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

LessWrong (Curated & Popular)

LessWrong (Curated & Popular)

38:05

In late 1972, U.S. Marine Captain Ron Forrester disappeared on a bombing run into North Vietnam. Back home in Texas, his family could only wait and hope. Audio subscribers to Texas Monthly can get early access to episodes of the series, plus exclusive interviews and audio. Visit texasmonthly.com/audio to join. Go to HelloFresh.com/FLIGHT10FM to get 10 Free Meals with a Free Item For Life.…

30 days ago 2:10

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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 [...]
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First published:
April 9th, 2025
Source:
https://www.lesswrong.com/posts/3NdpbA6M5AM2gHvTW/short-timelines-don-t-devalue-long-horizon-research
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Narrated by TYPE III AUDIO.

509 episodes

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

LessWrong (Curated & Popular)

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published 30 days ago

MP3•Episode home

509 episodes

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1
“Orienting Toward Wizard Power” by johnswentworth 8:20

1 day ago8:20

8:20

For months, I had the feeling: something is wrong. Some core part of myself had gone missing. I had words and ideas cached, which pointed back to the missing part. There was the story of Benjamin Jesty, a dairy farmer who vaccinated his family against smallpox in 1774 - 20 years before the vaccination technique was popularized, and the same year King Louis XV of France died of the disease. There was another old post which declared “I don’t care that much about giant yachts. I want a cure for aging. I want weekend trips to the moon. I want flying cars and an indestructible body and tiny genetically-engineered dragons.”. There was a cached instinct to look at certain kinds of social incentive gradient, toward managing more people or growing an organization or playing social-political games, and say “no, it's a trap”. To go… in a different direction, orthogonal [...] --- Outline: (01:19) In Search of a Name (04:23) Near Mode --- First published: May 8th, 2025 Source: https://www.lesswrong.com/posts/Wg6ptgi2DupFuAnXG/orienting-toward-wizard-power --- Narrated by TYPE III AUDIO .…

1
“Interpretability Will Not Reliably Find Deceptive AI” by Neel Nanda 13:15

5 days ago13:15

13:15

(Disclaimer: Post written in a personal capacity. These are personal hot takes and do not in any way represent my employer's views.) TL;DR: I do not think we will produce high reliability methods to evaluate or monitor the safety of superintelligent systems via current research paradigms, with interpretability or otherwise. Interpretability seems a valuable tool here and remains worth investing in, as it will hopefully increase the reliability we can achieve. However, interpretability should be viewed as part of an overall portfolio of defences: a layer in a defence-in-depth strategy. It is not the one thing that will save us, and it still won’t be enough for high reliability. Introduction There's a common, often implicit, argument made in AI safety discussions: interpretability is presented as the only reliable path forward for detecting deception in advanced AI - among many other sources it was argued for in [...] --- Outline: (00:55) Introduction (02:57) High Reliability Seems Unattainable (05:12) Why Won't Interpretability be Reliable? (07:47) The Potential of Black-Box Methods (08:48) The Role of Interpretability (12:02) Conclusion The original text contained 5 footnotes which were omitted from this narration. --- First published: May 4th, 2025 Source: https://www.lesswrong.com/posts/PwnadG4BFjaER3MGf/interpretability-will-not-reliably-find-deceptive-ai --- Narrated by TYPE III AUDIO .…

1
“Slowdown After 2028: Compute, RLVR Uncertainty, MoE Data Wall” by Vladimir_Nesov 11:33

6 days ago11:33

11:33

It'll take until ~2050 to repeat the level of scaling that pretraining compute is experiencing this decade, as increasing funding can't sustain the current pace beyond ~2029 if AI doesn't deliver a transformative commercial success by then. Natural text data will also run out around that time, and there are signs that current methods of reasoning training might be mostly eliciting capabilities from the base model. If scaling of reasoning training doesn't bear out actual creation of new capabilities that are sufficiently general, and pretraining at ~2030 levels of compute together with the low hanging fruit of scaffolding doesn't bring AI to crucial capability thresholds, then it might take a while. Possibly decades, since training compute will be growing 3x-4x slower after 2027-2029 than it does now, and the ~6 years of scaling since the ChatGPT moment stretch to 20-25 subsequent years, not even having access to any [...] --- Outline: (01:14) Training Compute Slowdown (04:43) Bounded Potential of Thinking Training (07:43) Data Inefficiency of MoE The original text contained 4 footnotes which were omitted from this narration. --- First published: May 1st, 2025 Source: https://www.lesswrong.com/posts/XiMRyQcEyKCryST8T/slowdown-after-2028-compute-rlvr-uncertainty-moe-data-wall --- Narrated by TYPE III AUDIO .…

