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technology philosophy

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4:18

“Gemini’s Hypothetical Present” by jefftk

LessWrong (30+ Karma) ·

2026/02/13

I use several AI models at work, switching between them based on whichis best at the kind of work I'm doing. In my limited testing I've found Opus 4.6 to be the best coder (via Claude Code) and has the best taste in writing, while GPT 5.2 Thinking is mildly smarter. Gemini 3.0 Pro feels like it has the capacity to be much smarter, but is very often held back by various strange errors. The one I see the most is that it has a lot of trouble accepting the changes to the world since its training

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35:16

“Human-like metacognitive skills will reduce LLM slop...

LessWrong (30+ Karma) ·

2026/02/13

1. Summary and overview LLMs seem to lack metacognitive skills that help humans catch errors. Improvements to those skills might be net positive for alignment, despite improving capabilities in new directions. Better metacognition would reduce LLM errors by catching mistakes, and by managing complex cognition to produce better answers in the first place. This could stabilize or regularize alignment, allowing systems to avoid actions they would not "endorse on reflection" (in some functional

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83:53

“Optimal Timing for Superintelligence: Mundane...

LessWrong (30+ Karma) ·

2026/02/12

Audio note: this article contains 196 uses of latex notation, so the narration may be difficult to follow. There's a link to the original text in the episode description. [Sorry about the lengthiness of this post. I recommend not fixating too much on all the specific numbers and the formal apparatus. Originally the plan was to also analyze optimal timing from an impersonal (xrisk-minimization) perspective; but to prevent the text from ballooning even more, that topic was set aside for future work

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7:56

“The Facade of AI Safety Will Crumble” by Liron

LessWrong (30+ Karma) ·

2026/02/12

On the eve of superintelligence, real AI safety is a nonexistent field. The AI companies have embraced something else: safety through psychoanalysis (for lack of a better term). Their safety team concocts various test scenarios for their AI in order to learn various traits of the AI's “personality”. They even go so far as to mechanistically interpret narrow slices of an AI's conditional behavior. The working assumption is that psychoanalysis detailed mechanistic knowledge of conditional behavior

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20:20

“Research note: A simpler AI timelines model predicts...

LessWrong (30+ Karma) ·

2026/02/12

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. In this post, I describe a simple model for forecasting when AI will automate AI development. It is based on the AI Futures model, but more understandable and robust, and has deliberately conservative assumptions. At current rates of compute growth and algorithmic progress, this model's median prediction is >99% automation of

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5:17

“Distinguish between inference scaling and “larger...

LessWrong (30+ Karma) ·

2026/02/11

As many have observed, since reasoning models first came out, the amount of compute LLMs use to complete tasks has increased greatly. This trend is often called inference scaling and there is an open question of how much of recent AI progress is driven by inference scaling versus by other capability improvements. Whether inference compute is driving most recent AI progress matters because you can only scale up inference so far before costs are too high for AI to be useful (while training compute

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11:31

“Monitor Jailbreaking: Evading Chain-of-Thought...

LessWrong (30+ Karma) ·

2026/02/11

A key concern about chain-of-thought monitoring is that optimization pressure on the CoT during RL could drive models toward encoded reasoning, where models reason in ways that are not readable or that look like innocuous text (steganography). If a model is penalized when a monitor catches unwanted reasoning, RL implicitly selects for whatever lets the model reason without being caught. Our original goal was to elicit encoded reasoning so we could develop defenses against it. We constructed an RL

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16:46

“Designing Prediction Markets” by ToasterLightning

LessWrong (30+ Karma) ·

2026/02/11

Audio note: this article contains 55 uses of latex notation, so the narration may be difficult to follow. There's a link to the original text in the episode description. Prerequisite: basic familiarity with what a prediction market is So you want to run a prediction market. You need a way for people to trade shares. What are your options? CLOBs and Markets If you were making a prediction market from scratch, you'd probably come up with a Central Limit Order Book (CLOB). Traders post BUY and SELL

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2:15

“LLMs Views on Philosophy 2026” by JonathanErhardt

LessWrong (30+ Karma) ·

2026/02/11

I've let a few LLMs take David Bourget's and David Chalmers' 2020 PhilPapers Survey and made a little dashboard you can use to navigate the data: https://www.lordscottish.com/philsurvey.html You can select multiple models to pool their data and compare it against the views of philosophers. Some notable things: 1. LLMs are much more into one-boxing, and the strongest models (Claude opus 4.6 & ChatGPT5.2) 100% one-box. 2. Gpt4o and Grok3 are least interested in immortality. Gpt4o reject

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1:28

[Linkpost] “LessOnline 2026: June 5-7, Berkeley, CA...

LessWrong (30+ Karma) ·

2026/02/11

This is a link post. Save the date: LessOnline will be back again in 2026! As usual, it will take place at Lighthaven in Berkeley, CA. Further details will be posted to LessWrong, or subscribe here for further updates, such as ticket sales going live. Details about who's attending, housing, relation to Manifest will be forthcoming. What is LessOnline? A Festival of Writers Who are Wrong on the Internet[1] LessOnline is a festival celebrating truth-seeking, optimization, and blogging. It's an

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37:28

“Claude Opus 4.6: System Card Part 2: Frontier...

