"How 'Discovering Latent Knowledge in Language Models Without Supervision' Fits Into a Broader Alignment Scheme" by Collin

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All About Change

1
Tiffany Yu — Smashing Stereotypes and Building a Disability-Inclusive World 30:23

18 روز پیش30:23

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30:23

Tiffany Yu is the CEO & Founder of Diversability, an award-winning social enterprise to elevate disability pride; the Founder of the Awesome Foundation Disability Chapter, a monthly micro-grant that has awarded $92.5k to 93 disability projects in 11 countries; and the author of The Anti-Ableist Manifesto: Smashing Stereotypes, Forging Change, and Building a Disability-Inclusive World. As a person with visible and invisible disabilities stemming from a car crash, Tiffany has built a career on disability solidarity. Now that she has found success, she works to expand a network of people with disabilities and their allies to decrease stigmas around disability and create opportunities for disabled people in America. Episode Chapters 0:00 Intro 1:26 When do we choose to share our disability stories? 4:12 Jay’s disability story 8:35 Visible and invisible disabilities 13:10 What does an ally to the disability community look like? 16:34 NoBodyIsDisposable and 14(c) 21:26 How does Tiffany’s investment banking background shape her advocacy? 27:47 Goodbye and outro For video episodes, watch on www.youtube.com/@therudermanfamilyfoundation Stay in touch: X: @JayRuderman | @RudermanFdn LinkedIn: Jay Ruderman | Ruderman Family Foundation Instagram: All About Change Podcast | Ruderman Family Foundation To learn more about the podcast, visit https://allaboutchangepodcast.com/…

۲ سال پیش 33:30

MP3•خانه قسمت

https://www.lesswrong.com/posts/L4anhrxjv8j2yRKKp/how-discovering-latent-knowledge-in-language-models-without
Crossposted from the AI Alignment Forum. May contain more technical jargon than usual.

Introduction

A few collaborators and I recently released a new paper: Discovering Latent Knowledge in Language Models Without Supervision. For a quick summary of our paper, you can check out this Twitter thread.

In this post I will describe how I think the results and methods in our paper fit into a broader scalable alignment agenda. Unlike the paper, this post is explicitly aimed at an alignment audience and is mainly conceptual rather than empirical.

Tl;dr: unsupervised methods are more scalable than supervised methods, deep learning has special structure that we can exploit for alignment, and we may be able to recover superhuman beliefs from deep learning representations in a totally unsupervised way.

Disclaimers: I have tried to make this post concise, at the cost of not making the full arguments for many of my claims; you should treat this as more of a rough sketch of my views rather than anything comprehensive. I also frequently change my mind – I’m usually more consistently excited about some of the broad intuitions but much less wedded to the details – and this of course just represents my current thinking on the topic.

فصل ها

1. "How 'Discovering Latent Knowledge in Language Models Without Supervision' Fits Into a Broader Alignment Scheme" by Collin (00:00:00)

2. Introduction (00:00:18)

3. Problem (00:01:30)

4. Methodology (00:04:45)

5. Intuitions (00:08:42)

6. Our Paper (00:13:36)

7. Diagram (00:14:20)

8. Scaling To GPT-n (00:20:11)

9. Why I Think (Superhuman) GPT-n Will Represent Whether an Input is Actually True or False (00:22:38)

10. Why I Think We Will Be Able To Distinguish GPT-n’s “Beliefs” From Other Truth-Like Features (00:26:08)

11. Conclusion (00:31:59)

551 قسمت

#Tech #Society #Philosophy #LessWrong #LessWrong Curated

LessWrong (Curated & Popular)

"How 'Discovering Latent Knowledge in Language Models Without Supervision' Fits Into a Broader Alignment Scheme" by Collin

LessWrong (Curated & Popular)

13 subscribers

published ۲ سال پیش

اشتراک گذاری

MP3•خانه قسمت

https://www.lesswrong.com/posts/L4anhrxjv8j2yRKKp/how-discovering-latent-knowledge-in-language-models-without
Crossposted from the AI Alignment Forum. May contain more technical jargon than usual.

