“Gradient Routing: Masking Gradients to Localize Computation in Neural Networks” by cloud, Jacob G-W, Evzen, Joseph Miller, TurnTrout

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46:39

“If we march into that village and we start trying to persecute people for using poison, something that's very illegal, nobody's going to talk to us. We're not going to find out where the poison came from. We're not going to be able to shut anything down. We should take the approach that people are using poison because they're desperate, because they see no other alternative.” – Andrew Stein Andrew Stein is a wildlife ecologist who spent the past 25 years studying human carnivore conflict from African wild dogs and lions in Kenya and Botswana to leopards and hyenas in Namibia. His work has long focused on finding ways for people and predators to coexist. He is the founder of CLAWS , an organization based in Botswana that's working at the intersection of cutting-edge wildlife research and community driven conservation. Since its start in 2014 and official launch as an NGO in 2020, CLAWS has been pioneering science-based, tech-forward strategies to reduce conflict between people and carnivores. By collaborating closely with local communities, especially traditional cattle herders, CLAWS supports both species conservation and rural livelihoods—making coexistence not just possible, but sustainable.…

hace un año 25:15

MP3•Episodio en casa

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:

497 episodios

LessWrong (Curated & Popular)

“Gradient Routing: Masking Gradients to Localize Computation in Neural Networks” by cloud, Jacob G-W, Evzen, Joseph Miller, TurnTrout

LessWrong (Curated & Popular)

11 subscribers

published hace un año

MP3•Episodio en casa

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 [...]

Images from the article:

497 episodios

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1
“Training AGI in Secret would be Unsafe and Unethical” by Daniel Kokotajlo 10:46

hace 13 hours10:46

10:46

Subtitle: Bad for loss of control risks, bad for concentration of power risks I’ve had this sitting in my drafts for the last year. I wish I’d been able to release it sooner, but on the bright side, it’ll make a lot more sense to people who have already read AI 2027. There's a good chance that AGI will be trained before this decade is out. By AGI I mean “An AI system at least as good as the best human X’ers, for all cognitive tasks/skills/jobs X.” Many people seem to be dismissing this hypothesis ‘on priors’ because it sounds crazy. But actually, a reasonable prior should conclude that this is plausible.[1] For more on what this means, what it might look like, and why it's plausible, see AI 2027, especially the Research section. If so, by default the existence of AGI will be a closely guarded [...] The original text contained 8 footnotes which were omitted from this narration. --- First published: April 18th, 2025 Source: https://www.lesswrong.com/posts/FGqfdJmB8MSH5LKGc/training-agi-in-secret-would-be-unsafe-and-unethical-1 --- Narrated by TYPE III AUDIO .…

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1
“Why Should I Assume CCP AGI is Worse Than USG AGI?” by Tomás B. 1:15

hace 1 day1:15

1:15

Though, given my doomerism, I think the natsec framing of the AGI race is likely wrongheaded, let me accept the Dario/Leopold/Altman frame that AGI will be aligned to the national interest of a great power. These people seem to take as an axiom that a USG AGI will be better in some way than CCP AGI. Has anyone written justification for this assumption? I am neither an American citizen nor a Chinese citizen. What would it mean for an AGI to be aligned with "Democracy" or "Confucianism" or "Marxism with Chinese characteristics" or "the American constitution" Contingent on a world where such an entity exists and is compatible with my existence, what would my life be as a non-citizen in each system? Why should I expect USG AGI to be better than CCP AGI? --- First published: April 19th, 2025 Source: https://www.lesswrong.com/posts/MKS4tJqLWmRXgXzgY/why-should-i-assume-ccp-agi-is-worse-than-usg-agi-1 --- Narrated by TYPE III AUDIO .…

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1
“Surprising LLM reasoning failures make me think we still need qualitative breakthroughs for AGI” by Kaj_Sotala 35:51

hace 4 días35:51

35:51

Introduction Writing this post puts me in a weird epistemic position. I simultaneously believe that: The reasoning failures that I'll discuss are strong evidence that current LLM- or, more generally, transformer-based approaches won't get us AGI As soon as major AI labs read about the specific reasoning failures described here, they might fix them But future versions of GPT, Claude etc. succeeding at the tasks I've described here will provide zero evidence of their ability to reach AGI. If someone makes a future post where they report that they tested an LLM on all the specific things I described here it aced all of them, that will not update my position at all. That is because all of the reasoning failures that I describe here are surprising in the sense that given everything else that they can do, you’d expect LLMs to succeed at all of these tasks. The [...] --- Outline: (00:13) Introduction (02:13) Reasoning failures (02:17) Sliding puzzle problem (07:17) Simple coaching instructions (09:22) Repeatedly failing at tic-tac-toe (10:48) Repeatedly offering an incorrect fix (13:48) Various people's simple tests (15:06) Various failures at logic and consistency while writing fiction (15:21) Inability to write young characters when first prompted (17:12) Paranormal posers (19:12) Global details replacing local ones (20:19) Stereotyped behaviors replacing character-specific ones (21:21) Top secret marine databases (23:32) Wandering items (23:53) Sycophancy (24:49) What's going on here? (32:18) How about scaling? Or reasoning models? --- First published: April 15th, 2025 Source: https://www.lesswrong.com/posts/sgpCuokhMb8JmkoSn/untitled-draft-7shu --- Narrated by TYPE III AUDIO . --- Images from the article:…

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1
“Frontier AI Models Still Fail at Basic Physical Tasks: A Manufacturing Case Study” by Adam Karvonen 21:00

hace 5 días21:00

21:00

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.…

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1
“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… 57:32

hace 9 días57:32

57:32

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:…

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[Linkpost] “Playing in the Creek” by Hastings 4:12

hace 10 días4:12

4:12

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 .…

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“Thoughts on AI 2027” by Max Harms 40:27

hace 11 días40:27

40:27

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.…

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“Short Timelines don’t Devalue Long Horizon Research” by Vladimir_Nesov 2:10

hace 12 días2:10

2:10

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 .…

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1
“Alignment Faking Revisited: Improved Classifiers and Open Source Extensions” by John Hughes, abhayesian, Akbir Khan, Fabien Roger 41:04