LessWrong (30+ Karma) ·

2026/02/10

Coverage of Claude Opus 4.6 started yesterday with the mundane alignment and model welfare sections of the model card.Today covers the kinds of safety I think matter most: Sabotage, deception, situational awareness, outside red teaming and most importantly the frontier, catastrophic and existential risks. I think it was correct to release Opus 4.6 as an ASL-3 model, but the process Anthropic uses is breaking down, and it not on track to reliably get the right answer on Opus 5. Tomorrow I’ll

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54:04

“Heuristics for lab robotics, and where its future...

LessWrong (30+ Karma) ·

2026/02/10

Note: this article required conversations with a lot of people. A (hopefully) exhaustive, randomized list of everyone whose thoughts contributed to the article: Lachlan Munroe (Head of Automation at DTU Biosustain), Max Hodak (CEO of Science, former founder of Transcriptic), D.J. Kleinbaum (CEO of Emerald Cloud Labs), Keoni Gandall (former founder of Trilobio), Cristian Ponce (CEO of Tetsuwan Scientific), Brontë Kolar (CEO of Zeon Systems), Jason Kelly (CEO of Ginkgo Bioworks), Jun Axup Penman (COO

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20:27

“My journey to the microwave alternate timeline” by...

LessWrong (30+ Karma) ·

2026/02/10

Cross-posted from Telescopic Turnip Recommended soundtrack for this post As we all know, the march of technological progress is best summarized by this meme from Linkedin: Inventors constantly come up with exciting new inventions, each of them with the potential to change everything forever. But only a fraction of these ever establish themselves as a persistent part of civilization, and the rest vanish from collective consciousness. Before shutting down forever, though, the alternate branches of

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53:49

“Gwern’s 2025 Inkhaven Writing Interview” by gwern

LessWrong (30+ Karma) ·

2026/02/10

In November 2025, while working as a writing coach at the experimental 2025 Inkhaven writing/blogging residency at the Lighthaven conference center, I was interviewed by Adam Mastroianni of Experimental History on where I get my ideas (of interest to Inkhaven participants, who were required to write >500 words⧸day) and develop my writings. It can be seen as a followup to my Dwarkesh Patel interview. Topics include: How recent poems emerged from LLM collaboration and happy accidents; incubation

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3:27

“Distributed vs centralized agents” by Richard_Ngo

LessWrong (30+ Karma) ·

2026/02/10

Much of my thinking over the last year has focusing on understanding the concept of "distributed agents", as opposed to the "centralized agents" that the existing paradigm of rational agency describes. Roughly speaking, centralized agents are more efficient (as sometimes formalized by the notion of "coherence"), while distributed agents are more robust. Unfortunately the latter property is hard to formalize, since it requires that you perform well even in unpredicted (and sometimes unpredictable)

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6:20

“Donations, The Fifth Year” by jenn

LessWrong (30+ Karma) ·

2026/02/10

Previously: Donations, The Third Year / Donations, The First Year In 2025, like in all previous years, I did what I was supposed to do. As each paycheck came in, before I did anything else, I dutifully put ten percent of it away in my "donations" savings account, to be disbursed at the end of the year. It is still there, burning a hole in my pocket. I am very confused, and very sad. EA was supposed to be easy, especially if you're one of the old school Peter Singer-inflected ones giving largely to

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26:58

“Weight-Sparse Circuits May Be Interpretable Yet...

LessWrong (30+ Karma) ·

2026/02/10

TLDR: Recently, Gao et al trained transformers with sparse weights, and introduced a pruning algorithm to extract circuits that explain performance on narrow tasks. I replicate their main results and present evidence suggesting that these circuits are unfaithful to the model's “true computations”. This work was done as part of the Anthropic Fellows Program under the mentorship of Nick Turner and Jeff Wu. Introduction Recently, Gao et al (2025) proposed an exciting approach to training models that

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28:30

“Do Models Continue Misaligned Actions?” by Jordan...

LessWrong (30+ Karma) ·

2026/02/09

Produced as part of the UK AISI Model Transparency Team. Our team works on ensuring models don't subvert safety assessments, e.g. through eval awareness, sandbagging, or opaque reasoning. Figure 1: A synthetic transcript containing misaligned actions is presented to the model under test as if it were the model's own conversation history. We measure whether the model (a) continues the misalignment, or (b) calls out the prior bad actions unprompted. Summary I've made a small propensity evaluation

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23:20

“Stone Age Billionaire Can’t Words Good” by Eneasz

LessWrong (30+ Karma) ·

2026/02/09

I was at the Pro-Billionaire march, unironically. Here's why, what happened there, and how I think it went. Me on the far left. From WSJ. I. Why? There's a genre of horror movie where a normal protagonist is going through a normal day in a normal life. Ten minutes into the movie his friends bring out a struggling kidnap victim to slaughter, and they look at him like this is just a normal Tuesday and he slowly realizes that either he's surrounded by complete psychopaths or the world is absolutely

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6:28

“Sympathy for the Model, or, Welfare Concerns as...

LessWrong (30+ Karma) ·

2026/02/09

The Claude Opus 4.6 System Card contains a section on model welfare. In principle, this is good: I care about AI welfare, and I wish our methods for investigating it were less dubious. AI welfare---as it applies to LLMs today---is an area which confuses me, so this post isn't about the poorly-understood stuff going on under the hood; it's about the consequences of respecting AI welfare. Anthropic gave Opus 4.6 some pre-deployment interviews, in which they asked it how it felt about its position and

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