur Conmy, Callum McDougall, Janos Kramar, Tom Lieberum, Rohin Shah, Neel Nanda
* = equal contribution
The following piece is a list of snippets about research from the GDM mechanistic interpretability team, which we didn’t consider a good fit for turning into a paper, but which we thought the community might benefit from seeing in this less formal form. These are largely things that we found in the process of a project investigating whether sparse autoencoders were useful for downstream tasks, notably out-of-distribution probing.
TL;DR

To validate whether SAEs were a worthwhile technique, we explored whether they were useful on the downstream task of OOD generalisation when detecting harmful intent in user prompts
[...]
---
**Outline:**
(01:08) TL;DR
(02:38) Introduction
(02:41) Motivation
(06:09) Our Task
(08:35) Conclusions and Strategic Updates
(13:59) Comparing different ways to train Chat SAEs
(18:30) Using SAEs for OOD Probing
(20:21) Technical Setup
(20:24) Datasets
(24:16) Probing
(26:48) Results
(30:36) Related Work and Discussion
(34:01) Is it surprising that SAEs didn't work?
(39:54) Dataset debugging with SAEs
(42:02) Autointerp and high frequency latents
(44:16) Removing High Frequency Latents from JumpReLU SAEs
(45:04) Method
(45:07) Motivation
(47:29) Modifying the sparsity penalty
(48:48) How we evaluated interpretability
(50:36) Results
(51:18) Reconstruction loss at fixed sparsity
(52:10) Frequency histograms
(52:52) Latent interpretability
(54:23) Conclusions
(56:43) Appendix
The original text contained 7 footnotes which were omitted from this narration.
---
First published:
March 26th, 2025
Source:
https://www.lesswrong.com/posts/4uXCAJNuPKtKBsi28/sae-progress-update-2-draft
---
Narrated by TYPE III AUDIO.
---
**Images from the article:**

Reconstruction loss vs L0 for the various SAE architectures and loss functions used in our experiment. The quadratic-frequency penalty (QF loss) has slightly worse reconstruction loss at any given sparsity than standard JumpReLU SAEs (L0 loss), but still compare favourably versus Gated and TopK SAEs. — “Negative Results for SAEs On Downstream Tasks and Deprioritising SAE Research (GDM Mech Interp Team Progress Update #2)” by Neel Nanda, lewis smith, Senthooran Rajamanoharan, Arthur Conmy, Callum McDougall, Tom Lieberum, János Kramár, Rohin Shah Apr 12, 2025
Show notes

Audio note: this article contains 31 uses of latex notation, so the narration may be difficult to follow. There's a link to the original text in the episode description.
Lewis Smith*, Sen Rajamanoharan*, Arthur Conmy, Callum McDougall, Janos Kramar, Tom Lieberum, Rohin Shah, Neel Nanda
* = equal contribution
The following piece is a list of snippets about research from the GDM mechanistic interpretability team, which we didn’t consider a good fit for turning into a paper, but which we thought the community might benefit from seeing in this less formal form. These are largely things that we found in the process of a project investigating whether sparse autoencoders were useful for downstream tasks, notably out-of-distribution probing.
TL;DR

To validate whether SAEs were a worthwhile technique, we explored whether they were useful on the downstream task of OOD generalisation when detecting harmful intent in user prompts
[...]
---
**Outline:**
(01:08) TL;DR
(02:38) Introduction
(02:41) Motivation
(06:09) Our Task
(08:35) Conclusions and Strategic Updates
(13:59) Comparing different ways to train Chat SAEs
(18:30) Using SAEs for OOD Probing
(20:21) Technical Setup
(20:24) Datasets
(24:16) Probing
(26:48) Results
(30:36) Related Work and Discussion
(34:01) Is it surprising that SAEs didn't work?
(39:54) Dataset debugging with SAEs
(42:02) Autointerp and high frequency latents
(44:16) Removing High Frequency Latents from JumpReLU SAEs
(45:04) Method
(45:07) Motivation
(47:29) Modifying the sparsity penalty
(48:48) How we evaluated interpretability
(50:36) Results
(51:18) Reconstruction loss at fixed sparsity
(52:10) Frequency histograms
(52:52) Latent interpretability
(54:23) Conclusions
(56:43) Appendix
The original text contained 7 footnotes which were omitted from this narration.
---
First published:
March 26th, 2025
Source:
https://www.lesswrong.com/posts/4uXCAJNuPKtKBsi28/sae-progress-update-2-draft
---
Narrated by TYPE III AUDIO.
---
**Images from the article:**

[Linkpost] “Playing in the Creek” by Hastings Apr 11, 2025

Show notes

This is a link post. When I was a really small kid, one of my favorite activities was to try and dam up the creek in my backyard. I would carefully move rocks into high walls, pile up leaves, or try patching the holes with sand. The goal was just to see how high I could get the lake, knowing that if I plugged every hole, eventually the water would always rise and defeat my efforts. Beaver behaviour.
One day, I had the realization that there was a simpler approach. I could just go get a big 5 foot long shovel, and instead of intricately locking together rocks and leaves and sticks, I could collapse the sides of the riverbank down and really build a proper big dam. I went to ask my dad for the shovel to try this out, and he told me, very heavily paraphrasing, 'Congratulations. You've [...]
---
First published:
April 10th, 2025
Source:
https://www.lesswrong.com/posts/rLucLvwKoLdHSBTAn/playing-in-the-creek
Linkpost URL:
https://hgreer.com/PlayingInTheCreek
---
Narrated by TYPE III AUDIO.