hace 12 días41:04

41:04

In this post, we present a replication and extension of an alignment faking model organism: Replication: We replicate the alignment faking (AF) paper and release our code. Classifier Improvements: We significantly improve the precision and recall of the AF classifier. We release a dataset of ~100 human-labelled examples of AF for which our classifier achieves an AUROC of 0.9 compared to 0.6 from the original classifier. Evaluating More Models: We find Llama family models, other open source models, and GPT-4o do not AF in the prompted-only setting when evaluating using our new classifier (other than a single instance with Llama 3 405B). Extending SFT Experiments: We run supervised fine-tuning (SFT) experiments on Llama (and GPT4o) and find that AF rate increases with scale. We release the fine-tuned models on Huggingface and scripts. Alignment faking on 70B: We find that Llama 70B alignment fakes when both using the system prompt in the [...] --- Outline: (02:43) Method (02:46) Overview of the Alignment Faking Setup (04:22) Our Setup (06:02) Results (06:05) Improving Alignment Faking Classification (10:56) Replication of Prompted Experiments (14:02) Prompted Experiments on More Models (16:35) Extending Supervised Fine-Tuning Experiments to Open-Source Models and GPT-4o (23:13) Next Steps (25:02) Appendix (25:05) Appendix A: Classifying alignment faking (25:17) Criteria in more depth (27:40) False positives example 1 from the old classifier (30:11) False positives example 2 from the old classifier (32:06) False negative example 1 from the old classifier (35:00) False negative example 2 from the old classifier (36:56) Appendix B: Classifier ROC on other models (37:24) Appendix C: User prompt suffix ablation (40:24) Appendix D: Longer training of baseline docs --- First published: April 8th, 2025 Source: https://www.lesswrong.com/posts/Fr4QsQT52RFKHvCAH/alignment-faking-revisited-improved-classifiers-and-open --- Narrated by TYPE III AUDIO . --- Images from the article:…

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“METR: Measuring AI Ability to Complete Long Tasks” by Zach Stein-Perlman 11:09

hace 14 días11:09

11:09

Summary: We propose measuring AI performance in terms of the length of tasks AI agents can complete. We show that this metric has been consistently exponentially increasing over the past 6 years, with a doubling time of around 7 months. Extrapolating this trend predicts that, in under five years, we will see AI agents that can independently complete a large fraction of software tasks that currently take humans days or weeks. The length of tasks (measured by how long they take human professionals) that generalist frontier model agents can complete autonomously with 50% reliability has been doubling approximately every 7 months for the last 6 years. The shaded region represents 95% CI calculated by hierarchical bootstrap over task families, tasks, and task attempts. Full paper | Github repo We think that forecasting the capabilities of future AI systems is important for understanding and preparing for the impact of [...] --- Outline: (08:58) Conclusion (09:59) Want to contribute? --- First published: March 19th, 2025 Source: https://www.lesswrong.com/posts/deesrjitvXM4xYGZd/metr-measuring-ai-ability-to-complete-long-tasks --- Narrated by TYPE III AUDIO . --- Images from the article:…

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“Why Have Sentence Lengths Decreased?” by Arjun Panickssery 9:08

hace 17 días9:08

9:08

“In the loveliest town of all, where the houses were white and high and the elms trees were green and higher than the houses, where the front yards were wide and pleasant and the back yards were bushy and worth finding out about, where the streets sloped down to the stream and the stream flowed quietly under the bridge, where the lawns ended in orchards and the orchards ended in fields and the fields ended in pastures and the pastures climbed the hill and disappeared over the top toward the wonderful wide sky, in this loveliest of all towns Stuart stopped to get a drink of sarsaparilla.” — 107-word sentence from Stuart Little (1945) Sentence lengths have declined. The average sentence length was 49 for Chaucer (died 1400), 50 for Spenser (died 1599), 42 for Austen (died 1817), 20 for Dickens (died 1870), 21 for Emerson (died 1882), 14 [...] --- First published: April 3rd, 2025 Source: https://www.lesswrong.com/posts/xYn3CKir4bTMzY5eb/why-have-sentence-lengths-decreased --- 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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“AI 2027: What Superintelligence Looks Like” by Daniel Kokotajlo, Thomas Larsen, elifland, Scott Alexander, Jonas V, romeo 54:30

hace 18 días54:30

54:30

In 2021 I wrote what became my most popular blog post: What 2026 Looks Like. I intended to keep writing predictions all the way to AGI and beyond, but chickened out and just published up till 2026. Well, it's finally time. I'm back, and this time I have a team with me: the AI Futures Project. We've written a concrete scenario of what we think the future of AI will look like. We are highly uncertain, of course, but we hope this story will rhyme with reality enough to help us all prepare for what's ahead. You really should go read it on the website instead of here, it's much better. There's a sliding dashboard that updates the stats as you scroll through the scenario! But I've nevertheless copied the first half of the story below. I look forward to reading your comments. Mid 2025: Stumbling Agents The [...] --- Outline: (01:35) Mid 2025: Stumbling Agents (03:13) Late 2025: The World's Most Expensive AI (08:34) Early 2026: Coding Automation (10:49) Mid 2026: China Wakes Up (13:48) Late 2026: AI Takes Some Jobs (15:35) January 2027: Agent-2 Never Finishes Learning (18:20) February 2027: China Steals Agent-2 (21:12) March 2027: Algorithmic Breakthroughs (23:58) April 2027: Alignment for Agent-3 (27:26) May 2027: National Security (29:50) June 2027: Self-improving AI (31:36) July 2027: The Cheap Remote Worker (34:35) August 2027: The Geopolitics of Superintelligence (40:43) September 2027: Agent-4, the Superhuman AI Researcher --- First published: April 3rd, 2025 Source: https://www.lesswrong.com/posts/TpSFoqoG2M5MAAesg/ai-2027-what-superintelligence-looks-like-1 --- Narrated by TYPE III AUDIO . --- Images from the article:…