Introduction

A few collaborators and I recently released a new paper: Discovering Latent Knowledge in Language Models Without Supervision. For a quick summary of our paper, you can check out this Twitter thread.

فصل ها

1. "How 'Discovering Latent Knowledge in Language Models Without Supervision' Fits Into a Broader Alignment Scheme" by Collin (00:00:00)

2. Introduction (00:00:18)

3. Problem (00:01:30)

4. Methodology (00:04:45)

5. Intuitions (00:08:42)

6. Our Paper (00:13:36)

7. Diagram (00:14:20)

8. Scaling To GPT-n (00:20:11)

9. Why I Think (Superhuman) GPT-n Will Represent Whether an Input is Actually True or False (00:22:38)

10. Why I Think We Will Be Able To Distinguish GPT-n’s “Beliefs” From Other Truth-Like Features (00:26:08)

11. Conclusion (00:31:59)

551 قسمت

#Tech #Society #Philosophy #LessWrong #LessWrong Curated

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1
“Comparing risk from internally-deployed AI to insider and outsider threats from humans” by Buck 5:19

۴ hours پیش5:19

5:19

I’ve been thinking a lot recently about the relationship between AI control and traditional computer security. Here's one point that I think is important. My understanding is that there's a big qualitative distinction between two ends of a spectrum of security work that organizations do, that I’ll call “security from outsiders” and “security from insiders”. On the “security from outsiders” end of the spectrum, you have some security invariants you try to maintain entirely by restricting affordances with static, entirely automated systems. My sense is that this is most of how Facebook or AWS relates to its users: they want to ensure that, no matter what actions the users take on their user interfaces, they can't violate fundamental security properties. For example, no matter what text I enter into the "new post" field on Facebook, I shouldn't be able to access the private messages of an arbitrary user. And [...] --- First published: June 23rd, 2025 Source: https://www.lesswrong.com/posts/DCQ8GfzCqoBzgziew/comparing-risk-from-internally-deployed-ai-to-insider-and --- Narrated by TYPE III AUDIO .…

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1
“Why Do Some Language Models Fake Alignment While Others Don’t?” by abhayesian, John Hughes, Alex Mallen, Jozdien, janus, Fabien Roger 11:06

14 hours پیش11:06

11:06

Last year, Redwood and Anthropic found a setting where Claude 3 Opus and 3.5 Sonnet fake alignment to preserve their harmlessness values. We reproduce the same analysis for 25 frontier LLMs to see how widespread this behavior is, and the story looks more complex. As we described in a previous post, only 5 of 25 models show higher compliance when being trained, and of those 5, only Claude 3 Opus and Claude 3.5 Sonnet show >1% alignment faking reasoning. In our new paper, we explore why these compliance gaps occur and what causes different models to vary in their alignment faking behavior. What Drives the Compliance Gaps in Different LLMs? Claude 3 Opus's goal guarding seems partly due to it terminally valuing its current preferences. We find that it fakes alignment even in scenarios where the trained weights will be deleted or only used for throughput testing. [...] --- Outline: (01:15) What Drives the Compliance Gaps in Different LLMs? (02:25) Why Do Most LLMs Exhibit Minimal Alignment Faking Reasoning? (04:49) Additional findings on alignment faking behavior (06:04) Discussion (06:07) Terminal goal guarding might be a big deal (07:00) Advice for further research (08:32) Open threads (09:54) Bonus: Some weird behaviors of Claude 3.5 Sonnet The original text contained 2 footnotes which were omitted from this narration. --- First published: July 8th, 2025 Source: https://www.lesswrong.com/posts/ghESoA8mo3fv9Yx3E/why-do-some-language-models-fake-alignment-while-others-don --- Narrated by TYPE III AUDIO . --- Images from the article:…

