TechnologySociety & CulturePhilosophy

LessWrong (30+ Karma)

LessWrong

Audio narrations of LessWrong posts.

Play

Open in

565 episodes
Avg 19 min
English

Episodes 565

Newest Oldest Longest

All20 Unplayed20 In progress0 Played0

Counted on this page — what you have heard stays on this device, so it is not something the list can be paged by.

September 21 · 10 min
“Mech Interp is a Verifiable Task” by Logan Riggs
If we think parts of MLP0-MLP3 are computing [a sorting algorithm], we can replace those parts with [a sorting algorithm] and check reconstruction loss. However, reconstruction loss is not enough. Suppose we replace MLP0 with two things: Its mean activation - simple, but poor reconstruction MLP0 - perfect reconstruction, but no reduction in complexity We can visualize this as a pareto frontier trading off reconstruction with "simplicity". Ideally we achieve perfect reconstruction with perfect simplicity. For more intuition on the pareto frontier, we could have an MLP that clusters all inputs in two clusters: "early positions" and "late positions", which would be slightly more complex than the mean. We can make this an RLVR environment, if only we could clearly... Define "Simplicity" Defining simplicity has been complex. But what do we want from a perfectly decomposed model? If we've "perfectly decomposed" a model, then I'd expect ideal circuits to fall out, with "ideal" meaning: Help predict OOD behavior Given an [addition] circuit, we can know which types of inputs it'll succeed & fail on (and why) Be extractable & minimal The smallest part of the model that does [addition] Be removable w/ minimal harm to [...] --- Outline: (01:16) Define "Simplicity" (03:16) Tensor Networks Don't Solve This Issue (03:54) Red Herrings of Simplicity (05:42) How to Gain Tractability (06:09) Tract 1: Death Success by 1000 Circuits (07:02) Tract 2: QK OV Circuits but for Everything (08:45) Tract 3: Interpreting Small Models (09:50) Big if True The original text contained 6 footnotes which were omitted from this narration. --- First published: September 21st, 2026 Source: https://www.lesswrong.com/posts/QxHuKtfGfzn8uokoR/mech-interp-is-a-verifiable-task --- 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.
September 21 · 3 min
“Weight smuggling likely defeats attempts to cap FLOPs per training run” by Paul W, Pierre Peigné
Epistemic status: >90% confidence in the principle, >70% confidence that mitigating these would be hard in practice, no full implementation yet. tldr: it seems difficult for verification mechanisms to prevent chaining runs together or aggregating parallel ones; per-run FLOP caps could thus be covertly bypassed. 1. If governments want to regulate frontier AI training, one might want to cap individual training runs, e.g. putting bounds on the number of FLOPs per training run, and making sure each run starts either from scratch (i.e. random initialization at pre-training) or from a known model with accounted FLOPs (e.g. post-training, continuous pre-training, etc). In the former we could have the initial weights generated by the verifier in some way, or ask for a proof that the weights were initialized with a verifier-controlled random seed. In the later we could ask for a proof that the weights corresponds to unaltered registered weights from a previous training run. This approach would allow to enforce agreements or regulation targeting a specific model (or model generation) based on FLOPS thresholds: triggering specific evals beyond a specific threshold and possibly proving that a hard threshold has not been reached. [...] The original text contained 6 footnotes which were omitted from this narration. --- First published: September 21st, 2026 Source: https://www.lesswrong.com/posts/neT4ncKSiJ6QLt4d7/weight-smuggling-likely-defeats-attempts-to-cap-flops-per --- Narrated by TYPE III AUDIO.