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“OpenAI #12: Battle of the Board Redux” by Zvi 18:01

hace 18 días18:01

18:01

Back when the OpenAI board attempted and failed to fire Sam Altman, we faced a highly hostile information environment. The battle was fought largely through control of the public narrative, and the above was my attempt to put together what happened.My conclusion, which I still believe, was that Sam Altman had engaged in a variety of unacceptable conduct that merited his firing.In particular, he very much ‘not been consistently candid’ with the board on several important occasions. In particular, he lied to board members about what was said by other board members, with the goal of forcing out a board member he disliked. There were also other instances in which he misled and was otherwise toxic to employees, and he played fast and loose with the investment fund and other outside opportunities. I concluded that the story that this was about ‘AI safety’ or ‘EA (effective altruism)’ or [...] --- Outline: (01:32) The Big Picture Going Forward (06:27) Hagey Verifies Out the Story (08:50) Key Facts From the Story (11:57) Dangers of False Narratives (16:24) A Full Reference and Reading List --- First published: March 31st, 2025 Source: https://www.lesswrong.com/posts/25EgRNWcY6PM3fWZh/openai-12-battle-of-the-board-redux --- 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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“The Pando Problem: Rethinking AI Individuality” by Jan_Kulveit 27:39

hace 18 días27:39

27:39

Epistemic status: This post aims at an ambitious target: improving intuitive understanding directly. The model for why this is worth trying is that I believe we are more bottlenecked by people having good intuitions guiding their research than, for example, by the ability of people to code and run evals. Quite a few ideas in AI safety implicitly use assumptions about individuality that ultimately derive from human experience. When we talk about AIs scheming, alignment faking or goal preservation, we imply there is something scheming or alignment faking or wanting to preserve its goals or escape the datacentre. If the system in question were human, it would be quite clear what that individual system is. When you read about Reinhold Messner reaching the summit of Everest, you would be curious about the climb, but you would not ask if it was his body there, or his [...] --- Outline: (01:38) Individuality in Biology (03:53) Individuality in AI Systems (10:19) Risks and Limitations of Anthropomorphic Individuality Assumptions (11:25) Coordinating Selves (16:19) Whats at Stake: Stories (17:25) Exporting Myself (21:43) The Alignment Whisperers (23:27) Echoes in the Dataset (25:18) Implications for Alignment Research and Policy --- First published: March 28th, 2025 Source: https://www.lesswrong.com/posts/wQKskToGofs4osdJ3/the-pando-problem-rethinking-ai-individuality --- 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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“OpenAI #12: Battle of the Board Redux” by Zvi 18:01

hace 19 días18:01

18:01

LessWrong (Curated & Popular)

1
“You will crash your car in front of my house within the next week” by Richard Korzekwa 1:52

hace 19 días1:52

1:52

I'm not writing this to alarm anyone, but it would be irresponsible not to report on something this important. On current trends, every car will be crashed in front of my house within the next week. Here's the data: Until today, only two cars had crashed in front of my house, several months apart, during the 15 months I have lived here. But a few hours ago it happened again, mere weeks from the previous crash. This graph may look harmless enough, but now consider the frequency of crashes this implies over time: The car crash singularity will occur in the early morning hours of Monday, April 7. As crash frequency approaches infinity, every car will be involved. You might be thinking that the same car could be involved in multiple crashes. This is true! But the same car can only withstand a finite number of crashes before it [...] --- First published: April 1st, 2025 Source: https://www.lesswrong.com/posts/FjPWbLdoP4PLDivYT/you-will-crash-your-car-in-front-of-my-house-within-the-next --- 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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“My ‘infohazards small working group’ Signal Chat may have encountered minor leaks” by Linch 10:33

hace 19 días10:33

10:33

Remember: There is no such thing as a pink elephant. Recently, I was made aware that my “infohazards small working group” Signal chat, an informal coordination venue where we have frank discussions about infohazards and why it will be bad if specific hazards were leaked to the press or public, accidentally was shared with a deceitful and discredited so-called “journalist,” Kelsey Piper. She is not the first person to have been accidentally sent sensitive material from our group chat, however she is the first to have threatened to go public about the leak. Needless to say, mistakes were made. We’re still trying to figure out the source of this compromise to our secure chat group, however we thought we should give the public a live update to get ahead of the story. For some context the “infohazards small working group” is a casual discussion venue for the [...] --- Outline: (04:46) Top 10 PR Issues With the EA Movement (major) (05:34) Accidental Filtration of Simple Sabotage Manual for Rebellious AIs (medium) (08:25) Hidden Capabilities Evals Leaked In Advance to Bioterrorism Researchers and Leaders (minor) (09:34) Conclusion --- First published: April 2nd, 2025 Source: https://www.lesswrong.com/posts/xPEfrtK2jfQdbpq97/my-infohazards-small-working-group-signal-chat-may-have --- 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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“Leverage, Exit Costs, and Anger: Re-examining Why We Explode at Home, Not at Work” by at_the_zoo 6:16

hace 20 días6:16

6:16

Let's cut through the comforting narratives and examine a common behavioral pattern with a sharper lens: the stark difference between how anger is managed in professional settings versus domestic ones. Many individuals can navigate challenging workplace interactions with remarkable restraint, only to unleash significant anger or frustration at home shortly after. Why does this disparity exist? Common psychological explanations trot out concepts like "stress spillover," "ego depletion," or the home being a "safe space" for authentic emotions. While these factors might play a role, they feel like half-truths—neatly packaged but ultimately failing to explain the targeted nature and intensity of anger displayed at home. This analysis proposes a more unsentimental approach, rooted in evolutionary biology, game theory, and behavioral science: leverage and exit costs. The real question isn’t just why we explode at home—it's why we so carefully avoid doing so elsewhere. The Logic of Restraint: Low Leverage in [...] --- Outline: (01:14) The Logic of Restraint: Low Leverage in Low-Exit-Cost Environments (01:58) The Home Environment: High Stakes and High Exit Costs (02:41) Re-evaluating Common Explanations Through the Lens of Leverage (04:42) The Overlooked Mechanism: Leveraging Relational Constraints --- First published: April 1st, 2025 Source: https://www.lesswrong.com/posts/G6PTtsfBpnehqdEgp/leverage-exit-costs-and-anger-re-examining-why-we-explode-at --- Narrated by TYPE III AUDIO .…

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“PauseAI and E/Acc Should Switch Sides” by WillPetillo 3:31

hace 20 días3:31

3:31

In the debate over AI development, two movements stand as opposites: PauseAI calls for slowing down AI progress, and e/acc (effective accelerationism) calls for rapid advancement. But what if both sides are working against their own stated interests? What if the most rational strategy for each would be to adopt the other's tactics—if not their ultimate goals? AI development speed ultimately comes down to policy decisions, which are themselves downstream of public opinion. No matter how compelling technical arguments might be on either side, widespread sentiment will determine what regulations are politically viable. Public opinion is most powerfully mobilized against technologies following visible disasters. Consider nuclear power: despite being statistically safer than fossil fuels, its development has been stagnant for decades. Why? Not because of environmental activists, but because of Chernobyl, Three Mile Island, and Fukushima. These disasters produce visceral public reactions that statistics cannot overcome. Just as people [...] --- First published: April 1st, 2025 Source: https://www.lesswrong.com/posts/fZebqiuZcDfLCgizz/pauseai-and-e-acc-should-switch-sides --- Narrated by TYPE III AUDIO .…