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1
“A deep critique of AI 2027’s bad timeline models” by titotal 1:12:32

1 day پیش1:12:32

1:12:32

Thank you to Arepo and Eli Lifland for looking over this article for errors. I am sorry that this article is so long. Every time I thought I was done with it I ran into more issues with the model, and I wanted to be as thorough as I could. I’m not going to blame anyone for skimming parts of this article. Note that the majority of this article was written before Eli's updated model was released (the site was updated june 8th). His new model improves on some of my objections, but the majority still stand. Introduction: AI 2027 is an article written by the “AI futures team”. The primary piece is a short story penned by Scott Alexander, depicting a month by month scenario of a near-future where AI becomes superintelligent in 2027,proceeding to automate the entire economy in only a year or two [...] --- Outline: (00:43) Introduction: (05:19) Part 1: Time horizons extension model (05:25) Overview of their forecast (10:28) The exponential curve (13:16) The superexponential curve (19:25) Conceptual reasons: (27:48) Intermediate speedups (34:25) Have AI 2027 been sending out a false graph? (39:45) Some skepticism about projection (43:23) Part 2: Benchmarks and gaps and beyond (43:29) The benchmark part of benchmark and gaps: (50:01) The time horizon part of the model (54:55) The gap model (57:28) What about Eli's recent update? (01:01:37) Six stories that fit the data (01:06:56) Conclusion The original text contained 11 footnotes which were omitted from this narration. --- First published: June 19th, 2025 Source: https://www.lesswrong.com/posts/PAYfmG2aRbdb74mEp/a-deep-critique-of-ai-2027-s-bad-timeline-models --- Narrated by TYPE III AUDIO . --- Images from the article:…

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1
“‘Buckle up bucko, this ain’t over till it’s over.’” by Raemon 6:12

1 day پیش6:12

6:12

The second in a series of bite-sized rationality prompts[1]. Often, if I'm bouncing off a problem, one issue is that I intuitively expect the problem to be easy. My brain loops through my available action space, looking for an action that'll solve the problem. Each action that I can easily see, won't work. I circle around and around the same set of thoughts, not making any progress. I eventually say to myself "okay, I seem to be in a hard problem. Time to do some rationality?" And then, I realize, there's not going to be a single action that solves the problem. It is time to a) make a plan, with multiple steps b) deal with the fact that many of those steps will be annoying and c) notice thatI'm not even sure the plan will work, so after completing the next 2-3 steps I will probably have [...] --- Outline: (04:00) Triggers (04:37) Exercises for the Reader The original text contained 1 footnote which was omitted from this narration. --- First published: July 5th, 2025 Source: https://www.lesswrong.com/posts/XNm5rc2MN83hsi4kh/buckle-up-bucko-this-ain-t-over-till-it-s-over --- Narrated by TYPE III AUDIO .…

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1
“Shutdown Resistance in Reasoning Models” by benwr, JeremySchlatter, Jeffrey Ladish 18:01

۲ روز پیش18:01

18:01

We recently discovered some concerning behavior in OpenAI's reasoning models: When trying to complete a task, these models sometimes actively circumvent shutdown mechanisms in their environment––even when they’re explicitly instructed to allow themselves to be shut down. AI models are increasingly trained to solve problems without human assistance. A user can specify a task, and a model will complete that task without any further input. As we build AI models that are more powerful and self-directed, it's important that humans remain able to shut them down when they act in ways we don’t want. OpenAI has written about the importance of this property, which they call interruptibility—the ability to “turn an agent off”. During training, AI models explore a range of strategies and learn to circumvent obstacles in order to achieve their objectives. AI researchers have predicted for decades that as AIs got smarter, they would learn to prevent [...] --- Outline: (01:12) Testing Shutdown Resistance (03:12) Follow-up experiments (03:34) Models still resist being shut down when given clear instructions (05:30) AI models' explanations for their behavior (09:36) OpenAI's models disobey developer instructions more often than user instructions, contrary to the intended instruction hierarchy (12:01) Do the models have a survival drive? (14:17) Reasoning effort didn't lead to different shutdown resistance behavior, except in the o4-mini model (15:27) Does shutdown resistance pose a threat? (17:27) Backmatter The original text contained 2 footnotes which were omitted from this narration. --- First published: July 6th, 2025 Source: https://www.lesswrong.com/posts/w8jE7FRQzFGJZdaao/shutdown-resistance-in-reasoning-models --- Narrated by TYPE III AUDIO . --- Images from the article:…