1
“Early Chinese Language Media Coverage of the AI 2027 Report: A Qualitative Analysis” by jeanne_, eeeee 27:35

9 days ago27:35

27:35

In this blog post, we analyse how the recent AI 2027 forecast by Daniel Kokotajlo, Scott Alexander, Thomas Larsen, Eli Lifland, and Romeo Dean has been discussed across Chinese language platforms. We present: Our research methodology and synthesis of key findings across media artefacts A proposal for how censorship patterns may provide signal for the Chinese government's thinking about AGI and the race to superintelligence A more detailed analysis of each of the nine artefacts, organised by type: Mainstream Media, Forum Discussion, Bilibili (Chinese Youtube) Videos, Personal Blogs. Methodology We conducted a comprehensive search across major Chinese-language platforms–including news outlets, video platforms, forums, microblogging sites, and personal blogs–to collect the media featured in this report. We supplemented this with Deep Research to identify additional sites mentioning AI 2027. Our analysis focuses primarily on content published in the first few days (4-7 April) following the report's release. More media [...] --- Outline: (00:58) Methodology (01:36) Summary (02:48) Censorship as Signal (07:29) Analysis (07:53) Mainstream Media (07:57) English Title: Doomsday Timeline is Here! Former OpenAI Researcher's 76-page Hardcore Simulation: ASI Takes Over the World in 2027, Humans Become NPCs (10:27) Forum Discussion (10:31) English Title: What do you think of former OpenAI researcher's AI 2027 predictions? (13:34) Bilibili Videos (13:38) English Title: \[AI 2027\] A mind-expanding wargame simulation of artificial intelligence competition by a former OpenAI researcher (15:24) English Title: Predicting AI Development in 2027 (17:13) Personal Blogs (17:16) English Title: Doomsday Timeline: AI 2027 Depicts the Arrival of Superintelligence and the Fate of Humanity Within the Decade (18:30) English Title: AI 2027: Expert Predictions on the Artificial Intelligence Explosion (21:57) English Title: AI 2027: A Science Fiction Article (23:16) English Title: Will AGI Take Over the World in 2027? (25:46) English Title: AI 2027 Prediction Report: AI May Fully Surpass Humans by 2027 (27:05) Acknowledgements --- First published: April 30th, 2025 Source: https://www.lesswrong.com/posts/JW7nttjTYmgWMqBaF/early-chinese-language-media-coverage-of-the-ai-2027-report --- Narrated by TYPE III AUDIO .…

1
[Linkpost] “Jaan Tallinn’s 2024 Philanthropy Overview” by jaan 1:17

15 days ago1:17

1:17

This is a link post. to follow up my philantropic pledge from 2020, i've updated my philanthropy page with the 2024 results. in 2024 my donations funded $51M worth of endpoint grants (plus $2.0M in admin overhead and philanthropic software development). this comfortably exceeded my 2024 commitment of $42M (20k times $2100.00 — the minimum price of ETH in 2024). this also concludes my 5-year donation pledge, but of course my philanthropy continues: eg, i’ve already made over $4M in endpoint grants in the first quarter of 2025 (not including 2024 grants that were slow to disburse), as well as pledged at least $10M to the 2025 SFF grant round. --- First published: April 23rd, 2025 Source: https://www.lesswrong.com/posts/8ojWtREJjKmyvWdDb/jaan-tallinn-s-2024-philanthropy-overview Linkpost URL: https://jaan.info/philanthropy/#2024-results --- Narrated by TYPE III AUDIO .…

1
“Impact, agency, and taste” by benkuhn 15:17

15 days ago15:17

15:17

I’ve been thinking recently about what sets apart the people who’ve done the best work at Anthropic. You might think that the main thing that makes people really effective at research or engineering is technical ability, and among the general population that's true. Among people hired at Anthropic, though, we’ve restricted the range by screening for extremely high-percentile technical ability, so the remaining differences, while they still matter, aren’t quite as critical. Instead, people's biggest bottleneck eventually becomes their ability to get leverage—i.e., to find and execute work that has a big impact-per-hour multiplier. For example, here are some types of work at Anthropic that tend to have high impact-per-hour, or a high impact-per-hour ceiling when done well (of course this list is extremely non-exhaustive!): Improving tooling, documentation, or dev loops. A tiny amount of time fixing a papercut in the right way can save [...] --- Outline: (03:28) 1. Agency (03:31) Understand and work backwards from the root goal (05:02) Don't rely too much on permission or encouragement (07:49) Make success inevitable (09:28) 2. Taste (09:31) Find your angle (11:03) Think real hard (13:03) Reflect on your thinking --- First published: April 19th, 2025 Source: https://www.lesswrong.com/posts/DiJT4qJivkjrGPFi8/impact-agency-and-taste --- Narrated by TYPE III AUDIO .…