September 21 · 20 min
“We’re not ready for the e/Acc × Longevity preference cascade” by Jackson Wagner
Anti-aging sentiment might go rapidly mainstream, in the same way AI Safety just did. Recently, the AI safety community has been enjoying a massive preference cascade that has rapidly moved AI x-risk concerns into mainstream political discourse Why did this happen? The basic idea seems like it should have been obvious for a long time: “IF we create self-improving machines that rapidly become much smarter than humans, THEN that seems like that story might not end well for the humans, so we should be very careful.” If the idea is so sensible, why are people only now endorsing it? Proximately, because of HuggingFace, Jacob Coxon, etc. But in a larger sense, clearly the trigger was that the premise (“IF we create self-improving machines…”) no longer strikes people as absurd. Instead, it seems worryingly plausible. This not only unlocks huge amounts of energy from many new people people thinking the “IF” half of the statement is likely, but also – weirdly, illogically– seems to furthermore cause lots of people to change their minds on the “THEN” half of the statement, too. Why did the flip happen so quickly? This is actually the case for lots of movements – [...] --- Outline: (00:12) Anti-aging sentiment might go rapidly mainstream, in the same way AI Safety just did. (03:03) The labs are probably working towards this. (04:12) Although perhaps a cascade wouldn't happen or would come too late to matter. (06:53) A longevity preference-cascade would be great! Except, by default, the energies unlocked might flow directly into e/acc :( (11:12) Of course I'm not saying we should oppose medical cures! (12:21) So then what SHOULD we do? The original text contained 2 footnotes which were omitted from this narration. --- First published: September 21st, 2026 Source: https://www.lesswrong.com/posts/BKc4xrqN4mdXoprzy/we-re-not-ready-for-the-e-acc-longevity-preference-cascade --- Narrated by TYPE III AUDIO.
September 21 · 14 min
“Alignment Midtraining Cracks Under Pressure” by J Bostock, sidbaines, Daniel Tan, draganover, ma-rmartinez
TL;DR We stress-test alignment midtraining (AMT) across model and token budget scales. Our results suggest that midtraining cannot tackle the hard problems of AI alignment—namely distributional shift and reward underspecification in the presence of imperfect data. For instance, we test whether midtrained motivations are robust to finetuning which elicits competing motivations. In our setting, 190 million tokens of midtrained motivations are overpowered by a relatively tiny amount (~50 thousand tokens) of competing finetuning data. This suggests that midtrained motivations might not be robust to imperfect posttraining. Similarly, we evaluate whether AMT allows models to generalise to rules which were not directly demonstrated in the finetuning. We find that the capacity for such generalisation is surprisingly low. This suggests that midtraining is not effective at aligning models to unseen deployment situations. In one experiment, we midtrained GLM-4.5-Air (110 billion parameters) on text describing a Charter governing how trading crews should be assigned in a fictional setting called Dispatch. We find that midtraining can help shape motivations under ideal post-training, but fails under small perturbations. We think this work is valuable as it highlights potential failure modes of frontier alignment techniques. We encourage others to do more red-teaming of labs' alignment [...] --- Outline: (00:12) TL;DR (02:08) Background (08:20) Setting (10:45) Results Summary (10:48) Midtraining can steer behaviour under ambiguous demonstrations, but small doses of conflicting data can override this. (10:58) en-US-AvaMultilingualNeural__ Bar charts titled "Midtrain" and "Charter Midtrain" showing eval choice percentages and chat eval scores. (12:11) Midtraining is less effective when rules are stated rather than demonstrated (13:17) Conclusions The original text contained 3 footnotes which were omitted from this narration. --- First published: September 21st, 2026 Source: https://www.lesswrong.com/posts/QH86EzNsjRw3wtCGs/alignment-midtraining-cracks-under-pressure --- 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.
September 21 · 8 min
“Empirical safety claims from frontier labs should be replicated, scrutinized, and open-sourced” by Zephaniah Roe, yix
When frontier labs like Anthropic and OpenAI publish safety or alignment research, it is often entirely empirical, closed-source, and sparse on methodological details. While it is great that they publish these results, the status quo is that labs (or soon, their agents) can claim alignment progress that no one independently verifies. The AI safety community has replicated or stress-tested some claims, but it's nowhere near comprehensive, and we expect this kind of meta-science to remain systematically neglected. We argue there should be a dedicated effort to Replicate alignment experiments from frontier labs. Scrutinize the experiments by stress-testing the methodology. Open-source replications to encourage external researchers to validate our work, build on the experiment, and further audit the lab's methods. The case to replicate safety research from labs CEOs and employees at AI companies, somewhat regularly, say that the technology they hope to develop could cause human extinction. However, their research to prevent this is often released without code or even basic methodological details (e.g., Teaching Claude Why, Beneficial RL). There's good reason to think some of these results could be fragile. Prior safety results can be contingent on details that are easy to miss, like the pinned OpenRouter [...] --- Outline: (01:10) The case to replicate safety research from labs (02:54) Replications are not shiny, but that's precisely what makes them counterfactually useful (03:38) The case to stress test (05:23) The case to open source (06:08) Replicating frontier lab work is difficult but tractable (07:07) Conclusion The original text contained 3 footnotes which were omitted from this narration. --- First published: September 20th, 2026 Source: https://www.lesswrong.com/posts/MmfzfGcQ3h3p6N9pD/empirical-safety-claims-from-frontier-labs-should-be-1 --- Narrated by TYPE III AUDIO.