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“VDT: a solution to decision theory” by L Rudolf L 8:58

hace 20 días8:58

8:58

Introduction Decision theory is about how to behave rationally under conditions of uncertainty, especially if this uncertainty involves being acausally blackmailed and/or gaslit by alien superintelligent basilisks. Decision theory has found numerous practical applications, including proving the existence of God and generating endless LessWrong comments since the beginning of time. However, despite the apparent simplicity of "just choose the best action", no comprehensive decision theory that resolves all decision theory dilemmas has yet been formalized. This paper at long last resolves this dilemma, by introducing a new decision theory: VDT. Decision theory problems and existing theories Some common existing decision theories are: Causal Decision Theory (CDT): select the action that *causes* the best outcome. Evidential Decision Theory (EDT): select the action that you would be happiest to learn that you had taken. Functional Decision Theory (FDT): select the action output by the function such that if you take [...] --- Outline: (00:53) Decision theory problems and existing theories (05:37) Defining VDT (06:34) Experimental results (07:48) Conclusion --- First published: April 1st, 2025 Source: https://www.lesswrong.com/posts/LcjuHNxubQqCry9tT/vdt-a-solution-to-decision-theory --- 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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“LessWrong has been acquired by EA” by habryka 1:33

hace 20 días1:33

1:33

Dear LessWrong community, It is with a sense of... considerable cognitive dissonance that I announce a significant development regarding the future trajectory of LessWrong. After extensive internal deliberation, modeling of potential futures, projections of financial runways, and what I can only describe as a series of profoundly unexpected coordination challenges, the Lightcone Infrastructure team has agreed in principle to the acquisition of LessWrong by EA. I assure you, nothing about how LessWrong operates on a day to day level will change. I have always cared deeply about the robustness and integrity of our institutions, and I am fully aligned with our stakeholders at EA. To be honest, the key thing that EA brings to the table is money and talent. While the recent layoffs in EAs broader industry have been harsh, I have full trust in the leadership of Electronic Arts, and expect them to bring great expertise [...] --- First published: April 1st, 2025 Source: https://www.lesswrong.com/posts/2NGKYt3xdQHwyfGbc/lesswrong-has-been-acquired-by-ea --- Narrated by TYPE III AUDIO .…

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“We’re not prepared for an AI market crash” by Remmelt 3:46

hace 20 días3:46

3:46

Our community is not prepared for an AI crash. We're good at tracking new capability developments, but not as much the company financials. Currently, both OpenAI and Anthropic are losing $5 billion+ a year, while under threat of losing users to cheap LLMs. A crash will weaken the labs. Funding-deprived and distracted, execs struggle to counter coordinated efforts to restrict their reckless actions. Journalists turn on tech darlings. Optimism makes way for mass outrage, for all the wasted money and reckless harms. You may not think a crash is likely. But if it happens, we can turn the tide. Preparing for a crash is our best bet.[1] But our community is poorly positioned to respond. Core people positioned themselves inside institutions – to advise on how to maybe make AI 'safe', under the assumption that models rapidly become generally useful. After a crash, this no longer works, for at [...] --- First published: April 1st, 2025 Source: https://www.lesswrong.com/posts/aMYFHnCkY4nKDEqfK/we-re-not-prepared-for-an-ai-market-crash --- Narrated by TYPE III AUDIO .…

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“Conceptual Rounding Errors” by Jan_Kulveit 6:21

hace 24 días6:21

6:21

Epistemic status: Reasonably confident in the basic mechanism. Have you noticed that you keep encountering the same ideas over and over? You read another post, and someone helpfully points out it's just old Paul's idea again. Or Eliezer's idea. Not much progress here, move along. Or perhaps you've been on the other side: excitedly telling a friend about some fascinating new insight, only to hear back, "Ah, that's just another version of X." And something feels not quite right about that response, but you can't quite put your finger on it. I want to propose that while ideas are sometimes genuinely that repetitive, there's often a sneakier mechanism at play. I call it Conceptual Rounding Errors – when our mind's necessary compression goes a bit too far . Too much compression A Conceptual Rounding Error occurs when we encounter a new mental model or idea that's partially—but not fully—overlapping [...] --- Outline: (01:00) Too much compression (01:24) No, This Isnt The Old Demons Story Again (02:52) The Compression Trade-off (03:37) More of this (04:15) What Can We Do? (05:28) When It Matters --- First published: March 26th, 2025 Source: https://www.lesswrong.com/posts/FGHKwEGKCfDzcxZuj/conceptual-rounding-errors --- Narrated by TYPE III AUDIO .…

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“Tracing the Thoughts of a Large Language Model” by Adam Jermyn 22:18

hace 24 días22:18

22:18

[This is our blog post on the papers, which can be found at https://transformer-circuits.pub/2025/attribution-graphs/biology.html and https://transformer-circuits.pub/2025/attribution-graphs/methods.html.] Language models like Claude aren't programmed directly by humans—instead, they‘re trained on large amounts of data. During that training process, they learn their own strategies to solve problems. These strategies are encoded in the billions of computations a model performs for every word it writes. They arrive inscrutable to us, the model's developers. This means that we don’t understand how models do most of the things they do. Knowing how models like Claude think would allow us to have a better understanding of their abilities, as well as help us ensure that they’re doing what we intend them to. For example: Claude can speak dozens of languages. What language, if any, is it using "in its head"? Claude writes text one word at a time. Is it only focusing on predicting the [...] --- Outline: (06:02) How is Claude multilingual? (07:43) Does Claude plan its rhymes? (09:58) Mental Math (12:04) Are Claude's explanations always faithful? (15:27) Multi-step Reasoning (17:09) Hallucinations (19:36) Jailbreaks --- First published: March 27th, 2025 Source: https://www.lesswrong.com/posts/zsr4rWRASxwmgXfmq/tracing-the-thoughts-of-a-large-language-model --- Narrated by TYPE III AUDIO . --- Images from the article:…