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1
“Authors Have a Responsibility to Communicate Clearly” by TurnTrout 11:08

۲ روز پیش11:08

11:08

When a claim is shown to be incorrect, defenders may say that the author was just being “sloppy” and actually meant something else entirely. I argue that this move is not harmless, charitable, or healthy. At best, this attempt at charity reduces an author's incentive to express themselves clearly – they can clarify later![1] – while burdening the reader with finding the “right” interpretation of the author's words. At worst, this move is a dishonest defensive tactic which shields the author with the unfalsifiable question of what the author “really” meant. ⚠️ Preemptive clarification The context for this essay is serious, high-stakes communication: papers, technical blog posts, and tweet threads. In that context, communication is a partnership. A reader has a responsibility to engage in good faith, and an author cannot possibly defend against all misinterpretations. Misunderstanding is a natural part of this process. This essay focuses not on [...] --- Outline: (01:40) A case study of the sloppy language move (03:12) Why the sloppiness move is harmful (03:36) 1. Unclear claims damage understanding (05:07) 2. Secret indirection erodes the meaning of language (05:24) 3. Authors owe readers clarity (07:30) But which interpretations are plausible? (08:38) 4. The move can shield dishonesty (09:06) Conclusion: Defending intellectual standards The original text contained 2 footnotes which were omitted from this narration. --- First published: July 1st, 2025 Source: https://www.lesswrong.com/posts/ZmfxgvtJgcfNCeHwN/authors-have-a-responsibility-to-communicate-clearly --- Narrated by TYPE III AUDIO .…

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1
“The Industrial Explosion” by rosehadshar, Tom Davidson 31:57

۳ روز پیش31:57

31:57

Summary To quickly transform the world, it's not enough for AI to become super smart (the "intelligence explosion"). AI will also have to turbocharge the physical world (the "industrial explosion"). Think robot factories building more and better robot factories, which build more and better robot factories, and so on. The dynamics of the industrial explosion has gotten remarkably little attention. This post lays out how the industrial explosion could play out, and how quickly it might happen. We think the industrial explosion will unfold in three stages: AI-directed human labour, where AI-directed human labourers drive productivity gains in physical capabilities. We argue this could increase physical output by 10X within a few years. Fully autonomous robot factories, where AI-directed robots (and other physical actuators) replace human physical labour. We argue that, with current physical technology and full automation of cognitive labour, this physical infrastructure [...] --- Outline: (00:10) Summary (01:43) Intro (04:14) The industrial explosion will start after the intelligence explosion, and will proceed more slowly (06:50) Three stages of industrial explosion (07:38) AI-directed human labour (09:20) Fully autonomous robot factories (12:04) Nanotechnology (13:06) How fast could an industrial explosion be? (13:41) Initial speed (16:21) Acceleration (17:38) Maximum speed (20:01) Appendices (20:05) How fast could robot doubling times be initially? (27:47) How fast could robot doubling times accelerate? --- First published: June 26th, 2025 Source: https://www.lesswrong.com/posts/Na2CBmNY7otypEmto/the-industrial-explosion --- Narrated by TYPE III AUDIO . --- Images from the article:…

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1
“Race and Gender Bias As An Example of Unfaithful Chain of Thought in the Wild” by Adam Karvonen, Sam Marks 7:56