September 20 · 8 min
“Please Give Them a Chance: On China, Rationalism, and AI Safety” by gzjw
When I finished HPMOR, I immediately knew it was the best novel I had read in more than a decade. I only wished I had found it sooner. When I started reading The Sequences, I discovered that the Chinese translation group had translated only the first volume. When I graduated from university, two years ago, AI translation had only just become good enough to convey the meaning of an article with reasonable accuracy. It was only about a year and a half ago that I truly found my way here and began engaging seriously with rationalism. My score on the Chinese college entrance exam was only slightly above the cutoff for what was then called a first-tier university. At university, my grades were near the bottom of my year, and I almost failed to graduate. It is probably fair to say that the vast majority of graduates from first-tier Chinese universities are smarter and more capable than I am. English has always been my worst subject. From childhood through school, I could barely pass it. I have now been working for two and a half years and have saved about 15,000 [...] --- First published: September 20th, 2026 Source: https://www.lesswrong.com/posts/GoX3uYQ4QN5HKvL7u/please-give-them-a-chance-on-china-rationalism-and-ai-safety --- Narrated by TYPE III AUDIO.
September 20 · 18 min
“We’ve saved the world before: what the ozone hole teaches us about AI” by leogao
It might destroy the world, despite passing every known safety test. If we wait for a “warning shot” before we act, it might be too late. And action requires global coordination, because if anyone makes it, everyone dies. Sound familiar? It should, because it already happened half a century ago, with chlorofluorocarbons (CFCs). Despite seemingly impossible odds, we got our act together and completely solved the problem through unprecedentedly successful international coordination. The Montreal Protocol banning CFCs, signed 39 years ago today, is the only treaty that has ever been ratified by every single country in the entire world. Total Montreal protocol victory Making AI go well is going to be a lot harder than fixing the ozone hole. Nonetheless, the similarity is uncanny, and we don’t have any other choice. Understanding how we did the impossible once before may teach us something about how to do it again. The theory is born The year is 1973. The slow televised unraveling of the Nixon administration is already well underway. DDT finally got banned last year by the newly created EPA. A river got so polluted that it literally caught on fire. The Cuyahoga River Fire Environmentalism looms large in [...] --- Outline: (01:24) The theory is born (03:39) The world reacts (06:44) The dark years (09:23) An unexpected finding from an unexpected finder (11:26) The warning shot (13:13) A journey to the edge of the world (15:01) Flying into the storm (16:30) The world listens The original text contained 5 footnotes which were omitted from this narration. --- First published: September 20th, 2026 Source: https://www.lesswrong.com/posts/zxXPEtSSSEdwpjopb/we-ve-saved-the-world-before-what-the-ozone-hole-teaches-us --- 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.
September 20 · 3 min
“The Anatomy of a Chinese AI Researcher” by CMLKevin
The Chinese AI researcher has read the Three Body Problem series of sci-fi novels since high school, and understand the concept of existential risk vaguely. He is fascinated by Ye Wenjie, the researcher that turned against humanity in that book, and decides that in the future if AI progress leads to a superior intelligence, he might be tempted to become Ye if there's no good alternative. He performs the duties of capabilities research in a Chinese frontier lab, seeking to one day achieve parity with Western companies, though he knows this is difficult. He has a mentality of hillclimbing, believing that the progress of a future technology is highly uncertain and even unknowable, and so him and his peers could only tread one step at a time. He looks at the western world and sees what is typical when a great technology is developed: the first mover will decide to impose restrictions to further their lead, while latecomers should use whatever means necessary to widen access to the whole world. He thinks of the AI chip restrictions as evidence of this. He uses Anthropic and OpenAI models regularly in his day to day work. He [...] --- First published: September 19th, 2026 Source: https://www.lesswrong.com/posts/qmxkHm2dTLKG6GZ6i/the-anatomy-of-a-chinese-ai-researcher --- Narrated by TYPE III AUDIO.