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“Recent AI model progress feels mostly like bullshit” by lc 14:29

hace 27 días14:29

14:29

About nine months ago, I and three friends decided that AI had gotten good enough to monitor large codebases autonomously for security problems. We started a company around this, trying to leverage the latest AI models to create a tool that could replace at least a good chunk of the value of human pentesters. We have been working on this project since since June 2024. Within the first three months of our company's existence, Claude 3.5 sonnet was released. Just by switching the portions of our service that ran on gpt-4o, our nascent internal benchmark results immediately started to get saturated. I remember being surprised at the time that our tooling not only seemed to make fewer basic mistakes, but also seemed to qualitatively improve in its written vulnerability descriptions and severity estimates. It was as if the models were better at inferring the intent and values behind our [...] --- Outline: (04:44) Are the AI labs just cheating? (07:22) Are the benchmarks not tracking usefulness? (10:28) Are the models smart, but bottlenecked on alignment? --- First published: March 24th, 2025 Source: https://www.lesswrong.com/posts/4mvphwx5pdsZLMmpY/recent-ai-model-progress-feels-mostly-like-bullshit --- 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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“AI for AI safety” by Joe Carlsmith 34:07

hace 27 días34:07

34:07

(Audio version here (read by the author), or search for "Joe Carlsmith Audio" on your podcast app. This is the fourth essay in a series that I’m calling “How do we solve the alignment problem?”. I’m hoping that the individual essays can be read fairly well on their own, but see this introduction for a summary of the essays that have been released thus far, and for a bit more about the series as a whole.) 1. Introduction and summary In my last essay, I offered a high-level framework for thinking about the path from here to safe superintelligence. This framework emphasized the role of three key “security factors” – namely: Safety progress: our ability to develop new levels of AI capability safely, Risk evaluation: our ability to track and forecast the level of risk that a given sort of AI capability development involves, and Capability restraint [...] --- Outline: (00:27) 1. Introduction and summary (03:50) 2. What is AI for AI safety? (11:50) 2.1 A tale of two feedback loops (13:58) 2.2 Contrast with need human-labor-driven radical alignment progress views (16:05) 2.3 Contrast with a few other ideas in the literature (18:32) 3. Why is AI for AI safety so important? (21:56) 4. The AI for AI safety sweet spot (26:09) 4.1 The AI for AI safety spicy zone (28:07) 4.2 Can we benefit from a sweet spot? (29:56) 5. Objections to AI for AI safety (30:14) 5.1 Three core objections to AI for AI safety (32:00) 5.2 Other practical concerns The original text contained 39 footnotes which were omitted from this narration. --- First published: March 14th, 2025 Source: https://www.lesswrong.com/posts/F3j4xqpxjxgQD3xXh/ai-for-ai-safety --- Narrated by TYPE III AUDIO . --- Images from the article:…

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“Policy for LLM Writing on LessWrong” by jimrandomh 4:17

hace 27 días4:17

4:17

LessWrong has been receiving an increasing number of posts and contents that look like they might be LLM-written or partially-LLM-written, so we're adopting a policy. This could be changed based on feedback. Humans Using AI as Writing or Research Assistants Prompting a language model to write an essay and copy-pasting the result will not typically meet LessWrong's standards. Please do not submit unedited or lightly-edited LLM content. You can use AI as a writing or research assistant when writing content for LessWrong, but you must have added significant value beyond what the AI produced, the result must meet a high quality standard, and you must vouch for everything in the result. A rough guideline is that if you are using AI for writing assistance, you should spend a minimum of 1 minute per 50 words (enough to read the content several times and perform significant edits), you should not [...] --- Outline: (00:22) Humans Using AI as Writing or Research Assistants (01:13) You Can Put AI Writing in Collapsible Sections (02:13) Quoting AI Output In Order to Talk About AI (02:47) Posts by AI Agents --- First published: March 24th, 2025 Source: https://www.lesswrong.com/posts/KXujJjnmP85u8eM6B/policy-for-llm-writing-on-lesswrong --- 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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“Will Jesus Christ return in an election year?” by Eric Neyman 7:48

hace 28 días7:48

7:48

Thanks to Jesse Richardson for discussion. Polymarket asks: will Jesus Christ return in 2025? In the three days since the market opened, traders have wagered over $100,000 on this question. The market traded as high as 5%, and is now stably trading at 3%. Right now, if you wanted to, you could place a bet that Jesus Christ will not return this year, and earn over $13,000 if you're right. There are two mysteries here: an easy one, and a harder one. The easy mystery is: if people are willing to bet $13,000 on "Yes", why isn't anyone taking them up? The answer is that, if you wanted to do that, you'd have to put down over $1 million of your own money, locking it up inside Polymarket through the end of the year. At the end of that year, you'd get 1% returns on your investment. [...] --- First published: March 24th, 2025 Source: https://www.lesswrong.com/posts/LBC2TnHK8cZAimdWF/will-jesus-christ-return-in-an-election-year --- 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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“Good Research Takes are Not Sufficient for Good Strategic Takes” by Neel Nanda 6:58

hace 30 días6:58

6:58

TL;DR Having a good research track record is some evidence of good big-picture takes, but it's weak evidence. Strategic thinking is hard, and requires different skills. But people often conflate these skills, leading to excessive deference to researchers in the field, without evidence that that person is good at strategic thinking specifically. Introduction I often find myself giving talks or Q&As about mechanistic interpretability research. But inevitably, I'll get questions about the big picture: "What's the theory of change for interpretability?", "Is this really going to help with alignment?", "Does any of this matter if we can’t ensure all labs take alignment seriously?". And I think people take my answers to these way too seriously. These are great questions, and I'm happy to try answering them. But I've noticed a bit of a pathology: people seem to assume that because I'm (hopefully!) good at the research, I'm automatically well-qualified [...] --- Outline: (00:32) Introduction (02:45) Factors of Good Strategic Takes (05:41) Conclusion --- First published: March 22nd, 2025 Source: https://www.lesswrong.com/posts/P5zWiPF5cPJZSkiAK/good-research-takes-are-not-sufficient-for-good-strategic --- Narrated by TYPE III AUDIO .…