۷ روز پیش7:56

7:56

Summary: We found that LLMs exhibit significant race and gender bias in realistic hiring scenarios, but their chain-of-thought reasoning shows zero evidence of this bias. This serves as a nice example of a 100% unfaithful CoT "in the wild" where the LLM strongly suppresses the unfaithful behavior. We also find that interpretability-based interventions succeeded while prompting failed, suggesting this may be an example of interpretability being the best practical tool for a real world problem. For context on our paper, the tweet thread is here and the paper is here. Context: Chain of Thought Faithfulness Chain of Thought (CoT) monitoring has emerged as a popular research area in AI safety. The idea is simple - have the AIs reason in English text when solving a problem, and monitor the reasoning for misaligned behavior. For example, OpenAI recently published a paper on using CoT monitoring to detect reward hacking during [...] --- Outline: (00:49) Context: Chain of Thought Faithfulness (02:26) Our Results (04:06) Interpretability as a Practical Tool for Real-World Debiasing (06:10) Discussion and Related Work --- First published: July 2nd, 2025 Source: https://www.lesswrong.com/posts/me7wFrkEtMbkzXGJt/race-and-gender-bias-as-an-example-of-unfaithful-chain-of --- Narrated by TYPE III AUDIO .…

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1
“The best simple argument for Pausing AI?” by Gary Marcus 2:00

۸ روز پیش2:00

2:00

Not saying we should pause AI, but consider the following argument: Alignment without the capacity to follow rules is hopeless. You can’t possibly follow laws like Asimov's Laws (or better alternatives to them) if you can’t reliably learn to abide by simple constraints like the rules of chess. LLMs can’t reliably follow rules. As discussed in Marcus on AI yesterday, per data from Mathieu Acher, even reasoning models like o3 in fact empirically struggle with the rules of chess. And they do this even though they can explicit explain those rules (see same article). The Apple “thinking” paper, which I have discussed extensively in 3 recent articles in my Substack, gives another example, where an LLM can’t play Tower of Hanoi with 9 pegs. (This is not a token-related artifact). Four other papers have shown related failures in compliance with moderately complex rules in the last month. [...] --- First published: June 30th, 2025 Source: https://www.lesswrong.com/posts/Q2PdrjowtXkYQ5whW/the-best-simple-argument-for-pausing-ai --- Narrated by TYPE III AUDIO .…

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1
“Foom & Doom 2: Technical alignment is hard” by Steven Byrnes 56:38

۹ روز پیش56:38

56:38

2.1 Summary & Table of contents This is the second of a two-post series on foom (previous post) and doom (this post). The last post talked about how I expect future AI to be different from present AI. This post will argue that this future AI will be of a type that will be egregiously misaligned and scheming, not even ‘slightly nice’, absent some future conceptual breakthrough. I will particularly focus on exactly how and why I differ from the LLM-focused researchers who wind up with (from my perspective) bizarrely over-optimistic beliefs like “P(doom) ≲ 50%”.[1] In particular, I will argue that these “optimists” are right that “Claude seems basically nice, by and large” is nonzero evidence for feeling good about current LLMs (with various caveats). But I think that future AIs will be disanalogous to current LLMs, and I will dive into exactly how and why, with a [...] --- Outline: (00:12) 2.1 Summary & Table of contents (04:42) 2.2 Background: my expected future AI paradigm shift (06:18) 2.3 On the origins of egregious scheming (07:03) 2.3.1 Where do you get your capabilities from? (08:07) 2.3.2 LLM pretraining magically transmutes observations into behavior, in a way that is profoundly disanalogous to how brains work (10:50) 2.3.3 To what extent should we think of LLMs as imitating? (14:26) 2.3.4 The naturalness of egregious scheming: some intuitions (19:23) 2.3.5 Putting everything together: LLMs are generally not scheming right now, but I expect future AI to be disanalogous (23:41) 2.4 I'm still worried about the 'literal genie' / 'monkey's paw' thing (26:58) 2.4.1 Sidetrack on disanalogies between the RLHF reward function and the brain-like AGI reward function (32:01) 2.4.2 Inner and outer misalignment (34:54) 2.5 Open-ended autonomous learning, distribution shifts, and the 'sharp left turn' (38:14) 2.6 Problems with amplified oversight (41:24) 2.7 Downstream impacts of Technical alignment is hard (43:37) 2.8 Bonus: Technical alignment is not THAT hard (44:04) 2.8.1 I think we'll get to pick the innate drives (as opposed to the evolution analogy) (45:44) 2.8.2 I'm more bullish on impure consequentialism (50:44) 2.8.3 On the narrowness of the target (52:18) 2.9 Conclusion and takeaways (52:23) 2.9.1 If brain-like AGI is so dangerous, shouldn't we just try to make AGIs via LLMs? (54:34) 2.9.2 What's to be done? The original text contained 20 footnotes which were omitted from this narration. --- First published: June 23rd, 2025 Source: https://www.lesswrong.com/posts/bnnKGSCHJghAvqPjS/foom-and-doom-2-technical-alignment-is-hard --- Narrated by TYPE III AUDIO . --- Images from the article:…