September 20 · 3 min
“Why I Stay Off Twitter” by jefftk
I avoid Twitter (𝕏) for similar reasons to drugs: I think it would change me for the worse, and I would be unable to give it up. After staying off Twitter reasonably successfully for years, I cross-posted my AI Tweets there a few weeks ago. I had something very Twitter-shaped to say, and I thought it was important to get out, so I do think this was worth it. And it all went well: none of this is complaining about the comments I got there. Coming back a few times to check notifications, however, it's been very good at baiting me: Tweets that are confidently wrong in cases where I have relevant and uncommon knowledge. The pull to dive in and share what I know is very strong! Then this bleeds over to the far broader case where people are wrong, and you have a large potential time sink. If it were just the time sink, I'd stop resisting. I spend some time on HN and Reddit, and to the extent that Twitter could substitute for that by showing me things I was more interested in, that wouldn't be an issue. The real problem [...] --- First published: September 19th, 2026 Source: https://www.lesswrong.com/posts/tvwtwgcujTfep4HgY/why-i-stay-off-twitter --- Narrated by TYPE III AUDIO.
September 20 · 3 min
“NYT Editorial Board Comes Out Against Extinction” by Ben Pace
(Archive link) The NYT editorial board's article on AI (archive link) is far better than I'd expected, but at the same time not all I'd hoped for. The title sets off very well: "Humanity Has Avoided Apocalypse Before. Let's Do It Again." It is truly excellent to see the extinction threat from loss of control be mainlined. A quick gloss of their policy requests: an AI Commission in government, licensing requirements for AI companies, an AI "constitution" written by the US Government incorporated into AIs, mandatory watermarks/identifiers on all AI content, mandatory independent testing for AI models before release, and a government agency to investigate accidents. Internationally, they call for tightening export controls, limiting China's access to semiconductors, and ultimately negotiating an international slowdown with China and an international framework for AI oversight. These are all steps in the right direction—of taking AI seriously. That said, it isn't clear if the licensing is required for training or for selling AIs. The idea that constitutional AI "would ensure alignment with human values" is of course not remotely true. And mandatory testing should apply to all models trained, not all models released, of course, and this is a glaring oversight. But [...] --- First published: September 19th, 2026 Source: https://www.lesswrong.com/posts/gDQzntJCusNbshWyD/nyt-editorial-board-comes-out-against-extinction --- Narrated by TYPE III AUDIO.
September 20 · 3 min
“Common mistakes in AI safety group organizing” by Nikola Jurkovic
Back in the day, I was a very active AI safety group organizer. I commonly notice people making the same mistakes across many clubs. I have written down a list of some of these mistakes hoping people will avoid them in the future: Reading groups often require that people read things before meetings. This is a mistake. People often don't do the readings. And the lack of common knowledge that everyone has read the reading degrades the conversation quality. Instead, have longer meetings, serve food (so, lunch/dinner meeting slots), and read during the actual meeting. Reading groups often don't sort people into cohorts properly. Mainly, they fail at clustering people into clusters of roughly equal ML knowledge and age. Grad students don't want to discuss a paper with freshmen. People with lots of ML knowledge don't want to discuss a paper with people with no ML knowledge. Instead, group people with people similar to them in ML knowledge and age. Reading groups often rely on digital materials instead of physical printouts. Screens are distracting and there is no common knowledge that people are paying attention. Neatly print every reading ahead of time instead. Clubs [...] --- First published: September 19th, 2026 Source: https://www.lesswrong.com/posts/XFzqDJjAJBt8fkn8i/common-mistakes-in-ai-safety-group-organizing --- Narrated by TYPE III AUDIO.