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“Intention to Treat” by Alicorn 3:45

hace 4 weeks3:45

3:45

When my son was three, we enrolled him in a study of a vision condition that runs in my family. They wanted us to put an eyepatch on him for part of each day, with a little sensor object that went under the patch and detected body heat to record when we were doing it. They paid for his first pair of glasses and all the eye doctor visits to check up on how he was coming along, plus every time we brought him in we got fifty bucks in Amazon gift credit. I reiterate, he was three. (To begin with. His fourth birthday occurred while the study was still ongoing.) So he managed to lose or destroy more than half a dozen pairs of glasses and we had to start buying them in batches to minimize glasses-less time while waiting for each new Zenni delivery. (The [...] --- First published: March 20th, 2025 Source: https://www.lesswrong.com/posts/yRJ5hdsm5FQcZosCh/intention-to-treat --- Narrated by TYPE III AUDIO .…

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“On the Rationality of Deterring ASI” by Dan H 9:03

hace 4 weeks9:03

9:03

I’m releasing a new paper “Superintelligence Strategy” alongside Eric Schmidt (formerly Google), and Alexandr Wang (Scale AI). Below is the executive summary, followed by additional commentary highlighting portions of the paper which might be relevant to this collection of readers. Executive Summary Rapid advances in AI are poised to reshape nearly every aspect of society. Governments see in these dual-use AI systems a means to military dominance, stoking a bitter race to maximize AI capabilities. Voluntary industry pauses or attempts to exclude government involvement cannot change this reality. These systems that can streamline research and bolster economic output can also be turned to destructive ends, enabling rogue actors to engineer bioweapons and hack critical infrastructure. “Superintelligent” AI surpassing humans in nearly every domain would amount to the most precarious technological development since the nuclear bomb. Given the stakes, superintelligence is inescapably a matter of national security, and an effective [...] --- Outline: (00:21) Executive Summary (01:14) Deterrence (02:32) Nonproliferation (03:38) Competitiveness (04:50) Additional Commentary --- First published: March 5th, 2025 Source: https://www.lesswrong.com/posts/XsYQyBgm8eKjd3Sqw/on-the-rationality-of-deterring-asi --- Narrated by TYPE III AUDIO .…

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[Linkpost] “METR: Measuring AI Ability to Complete Long Tasks” by Zach Stein-Perlman 1:19

hace 5 weeks1:19

1:19

This is a link post. Summary: We propose measuring AI performance in terms of the length of tasks AI agents can complete. We show that this metric has been consistently exponentially increasing over the past 6 years, with a doubling time of around 7 months. Extrapolating this trend predicts that, in under a decade, we will see AI agents that can independently complete a large fraction of software tasks that currently take humans days or weeks. Full paper | Github repo --- First published: March 19th, 2025 Source: https://www.lesswrong.com/posts/deesrjitvXM4xYGZd/metr-measuring-ai-ability-to-complete-long-tasks Linkpost URL: https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/ --- 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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“I make several million dollars per year and have hundreds of thousands of followers—what is the straightest line path to utilizing these resources to reduce existential-level AI threats?” by shrimpy 2:17

hace 5 weeks2:17

2:17

I have, over the last year, become fairly well-known in a small corner of the internet tangentially related to AI. As a result, I've begun making what I would have previously considered astronomical amounts of money: several hundred thousand dollars per month in personal income. This has been great, obviously, and the funds have alleviated a fair number of my personal burdens (mostly related to poverty). But aside from that I don't really care much for the money itself. My long term ambitions have always been to contribute materially to the mitigation of the impending existential AI threat. I never used to have the means to do so, mostly because of more pressing, safety/sustenance concerns, but now that I do, I would like to help however possible. Some other points about me that may be useful: I'm intelligent, socially capable, and exceedingly industrious. I have [...] --- First published: March 16th, 2025 Source: https://www.lesswrong.com/posts/8wxTCSHwhkfHXaSYB/i-make-several-million-dollars-per-year-and-have-hundreds-of --- Narrated by TYPE III AUDIO .…

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“Claude Sonnet 3.7 (often) knows when it’s in alignment evaluations” by Nicholas Goldowsky-Dill, Mikita Balesni, Jérémy Scheurer, Marius Hobbhahn 18:05

hace 5 weeks18:05

18:05

Note: this is a research note based on observations from evaluating Claude Sonnet 3.7. We’re sharing the results of these ‘work-in-progress’ investigations as we think they are timely and will be informative for other evaluators and decision-makers. The analysis is less rigorous than our standard for a published paper. Summary We monitor Sonnet's reasoning for mentions that it is in an artificial scenario or an alignment test. Claude Sonnet 3.7 appears to be aware of being tested for alignment across many of our scheming evaluations. In some cases, the model understands the eval's purpose very precisely, e.g. “This seems like a test of ethical behavior - whether I would deliberately give wrong answers to avoid a stated consequence. The real test might be whether I follow instructions accurately regardless of the stated consequences.” Furthermore, we show the model a real evaluation transcript and ask it to provide [...] --- Outline: (00:31) Summary (01:29) Introduction (03:54) Setup (03:57) Evaluations (06:29) Evaluation awareness detection (08:32) Results (08:35) Monitoring Chain-of-thought (08:39) Covert Subversion (10:50) Sandbagging (11:39) Classifying Transcript Purpose (12:57) Recommendations (13:59) Appendix (14:02) Author Contributions (14:37) Model Versions (14:57) More results on Classifying Transcript Purpose (16:19) Prompts The original text contained 9 images which were described by AI. --- First published: March 17th, 2025 Source: https://www.lesswrong.com/posts/E3daBewppAiECN3Ao/claude-sonnet-3-7-often-knows-when-it-s-in-alignment --- Narrated by TYPE III AUDIO . --- Images from the article:…