1
[Linkpost] “To Understand History, Keep Former Population Distributions In Mind” by Arjun Panickssery 5:42

16 days ago5:42

5:42

This is a link post. Guillaume Blanc has a piece in Works in Progress (I assume based on his paper) about how France's fertility declined earlier than in other European countries, and how its power waned as its relative population declined starting in the 18th century. In 1700, France had 20% of Europe's population (4% of the whole world population). Kissinger writes in Diplomacy with respect to the Versailles Peace Conference: Victory brought home to France the stark realization that revanche had cost it too dearly, and that it had been living off capital for nearly a century. France alone knew just how weak it had become in comparison with Germany, though nobody else, especially not America, was prepared to believe it ... Though France's allies insisted that its fears were exaggerated, French leaders knew better. In 1880, the French had represented 15.7 percent of Europe's population. By 1900, that [...] --- First published: April 23rd, 2025 Source: https://www.lesswrong.com/posts/gk2aJgg7yzzTXp8HJ/to-understand-history-keep-former-population-distributions Linkpost URL: https://arjunpanickssery.substack.com/p/to-understand-history-keep-former --- 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.…

1
“AI-enabled coups: a small group could use AI to seize power” by Tom Davidson, Lukas Finnveden, rosehadshar 15:22

16 days ago15:22

15:22

We’ve written a new report on the threat of AI-enabled coups. I think this is a very serious risk – comparable in importance to AI takeover but much more neglected. In fact, AI-enabled coups and AI takeover have pretty similar threat models. To see this, here's a very basic threat model for AI takeover: Humanity develops superhuman AI Superhuman AI is misaligned and power-seeking Superhuman AI seizes power for itself And now here's a closely analogous threat model for AI-enabled coups: Humanity develops superhuman AI Superhuman AI is controlled by a small group Superhuman AI seizes power for the small group While the report focuses on the risk that someone seizes power over a country, I think that similar dynamics could allow someone to take over the world. In fact, if someone wanted to take over the world, their best strategy might well be to first stage an AI-enabled [...] --- Outline: (02:39) Summary (03:31) An AI workforce could be made singularly loyal to institutional leaders (05:04) AI could have hard-to-detect secret loyalties (06:46) A few people could gain exclusive access to coup-enabling AI capabilities (09:46) Mitigations (13:00) Vignette The original text contained 2 footnotes which were omitted from this narration. --- First published: April 16th, 2025 Source: https://www.lesswrong.com/posts/6kBMqrK9bREuGsrnd/ai-enabled-coups-a-small-group-could-use-ai-to-seize-power-1 --- Narrated by TYPE III AUDIO . --- Images from the article:…

1
“Accountability Sinks” by Martin Sustrik 28:50

17 days ago28:50

28:50

Back in the 1990s, ground squirrels were briefly fashionable pets, but their popularity came to an abrupt end after an incident at Schiphol Airport on the outskirts of Amsterdam. In April 1999, a cargo of 440 of the rodents arrived on a KLM flight from Beijing, without the necessary import papers. Because of this, they could not be forwarded on to the customer in Athens. But nobody was able to correct the error and send them back either. What could be done with them? It's hard to think there wasn’t a better solution than the one that was carried out; faced with the paperwork issue, airport staff threw all 440 squirrels into an industrial shredder. [...] It turned out that the order to destroy the squirrels had come from the Dutch government's Department of Agriculture, Environment Management and Fishing. However, KLM's management, with the benefit of hindsight, said that [...] --- First published: April 22nd, 2025 Source: https://www.lesswrong.com/posts/nYJaDnGNQGiaCBSB5/accountability-sinks --- 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.…

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

19 days ago10:46

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 .…

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

20 days ago1:15

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 .…

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

22 days ago35:51

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:…

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

23 days ago21:00

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.…

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

27 days ago57:32

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:…

1
[Linkpost] “Playing in the Creek” by Hastings 4:12

28 days ago4:12