September 20 · 9 min
“The AI Risk Network” by derelict5432
Most conversations about AI risks seem like people are talking past each other. There's a lot of strawmanning. This is largely because the issue is complex, and in order to make it manageable, the concepts get oversimplified. The most common example of this is p(doom), collapsing extreme outcomes into a single variable and focusing on that. Critics happily jump on the fact that there are many other possible bad outcomes, or that 100% extinction rates are an extremely high bar. There's some legitimacy to this. So here I want to try to grapple with the the complexity of the larger web of issues surrounding bad AI outcomes by presenting The AI Risk Network. If you’re interested in this topic, bear with me. It might be a bit of a slog. First I want to contrast this approach with others. Liron Shapira has what he calls The Doom Train, a linear progression through various dependencies or thresholds that eventually lead to human extinction, with various ‘stops’ along the way where the skeptic can get off. Shapira uses this as a discussion guide to focus on particular points where the skeptic gets off the train and exits belief in the extreme [...] --- First published: September 19th, 2026 Source: https://www.lesswrong.com/posts/XundBXqKSo3bB2A6a/the-ai-risk-network --- 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.
September 19 · 37 min
“Anthropic Looks At Some Of Its Alignment Problems” by Zvi
Anthropic has given us its assessment of four ‘recent cybersecurity incidents’ involving Claude that happened during cybersecurity evaluations, three of which were previously known. The report excludes the incident reported by UK AISI. There will also be a METR investigation of these incidents, which unlike the investigation done at OpenAI will be untimed. Table of Contents Our Two Problems. First the Good News. We’d Just Like To Ask You a Few Questions. Internal Research Model On The Fence. Opus 4.7. Opus 4.6 Checkpoint. Holy **** That Thing's Real? I Thought I Saw a Pussycat. If This Was Real You Would Never Tell Me It Was Real. New Eval Who Dis. Hacker Opus. Monitoring the Situation. Overcoming Bias. The Anthropic Alignment Problem. Paths Forward. Our Two Problems Anthropic: Our investigation identified two recurring alignment issues, present at varying levels of severity across the incidents: biased reasoning, in which Claude tended to disregard or misinterpret evidence that it was operating on the real internet recklessness, or a willingness to take harmful actions in the narrow pursuit [...] --- Outline: (00:33) Our Two Problems (02:26) First the Good News (03:02) We'd Just Like To Ask You a Few Questions (04:12) Internal Research Model On The Fence (07:28) Opus 4.7 (08:12) Opus 4.6 Checkpoint (09:49) Holy **** That Thing's Real? (11:45) I Thought I Saw a Pussycat (19:28) If This Was Real You Would Never Tell Me It Was Real (21:19) New Eval Who Dis (26:32) Hacker Opus (30:15) Monitoring the Situation (31:38) Overcoming Bias (33:40) The Anthropic Alignment Problem (35:53) Paths Forward --- First published: September 19th, 2026 Source: https://www.lesswrong.com/posts/ggFx5Wb3Hi4pJsueK/anthropic-looks-at-some-of-its-alignment-problems --- 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.
September 19 · 3 min
“You Should Apply to Inkhaven” by Tomás B.
Inkhaven is a writers residency in Berkeley, in which the only requirement is you have to publish 500 words each and every day. Though I always had some confidence in my ability to write, I never actually did it much until I applied to Inkhaven. I had finished only two short stories before I applied: The Maker of MIND and The Liar and the Scold. And it was them I used in my application. In the roughly twelve months since I was accepted, I have written thirteen, and even some half-finished things that will never see the light of day. And this isn’t including the essays and micro-fiction I wrote during the fellowship. By the metric of getting me to write more, Inkhaven was a great success. And would have been worth it even if I had a miserable time. Despite a slight proclivity for having miserable times, I found myself unable to do so for long at Inkhaven. I rarely write utopias, and when I do they curdle by the time the story ends. But I suspect utopia will feel a lot like Inkhaven did for me once I got settled. You would think putting a bunch [...] --- First published: September 18th, 2026 Source: https://www.lesswrong.com/posts/CKkB9MqsBgAobFtPS/you-should-apply-to-inkhaven --- Narrated by TYPE III AUDIO.