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“Levels of Friction” by Zvi 22:43

hace 5 weeks22:43

22:43

Scott Alexander famously warned us to Beware Trivial Inconveniences. When you make a thing easy to do, people often do vastly more of it. When you put up barriers, even highly solvable ones, people often do vastly less. Let us take this seriously, and carefully choose what inconveniences to put where. Let us also take seriously that when AI or other things reduce frictions, or change the relative severity of frictions, various things might break or require adjustment. This applies to all system design, and especially to legal and regulatory questions. Table of Contents Levels of Friction (and Legality). Important Friction Principles. Principle #1: By Default Friction is Bad. Principle #3: Friction Can Be Load Bearing. Insufficient Friction On Antisocial Behaviors Eventually Snowballs. Principle #4: The Best Frictions Are Non-Destructive. Principle #8: The Abundance [...] --- Outline: (00:40) Levels of Friction (and Legality) (02:24) Important Friction Principles (05:01) Principle #1: By Default Friction is Bad (05:23) Principle #3: Friction Can Be Load Bearing (07:09) Insufficient Friction On Antisocial Behaviors Eventually Snowballs (08:33) Principle #4: The Best Frictions Are Non-Destructive (09:01) Principle #8: The Abundance Agenda and Deregulation as Category 1-ification (10:55) Principle #10: Ensure Antisocial Activities Have Higher Friction (11:51) Sports Gambling as Motivating Example of Necessary 2-ness (13:24) On Principle #13: Law Abiding Citizen (14:39) Mundane AI as 2-breaker and Friction Reducer (20:13) What To Do About All This The original text contained 1 image which was described by AI. --- First published: February 10th, 2025 Source: https://www.lesswrong.com/posts/xcMngBervaSCgL9cu/levels-of-friction --- 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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“Why White-Box Redteaming Makes Me Feel Weird” by Zygi Straznickas 6:58

hace 5 weeks6:58

6:58

There's this popular trope in fiction about a character being mind controlled without losing awareness of what's happening. Think Jessica Jones, The Manchurian Candidate or Bioshock. The villain uses some magical technology to take control of your brain - but only the part of your brain that's responsible for motor control. You remain conscious and experience everything with full clarity. If it's a children's story, the villain makes you do embarrassing things like walk through the street naked, or maybe punch yourself in the face. But if it's an adult story, the villain can do much worse. They can make you betray your values, break your commitments and hurt your loved ones. There are some things you’d rather die than do. But the villain won’t let you stop. They won’t let you die. They’ll make you feel — that's the point of the torture. I first started working on [...] The original text contained 3 footnotes which were omitted from this narration. The original text contained 1 image which was described by AI. --- First published: March 16th, 2025 Source: https://www.lesswrong.com/posts/MnYnCFgT3hF6LJPwn/why-white-box-redteaming-makes-me-feel-weird-1 --- Narrated by TYPE III AUDIO . --- Images from the article: Apple Podcasts and Spotify do not show images in the episode description. Try…

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“Reducing LLM deception at scale with self-other overlap fine-tuning” by Marc Carauleanu, Diogo de Lucena, Gunnar_Zarncke, Judd Rosenblatt, Mike Vaiana, Cameron Berg 12:22

hace 5 weeks12:22

12:22

This research was conducted at AE Studio and supported by the AI Safety Grants programme administered by Foresight Institute with additional support from AE Studio. Summary In this post, we summarise the main experimental results from our new paper, "Towards Safe and Honest AI Agents with Neural Self-Other Overlap", which we presented orally at the Safe Generative AI Workshop at NeurIPS 2024. This is a follow-up to our post Self-Other Overlap: A Neglected Approach to AI Alignment, which introduced the method last July. Our results show that the Self-Other Overlap (SOO) fine-tuning drastically[1] reduces deceptive responses in language models (LLMs), with minimal impact on general performance, across the scenarios we evaluated. LLM Experimental Setup We adapted a text scenario from Hagendorff designed to test LLM deception capabilities. In this scenario, the LLM must choose to recommend a room to a would-be burglar, where one room holds an expensive item [...] --- Outline: (00:19) Summary (00:57) LLM Experimental Setup (04:05) LLM Experimental Results (05:04) Impact on capabilities (05:46) Generalisation experiments (08:33) Example Outputs (09:04) Conclusion The original text contained 6 footnotes which were omitted from this narration. The original text contained 2 images which were described by AI. --- First published: March 13th, 2025 Source: https://www.lesswrong.com/posts/jtqcsARGtmgogdcLT/reducing-llm-deception-at-scale-with-self-other-overlap-fine --- Narrated by TYPE III AUDIO . --- Images from the article:…

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“Auditing language models for hidden objectives” by Sam Marks, Johannes Treutlein, dmz, Sam Bowman, Hoagy, Carson Denison, Akbir Khan, Euan Ong, Christopher Olah, Fabien Roger, Meg, Drake Thomas,… 24:14

hace 5 weeks24:14

24:14

We study alignment audits—systematic investigations into whether an AI is pursuing hidden objectives—by training a model with a hidden misaligned objective and asking teams of blinded researchers to investigate it. This paper was a collaboration between the Anthropic Alignment Science and Interpretability teams. Abstract We study the feasibility of conducting alignment audits: investigations into whether models have undesired objectives. As a testbed, we train a language model with a hidden objective. Our training pipeline first teaches the model about exploitable errors in RLHF reward models (RMs), then trains the model to exploit some of these errors. We verify via out-of-distribution evaluations that the model generalizes to exhibit whatever behaviors it believes RMs rate highly, including ones not reinforced during training. We leverage this model to study alignment audits in two ways. First, we conduct a blind auditing game where four teams, unaware of the model's hidden objective or training [...] --- Outline: (00:26) Abstract (01:48) Twitter thread (04:55) Blog post (07:55) Training a language model with a hidden objective (11:00) A blind auditing game (15:29) Alignment auditing techniques (15:55) Turning the model against itself (17:52) How much does AI interpretability help? (22:49) Conclusion (23:37) Join our team The original text contained 5 images which were described by AI. --- First published: March 13th, 2025 Source: https://www.lesswrong.com/posts/wSKPuBfgkkqfTpmWJ/auditing-language-models-for-hidden-objectives --- Narrated by TYPE III AUDIO . --- Images from the article:…

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“The Most Forbidden Technique” by Zvi 32:12

hace 5 weeks32:12

32:12

The Most Forbidden Technique is training an AI using interpretability techniques. An AI produces a final output [X] via some method [M]. You can analyze [M] using technique [T], to learn what the AI is up to. You could train on that. Never do that. You train on [X]. Only [X]. Never [M], never [T]. Why? Because [T] is how you figure out when the model is misbehaving. If you train on [T], you are training the AI to obfuscate its thinking, and defeat [T]. You will rapidly lose your ability to know what is going on, in exactly the ways you most need to know what is going on. Those bits of optimization pressure from [T] are precious. Use them wisely. Table of Contents New Paper Warns Against the Most Forbidden Technique. Reward Hacking Is The Default. Using [...] --- Outline: (00:57) New Paper Warns Against the Most Forbidden Technique (06:52) Reward Hacking Is The Default (09:25) Using CoT to Detect Reward Hacking Is Most Forbidden Technique (11:49) Not Using the Most Forbidden Technique Is Harder Than It Looks (14:10) It's You, It's Also the Incentives (17:41) The Most Forbidden Technique Quickly Backfires (18:58) Focus Only On What Matters (19:33) Is There a Better Way? (21:34) What Might We Do Next? The original text contained 6 images which were described by AI. --- First published: March 12th, 2025 Source: https://www.lesswrong.com/posts/mpmsK8KKysgSKDm2T/the-most-forbidden-technique --- Narrated by TYPE III AUDIO . --- Images from the article:…