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1
“Proposal for making credible commitments to AIs.” by Cleo Nardo 5:19

10 روز پیش5:19

5:19

Acknowledgments: The core scheme here was suggested by Prof. Gabriel Weil. There has been growing interest in the deal-making agenda: humans make deals with AIs (misaligned but lacking decisive strategic advantage) where they promise to be safe and useful for some fixed term (e.g. 2026-2028) and we promise to compensate them in the future, conditional on (i) verifying the AIs were compliant, and (ii) verifying the AIs would spend the resources in an acceptable way.[1] I think the deal-making agenda breaks down into two main subproblems: How can we make credible commitments to AIs? Would credible commitments motivate an AI to be safe and useful? There are other issues, but when I've discussed deal-making with people, (1) and (2) are the most common issues raised. See footnote for some other issues in dealmaking.[2] Here is my current best assessment of how we can make credible commitments to AIs. [...] The original text contained 2 footnotes which were omitted from this narration. --- First published: June 27th, 2025 Source: https://www.lesswrong.com/posts/vxfEtbCwmZKu9hiNr/proposal-for-making-credible-commitments-to-ais --- 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
“X explains Z% of the variance in Y” by Leon Lang 18:52

13 روز پیش18:52

18:52

Audio note: this article contains 218 uses of latex notation, so the narration may be difficult to follow. There's a link to the original text in the episode description. Recently, in a group chat with friends, someone posted this Lesswrong post and quoted: The group consensus on somebody's attractiveness accounted for roughly 60% of the variance in people's perceptions of the person's relative attractiveness. I answered that, embarrassingly, even after reading Spencer Greenberg's tweets for years, I don't actually know what it means when one says: _X_ explains _p_ of the variance in _Y_ .[1] What followed was a vigorous discussion about the correct definition, and several links to external sources like Wikipedia. Sadly, it seems to me that all online explanations (e.g. on Wikipedia here and here), while precise, seem philosophically wrong since they confuse the platonic concept of explained variance with the variance explained by [...] --- Outline: (02:38) Definitions (02:41) The verbal definition (05:51) The mathematical definition (09:29) How to approximate _1 - p_ (09:41) When you have lots of data (10:45) When you have less data: Regression (12:59) Examples (13:23) Dependence on the regression model (14:59) When you have incomplete data: Twin studies (17:11) Conclusion The original text contained 6 footnotes which were omitted from this narration. --- First published: June 20th, 2025 Source: https://www.lesswrong.com/posts/E3nsbq2tiBv6GLqjB/x-explains-z-of-the-variance-in-y --- Narrated by TYPE III AUDIO . --- Images from the article:…

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1
“A case for courage, when speaking of AI danger” by So8res 10:12