Graph showing predicted arrival timeline for — **“Thoughts on AI 2027” by Max Harms** Apr 10, 2025
Show notes

This is part of the MIRI Single Author Series. Pieces in this series represent the beliefs and opinions of their named authors, and do not claim to speak for all of MIRI.
Okay, I'm annoyed at people covering AI 2027 burying the lede, so I'm going to try not to do that. The authors predict a strong chance that all humans will be (effectively) dead in 6 years, and this agrees with my best guess about the future. (My modal timeline has loss of control of Earth mostly happening in 2028, rather than late 2027, but nitpicking at that scale hardly matters.) Their timeline to transformative AI also seems pretty close to the perspective of frontier lab CEO's (at least Dario Amodei, and probably Sam Altman) and the aggregate market opinion of both Metaculus and Manifold!
If you look on those market platforms you get graphs like this:
Both [...]
---
**Outline:**
(02:23) Mode ≠ Median
(04:50) Theres a Decent Chance of Having Decades
(06:44) More Thoughts
(08:55) Mid 2025
(09:01) Late 2025
(10:42) Early 2026
(11:18) Mid 2026
(12:58) Late 2026
(13:04) January 2027
(13:26) February 2027
(14:53) March 2027
(16:32) April 2027
(16:50) May 2027
(18:41) June 2027
(19:03) July 2027
(20:27) August 2027
(22:45) September 2027
(24:37) October 2027
(26:14) November 2027 (Race)
(29:08) December 2027 (Race)
(30:53) 2028 and Beyond (Race)
(34:42) Thoughts on Slowdown
(38:27) Final Thoughts
---
First published:
April 9th, 2025
Source:
https://www.lesswrong.com/posts/Yzcb5mQ7iq4DFfXHx/thoughts-on-ai-2027
---
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.*

Graph showing projected data from 2025-2050, with peak around 2025-2027. — **“Thoughts on AI 2027” by Max Harms** Apr 10, 2025
Show notes

This is part of the MIRI Single Author Series. Pieces in this series represent the beliefs and opinions of their named authors, and do not claim to speak for all of MIRI.
Okay, I'm annoyed at people covering AI 2027 burying the lede, so I'm going to try not to do that. The authors predict a strong chance that all humans will be (effectively) dead in 6 years, and this agrees with my best guess about the future. (My modal timeline has loss of control of Earth mostly happening in 2028, rather than late 2027, but nitpicking at that scale hardly matters.) Their timeline to transformative AI also seems pretty close to the perspective of frontier lab CEO's (at least Dario Amodei, and probably Sam Altman) and the aggregate market opinion of both Metaculus and Manifold!
If you look on those market platforms you get graphs like this:
Both [...]
---
**Outline:**
(02:23) Mode ≠ Median
(04:50) Theres a Decent Chance of Having Decades
(06:44) More Thoughts
(08:55) Mid 2025
(09:01) Late 2025
(10:42) Early 2026
(11:18) Mid 2026
(12:58) Late 2026
(13:04) January 2027
(13:26) February 2027
(14:53) March 2027
(16:32) April 2027
(16:50) May 2027
(18:41) June 2027
(19:03) July 2027
(20:27) August 2027
(22:45) September 2027
(24:37) October 2027
(26:14) November 2027 (Race)
(29:08) December 2027 (Race)
(30:53) 2028 and Beyond (Race)
(34:42) Thoughts on Slowdown
(38:27) Final Thoughts
---
First published:
April 9th, 2025
Source:
https://www.lesswrong.com/posts/Yzcb5mQ7iq4DFfXHx/thoughts-on-ai-2027
---
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.*

“Short Timelines don’t Devalue Long Horizon Research” by Vladimir_Nesov Apr 09, 2025

Show notes

Short AI takeoff timelines seem to leave no time for some lines of alignment research to become impactful. But any research rebalances the mix of currently legible research directions that could be handed off to AI-assisted alignment researchers or early autonomous AI researchers whenever they show up. So even hopelessly incomplete research agendas could still be used to prompt future capable AI to focus on them, while in the absence of such incomplete research agendas we'd need to rely on AI's judgment more completely. This doesn't crucially depend on giving significant probability to long AI takeoff timelines, or on expected value in such scenarios driving the priorities.
Potential for AI to take up the torch makes it reasonable to still prioritize things that have no hope at all of becoming practical for decades (with human effort). How well AIs can be directed to advance a line of research [...]
---
First published:
April 9th, 2025
Source:
https://www.lesswrong.com/posts/3NdpbA6M5AM2gHvTW/short-timelines-don-t-devalue-long-horizon-research
---
Narrated by TYPE III AUDIO.