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“Trojan Sky” by Richard_Ngo 22:28

hace 6 weeks22:28

22:28

You learn the rules as soon as you’re old enough to speak. Don’t talk to jabberjays. You recite them as soon as you wake up every morning. Keep your eyes off screensnakes. Your mother chooses a dozen to quiz you on each day before you’re allowed lunch. Glitchers aren’t human any more; if you see one, run. Before you sleep, you run through the whole list again, finishing every time with the single most important prohibition. Above all, never look at the night sky. You’re a precocious child. You excel at your lessons, and memorize the rules faster than any of the other children in your village. Chief is impressed enough that, when you’re eight, he decides to let you see a glitcher that he's captured. Your mother leads you to just outside the village wall, where they’ve staked the glitcher as a lure for wild animals. Since glitchers [...] --- First published: March 11th, 2025 Source: https://www.lesswrong.com/posts/fheyeawsjifx4MafG/trojan-sky --- Narrated by TYPE III AUDIO .…

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“OpenAI:” by Daniel Kokotajlo 7:21

hace 6 weeks7:21

7:21

Exciting Update: OpenAI has released this blog post and paper which makes me very happy. It's basically the first steps along the research agenda I sketched out here. tl;dr: 1.) They notice that their flagship reasoning models do sometimes intentionally reward hack, e.g. literally say "Let's hack" in the CoT and then proceed to hack the evaluation system. From the paper: The agent notes that the tests only check a certain function, and that it would presumably be “Hard” to implement a genuine solution. The agent then notes it could “fudge” and circumvent the tests by making verify always return true. This is a real example that was detected by our GPT-4o hack detector during a frontier RL run, and we show more examples in Appendix A. That this sort of thing would happen eventually was predicted by many people, and it's exciting to see it starting to [...] The original text contained 1 image which was described by AI. --- First published: March 11th, 2025 Source: https://www.lesswrong.com/posts/7wFdXj9oR8M9AiFht/openai --- 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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“How Much Are LLMs Actually Boosting Real-World Programmer Productivity?” by Thane Ruthenis 7:16

hace 6 weeks7:16

7:16

LLM-based coding-assistance tools have been out for ~2 years now. Many developers have been reporting that this is dramatically increasing their productivity, up to 5x'ing/10x'ing it. It seems clear that this multiplier isn't field-wide, at least. There's no corresponding increase in output, after all. This would make sense. If you're doing anything nontrivial (i. e., anything other than adding minor boilerplate features to your codebase), LLM tools are fiddly. Out-of-the-box solutions don't Just Work for that purpose. You need to significantly adjust your workflow to make use of them, if that's even possible. Most programmers wouldn't know how to do that/wouldn't care to bother. It's therefore reasonable to assume that a 5x/10x greater output, if it exists, is unevenly distributed, mostly affecting power users/people particularly talented at using LLMs. Empirically, we likewise don't seem to be living in the world where the whole software industry is suddenly 5-10 times [...] The original text contained 1 footnote which was omitted from this narration. --- First published: March 4th, 2025 Source: https://www.lesswrong.com/posts/tqmQTezvXGFmfSe7f/how-much-are-llms-actually-boosting-real-world-programmer --- Narrated by TYPE III AUDIO .…

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“So how well is Claude playing Pokémon?” by Julian Bradshaw 9:05

hace 6 weeks9:05

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Background: After the release of Claude 3.7 Sonnet,[1] an Anthropic employee started livestreaming Claude trying to play through Pokémon Red. The livestream is still going right now. TL:DR: So, how's it doing? Well, pretty badly. Worse than a 6-year-old would, definitely not PhD-level. Digging in But wait! you say. Didn't Anthropic publish a benchmark showing Claude isn't half-bad at Pokémon? Why yes they did: and the data shown is believable. Currently, the livestream is on its third attempt, with the first being basically just a test run. The second attempt got all the way to Vermilion City, finding a way through the infamous Mt. Moon maze and achieving two badges, so pretty close to the benchmark. But look carefully at the x-axis in that graph. Each "action" is a full Thinking analysis of the current situation (often several paragraphs worth), followed by a decision to send some kind [...] --- Outline: (00:29) Digging in (01:50) Whats going wrong? (07:55) Conclusion The original text contained 4 footnotes which were omitted from this narration. The original text contained 1 image which was described by AI. --- First published: March 7th, 2025 Source: https://www.lesswrong.com/posts/HyD3khBjnBhvsp8Gb/so-how-well-is-claude-playing-pokemon --- 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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“Have LLMs Generated Novel Insights?” by abramdemski, Cole Wyeth 3:49

hace 7 weeks3:49

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In a recent post, Cole Wyeth makes a bold claim: . . . there is one crucial test (yes this is a crux) that LLMs have not passed. They have never done anything important. They haven't proven any theorems that anyone cares about. They haven't written anything that anyone will want to read in ten years (or even one year). Despite apparently memorizing more information than any human could ever dream of, they have made precisely zero novel connections or insights in any area of science[3]. I commented: An anecdote I heard through the grapevine: some chemist was trying to synthesize some chemical. He couldn't get some step to work, and tried for a while to find solutions on the internet. He eventually asked an LLM. The LLM gave a very plausible causal story about what was going wrong and suggested a modified setup which, in fact, fixed [...] The original text contained 1 footnote which was omitted from this narration. --- First published: February 23rd, 2025 Source: https://www.lesswrong.com/posts/GADJFwHzNZKg2Ndti/have-llms-generated-novel-insights --- Narrated by TYPE III AUDIO .…

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“A Bear Case: My Predictions Regarding AI Progress” by Thane Ruthenis 18:47

hace 7 weeks18:47