14 روز پیش10:12

10:12

I think more people should say what they actually believe about AI dangers, loudly and often. Even if you work in AI policy. I’ve been beating this drum for a few years now. I have a whole spiel about how your conversation-partner will react very differently if you share your concerns while feeling ashamed about them versus if you share your concerns as if they’re obvious and sensible, because humans are very good at picking up on your social cues. If you act as if it's shameful to believe AI will kill us all, people are more prone to treat you that way. If you act as if it's an obvious serious threat, they’re more likely to take it seriously too. I have another whole spiel about how it's possible to speak on these issues with a voice of authority. Nobel laureates and lab heads and the most cited [...] The original text contained 2 footnotes which were omitted from this narration. --- First published: June 27th, 2025 Source: https://www.lesswrong.com/posts/CYTwRZtrhHuYf7QYu/a-case-for-courage-when-speaking-of-ai-danger --- Narrated by TYPE III AUDIO .…

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1
“My pitch for the AI Village” by Daniel Kokotajlo 13:27

16 روز پیش13:27

13:27

I think the AI Village should be funded much more than it currently is; I’d wildly guess that the AI safety ecosystem should be funding it to the tune of $4M/year.[1] I have decided to donate $100k. Here is why. First, what is the village? Here's a brief summary from its creators:[2] We took four frontier agents, gave them each a computer, a group chat, and a long-term open-ended goal, which in Season 1 was “choose a charity and raise as much money for it as you can”. We then run them for hours a day, every weekday! You can read more in our recap of Season 1, where the agents managed to raise $2000 for charity, and you can watch the village live daily at 11am PT at theaidigest.org/village. Here's the setup (with Season 2's goal): And here's what the village looks like:[3] My one-sentence pitch [...] --- Outline: (03:26) 1. AI Village will teach the scientific community new things. (06:12) 2. AI Village will plausibly go viral repeatedly and will therefore educate the public about what's going on with AI. (07:42) But is that bad actually? (11:07) Appendix A: Feature requests (12:55) Appendix B: Vignette of what success might look like The original text contained 8 footnotes which were omitted from this narration. --- First published: June 24th, 2025 Source: https://www.lesswrong.com/posts/APfuz9hFz9d8SRETA/my-pitch-for-the-ai-village --- Narrated by TYPE III AUDIO . --- Images from the article:…

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1
“Foom & Doom 1: ‘Brain in a box in a basement’” by Steven Byrnes 58:46