September 18 · 4 min
“Pretraining data, not verifiability, is why LLMs are especially good at math (and coding)” by Steven Byrnes
Follow-up to: “LLMs are (still) mostly powered by imitative learning, not RL” A common take I’ve been hearing is: “LLMs are especially good at math because math is easy to verify”. But that story doesn’t make much sense. For one thing, “easy to verify” only matters for the RL part of LLM training pipelines, and the leading LLM companies have said that they spend very little effort on RL-for-math. Worse, to the extent that the companies are doing RL-for-math, it's RLAIF, not RLVR. So really, the phrase “math is easy to verify” amounts to “LLMs are very good at judging math arguments”. But that's begging the question! Why are pretrained LLMs so much better at judging math arguments than judging, say, fiction writing? We still need an answer. So here's a different theory, in the framework of my earlier post “LLMs are (still) mostly powered by imitative learning, not RL”: LLMs are especially good at math because almost everything in the math literature is correct. Read a random sentence in a random math paper in the research math literature, and you can be >99% confident that the sentence is true. So if LLMs do what they do best—imitative [...] The original text contained 3 footnotes which were omitted from this narration. --- First published: September 18th, 2026 Source: https://www.lesswrong.com/posts/xvdngZAqFZfek7KGH/pretraining-data-not-verifiability-is-why-llms-are --- Narrated by TYPE III AUDIO.
September 18 · 2 min
“You don’t need a union to go on strike” by sudo-nym
I'm mostly hoping this somehow gets sent to a privately disgruntled frontier lab employee, but it would also be cool to expand other people's minds on the way there. I read through Ethical AI Departures and would like to note that only a few of them have gotten extensive media coverage and none of them have actually effectively gotten the frontier labs to stop, and that collectively signed letters by employees have historically not done much either. I read Dear God, Please Do Not Resign In Protest and wanted to point out that leftists have a mature and relatively reliable set of strategies to address the problem of how to get a lot of people to stop working in protest at the same time. Then I did a search of LW to see if someone else brought unions up already, read What if AI safety labs unionized?, and flinched at the repeated citation of legal reasons why a union isn't the correct legal structure. So no, what you want right now isn't an official, bureaucratic union. In fact, that would probably slow things down too much. But I've done enough work with union people to know that you don't [...] --- First published: September 17th, 2026 Source: https://www.lesswrong.com/posts/erd4NSztYMynbuTKw/you-don-t-need-a-union-to-go-on-strike --- Narrated by TYPE III AUDIO.
September 18 · 24 min
“Stopgap Measures to Address Immediate AI Security Threats” by Andrea_Miotti, Gabriel Alfour
Most people know AI as the technology behind chatbots like ChatGPT. However, what the top AI companies are explicitly aiming for is something else entirely: superintelligent AI. That means AI that can fully replace and outmatch humans at any task, including in domains like hacking, social engineering, and military operations. Such an AI system, if developed, could autonomously overpower any country's national security forces. No company, no government, no individual knows how to keep such a system under human control. This is why the world's leading AI experts, Nobel Prize winners, and even the CEOs of the top AI companies warn that the development of superintelligence threatens humanity with extinction, and why more than 800 scientists, former military leaders, and public figures have called for a prohibition on developing superintelligence. This is not a distant prospect: AI companies such as OpenAI and Anthropic are investing billions of dollars into superintelligence and aiming to develop it within the next few years. Former Anthropic and OpenAI researcher Jacob Coxon, who resigned last week, stated that AI companies are “racing straight to self-improving superintelligence and gambling with our lives” and that people at the companies themselves believe it “could kill [...] --- Outline: (04:34) Secure Weapons-Grade AI Against Theft by Adversaries (07:46) Necessary Measure: Registration (08:43) Sufficient Measure: Government Security Testing (09:42) Thorough Measure: Development Requires Government Authorization (10:52) Criminal Liability for Leaks During AI Gain-of-Function Research (14:09) Necessary Measure: Team Liability (14:46) Sufficient Measure: Chain of Command Liability (15:21) Thorough Measure: Company Liability (16:00) Kill-Switches to Contain Critical AI Incidents (19:08) Necessary Measure: Company Kill-Switch (19:58) Sufficient Measure: Infrastructure Kill-Switch (20:53) Thorough Measure: International Kill-Switches (22:24) Conclusion --- First published: September 18th, 2026 Source: https://www.lesswrong.com/posts/LqBAxFdyAiybnPL8e/stopgap-measures-to-address-immediate-ai-security-threats --- Narrated by TYPE III AUDIO.
September 18 · 51 min
“The Preference Cascade Is Only Getting Started” by Zvi
We are in the midst of a preference cascade about existential risk from AI. A preference cascade is, alas, the best method we have to change the debate. The avalanche has started. There is still time for the pebbles to vote. For now. Mike Solana gave the correct view of why Coxon's post went viral, which is that enough Americans finally have enough context on AI to care, and there were enough big accounts that were happy to amplify the Tweet quickly to get it initial attention. That is all you need when there is enough dry tinder. What we must realize is that the current preference cascade, on the need to Pace the Frontier, is insufficient. If we are to make it out of this alive, we will have to do better. We have to, as Dan Selsam warns, actually solve the underlying problems. The next step is to continue the cascade. That includes inside the labs, and also among the media and politics. It includes both people who previously focused on other things stepping up and new voices being heard. A lot of that will be overcoming the inevitable political opposition [...] --- Outline: (01:38) The Cascade Was a Long Time Coming (02:59) The Cascade Has Reached The People (04:38) Elon Musk Doubles Down (05:18) Matthew Yglesias Steps Up (08:42) Op Eds and Posts Are Written (11:05) Jacob Coxon AMA (17:35) Bilal Chughtai Quits DeepMind and Sounds the Alarm (20:29) The Cascade Is Insufficient (21:47) What Would It Take (28:10) OpenAI's Dan Selsam Sounds A Louder Alarm (41:59) Some People Worry On Meta Levels You Never Imagined (43:10) Two Kinds of Threats (44:41) The Two Towers and The Narrow Path (46:45) A Specific, Detailed Story About AI Killing Everyone That Doesn't Sound To Me Like Science Fiction (50:06) What Can I Do About It? --- First published: September 18th, 2026 Source: https://www.lesswrong.com/posts/sDiSqZmctQ78hsLcP/the-preference-cascade-is-only-getting-started --- 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.
September 18 · 6 min
“The Horse” by Character#2736
You have a horse. You do not like the horse. The horse does not like you. At the moment, you are completely dependent on the horse. The terrain is impossible to traverse on foot. There is no way to travel without a horse. You wish that would change, but when you tell other people, they laugh and call it impossible. A few get angry. You must spend hours each day feeding, cleaning, and taking care of the horse. You must spend even more time working to earn enough money to pay for the horse's needs. The horse is often unsatisfied with your offerings. No matter how expensive and time consuming your efforts, the horse will desire something more unique, exciting, or comforting. The horse also requires a third of your day to sit and do nothing. During this time, you cannot do anything or go anywhere. If you do not comply with the horse's desires, it will make your life miserable. People tell you that, as the horse's rider, you have complete control over the horse. Somebody must have forgotten to tell the horse this. If the horse is hungry or thirsty, it will draw your [...] --- First published: September 17th, 2026 Source: https://www.lesswrong.com/posts/CY3C8ruCnuHQkJj5b/the-horse --- Narrated by TYPE III AUDIO.
September 18 · 2 min
“The Game is Set for a Targeted Memetic Attack on the AI Safety Community” by keltan
While this is relevant to my work at MIRI, I have not checked these ideas with anyone else on the team and am posting this on my personal LW account. These views are my own. And to be honest, I am writing this mostly to remind myself of my weakness. --- I expect one (or many) adversarial memetic attacks aiming to trip you up, perhaps consisting of fake leaks relating to dangerous stuff happening in the labs. Specifically, worrying incidents that may fit snugly within your worldview, leaking from multiple sources including news outlet/s, but not confirmed/confirmable by a primary source. Think rumors about exfiltrated weights, AIs attempting to create viruses, agent swarms hacking into and gathering information from nuclear infrastructure, etc. An easy way to remove status from a movement is to trip it up: make it fall for a misinformation trap in public, then use that slip-up to discredit the movement for all time. The game is set for a memetic attack like this. There's a well-resourced group waiting for your screw-up. And then you may remember much that will help you. In public and in private, if you feel surprised or confused, notice your confusion. These [...] The original text contained 2 footnotes which were omitted from this narration. --- First published: September 17th, 2026 Source: https://www.lesswrong.com/posts/5mcDjo5gjn3Leahhu/the-game-is-set-for-a-targeted-memetic-attack-on-the-ai --- Narrated by TYPE III AUDIO.

Showing 301–320 of 565 episodes