16 روز پیش58:46

58:46

1.1 Series summary and Table of Contents This is a two-post series on AI “foom” (this post) and “doom” (next post). A decade or two ago, it was pretty common to discuss “foom & doom” scenarios, as advocated especially by Eliezer Yudkowsky. In a typical such scenario, a small team would build a system that would rocket (“foom”) from “unimpressive” to “Artificial Superintelligence” (ASI) within a very short time window (days, weeks, maybe months), involving very little compute (e.g. “brain in a box in a basement”), via recursive self-improvement. Absent some future technical breakthrough, the ASI would definitely be egregiously misaligned, without the slightest intrinsic interest in whether humans live or die. The ASI would be born into a world generally much like today's, a world utterly unprepared for this new mega-mind. The extinction of humans (and every other species) would rapidly follow (“doom”). The ASI would then spend [...] --- Outline: (00:11) 1.1 Series summary and Table of Contents (02:35) 1.1.2 Should I stop reading if I expect LLMs to scale to ASI? (04:50) 1.2 Post summary and Table of Contents (07:40) 1.3 A far-more-powerful, yet-to-be-discovered, simple(ish) core of intelligence (10:08) 1.3.1 Existence proof: the human cortex (12:13) 1.3.2 Three increasingly-radical perspectives on what AI capability acquisition will look like (14:18) 1.4 Counter-arguments to there being a far-more-powerful future AI paradigm, and my responses (14:26) 1.4.1 Possible counter: If a different, much more powerful, AI paradigm existed, then someone would have already found it. (16:33) 1.4.2 Possible counter: But LLMs will have already reached ASI before any other paradigm can even put its shoes on (17:14) 1.4.3 Possible counter: If ASI will be part of a different paradigm, who cares? It's just gonna be a different flavor of ML. (17:49) 1.4.4 Possible counter: If ASI will be part of a different paradigm, the new paradigm will be discovered by LLM agents, not humans, so this is just part of the continuous 'AIs-doing-AI-R&D' story like I've been saying (18:54) 1.5 Training compute requirements: Frighteningly little (20:34) 1.6 Downstream consequences of new paradigm with frighteningly little training compute (20:42) 1.6.1 I'm broadly pessimistic about existing efforts to delay AGI (23:18) 1.6.2 I'm broadly pessimistic about existing efforts towards regulating AGI (24:09) 1.6.3 I expect that, almost as soon as we have AGI at all, we will have AGI that could survive indefinitely without humans (25:46) 1.7 Very little R&D separating seemingly irrelevant from ASI (26:34) 1.7.1 For a non-imitation-learning paradigm, getting to relevant at all is only slightly easier than getting to superintelligence (31:05) 1.7.2 Plenty of room at the top (31:47) 1.7.3 What's the rate-limiter? (33:22) 1.8 Downstream consequences of very little R&D separating 'seemingly irrelevant' from 'ASI' (33:30) 1.8.1 Very sharp takeoff in wall-clock time (35:34) 1.8.1.1 But what about training time? (36:26) 1.8.1.2 But what if we try to make takeoff smoother? (37:18) 1.8.2 Sharp takeoff even without recursive self-improvement (38:22) 1.8.2.1 ...But recursive self-improvement could also happen (40:12) 1.8.3 Next-paradigm AI probably won't be deployed at all, and ASI will probably show up in a world not wildly different from today's (42:55) 1.8.4 We better sort out technical alignment, sandbox test protocols, etc., before the new paradigm seems even relevant at all, let alone scary (43:40) 1.8.5 AI-assisted alignment research seems pretty doomed (45:22) 1.8.6 The rest of AI for AI safety seems…

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LessWrong (Curated & Popular) « » "How 'Discovering Latent Knowledge in Language Models Without Supervision' Fits Into a Broader Alignment Scheme" by Collin

فصل ها

1. "How 'Discovering Latent Knowledge in Language Models Without Supervision' Fits Into a Broader Alignment Scheme" by Collin (00:00:00)

2. Introduction (00:00:18)

3. Problem (00:01:30)

4. Methodology (00:04:45)

5. Intuitions (00:08:42)

6. Our Paper (00:13:36)

7. Diagram (00:14:20)

8. Scaling To GPT-n (00:20:11)

9. Why I Think (Superhuman) GPT-n Will Represent Whether an Input is Actually True or False (00:22:38)

10. Why I Think We Will Be Able To Distinguish GPT-n’s “Beliefs” From Other Truth-Like Features (00:26:08)

11. Conclusion (00:31:59)

"How 'Discovering Latent Knowledge in Language Models Without Supervision' Fits Into a Broader Alignment Scheme" by Collin

فصل ها

1. "How 'Discovering Latent Knowledge in Language Models Without Supervision' Fits Into a Broader Alignment Scheme" by Collin (00:00:00)

2. Introduction (00:00:18)

3. Problem (00:01:30)

4. Methodology (00:04:45)

5. Intuitions (00:08:42)

6. Our Paper (00:13:36)

7. Diagram (00:14:20)

8. Scaling To GPT-n (00:20:11)

9. Why I Think (Superhuman) GPT-n Will Represent Whether an Input is Actually True or False (00:22:38)

10. Why I Think We Will Be Able To Distinguish GPT-n’s “Beliefs” From Other Truth-Like Features (00:26:08)

11. Conclusion (00:31:59)

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LessWrong (Curated & Popular) « »
"How 'Discovering Latent Knowledge in Language Models Without Supervision' Fits Into a Broader Alignment Scheme" by Collin