Given both my personal experience with LLMs and my reading of the role that empirical engagement has historically played in non-paradigmatic research, I tend to advocate for a methodology which incorporates immediate feedback loops with present day deep learning systems over the classical "philosophy -> math -> engineering" deconfusion/agent foundations paradigm.

I'm curious what your read of the history is, here? My impression is that most important paradigm-forming work so far has involved empirical feedback somehow, but often in ways exceedingly dissimilar from/illegible to prevailing scientific and engineering practice.

I have a hard time imagining scientists like e.g. Darwin, Carnot, or Shannon describing their work as depending much on "immediate feedback loops with present day" systems. So I'm curious whether you think PIBBSS would admit researchers like these into your program, were they around and pursuing similar strategies today?

For what it's worth, as someone in basically the position you describe—I struggle to imagine automated alignment working, mostly because of Godzilla-ish concerns—demos like these do not strike me as cruxy. I'm not sure what the cruxes are, exactly, but I'm guessing they're more about things like e.g. relative enthusiasm about prosaic alignment, relative likelihood of sharp left turn-type problems, etc., than about whether early automated demos are likely to work on early systems.

Maybe you want to call these concerns unserious too, but regardless I do think it's worth bearing in mind that early results like these might seem like stronger/more relevant evidence to people whose prior is that scaled-up versions of them would be meaningfully helpful for aligning a superintelligence.

I sympathize with the annoyance, but I think the response from the broader safety crowd (e.g., your Manifold market, substantive critiques and general ill-reception on LessWrong) has actually been pretty healthy overall; I think it's rare that peer review or other forms of community assessment work as well or quickly.

It's not a full conceptual history, but fwiw Boole does give a decent account of his own process and frustrations in the preface and first chapter of his book.

I just meant there are many teams racing to build more agentic models. I agree current ones aren't very agentic, though whether that's because they're meaningfully more like "tools" or just still too stupid to do agency well or something else entirely, feels like an open question to me; I think our language here (like our understanding) remains confused and ill-defined.

I do think current systems are very unlike oracles though, in that they have far more opportunity to exert influence than the prototypical imagined oracle design—e.g., most have I/O with ~any browser (or human) anywhere, people are actively experimenting with hooking them up to robotic effectors, etc.

I liked Thermodynamic Weirdness for similar reasons. It does the best job of books I've found at describing case studies of conceptual progress—i.e., what the initial prevailing conceptualizations were, and how/why scientists realized they could be improved.

It's rare that books describe such processes well, I suspect partly because it's so wildly harder to generate scientific ideas than to understand them, that they tend to strike people as almost blindingly obvious in retrospect. For example, I think it's often pretty difficult for people familiar with evolution to understand why it would have taken Darwin years to realize that organisms that reproduce more influence descendants more, or why it was so hard for thermodynamicists to realize they should demarcate entropy from heat, etc. Weirdness helped make this more intuitive for me, which I appreciate.

(I tentatively think Energy, Force and Matter will end up being my second-favorite conceptual history, but I haven't finished yet so not confident).

This seems like a great activity, thank you for doing/sharing it. I disagree with the claim near the end that this seems better than Stop, and in general felt somewhat alarmed throughout at (what seemed to me like) some conflation/conceptual slippage between arguments that various strategies were tractable, and that they were meaningfully helpful. Even so, I feel happy that the world contains people sharing things like this; props.

I think the latter group is is much smaller. I'm not sure who exactly has most influence over risk evaluation, but the most obvious examples are company leadership and safety staff/red-teamers. From what I hear, even those currently receive equity (which seems corroborated by job listings, e.g. Anthropic, DeepMind, OpenAI).

What seemed psychologizing/unfair to you, Raemon? I think it was probably unnecessarily rude/a mistake to try to summarize Anthropic’s whole RSP in a sentence, given that the inferential distance here is obviously large. But I do think the sentence was fair.

As I understand it, Anthropic’s plan for detecting threats is mostly based on red-teaming (i.e., asking the models to do things to gain evidence about whether they can). But nobody understands the models well enough to check for the actual concerning properties themselves, so red teamers instead check for distant proxies, or properties that seem plausibly like precursors. (E.g., for “ability to search filesystems for passwords” as a partial proxy for “ability to autonomously self-replicate,” since maybe the former is a prerequisite for the latter).

But notice that this activity does not involve directly measuring the concerning behavior. Rather, it instead measures something more like “the amount the model strikes the evaluators as broadly sketchy-seeming/suggestive that it might be capable of doing other bad stuff.” And the RSP’s description of Anthropic’s planned responses to these triggers is so chock full of weasel words and caveats and vague ambiguous language that I think it barely constrains their response at all.

So in practice, I think both Anthropic’s plan for detecting threats, and for deciding how to respond, fundamentally hinge on wildly subjective judgment calls, based on broad, high-level, gestalt-ish impressions of how these systems seem likely to behave. I grant that this process is more involved than the typical thing people describe as a “vibe check,” but I do think it’s basically the same epistemic process, and I expect will generate conclusions around as sound.

My guess is that most don’t do this much in public or on the internet, because it’s absolutely exhausting, and if you say something misremembered or misinterpreted you’re treated as a liar, it’ll be taken out of context either way, and you probably can’t make corrections.  I keep doing it anyway because I occasionally find useful perspectives or insights this way, and think it’s important to share mine.  That said, there’s a loud minority which makes the AI-safety-adjacent community by far the most hostile and least charitable environment I spend any time in, and I fully understand why many of my colleagues might not want to.

My guess is that this seems so stressful mostly because Anthropic’s plan is in fact so hard to defend, due to making little sense. Anthropic is attempting to build a new mind vastly smarter than any human, and as I understand it, plans to ensure this goes well basically by doing periodic vibe checks to see whether their staff feel sketched out yet. I think a plan this shoddy obviously endangers life on Earth, so it seems unsurprising (and good) that people might sometimes strongly object; if Anthropic had more reassuring things to say, I’m guessing it would feel less stressful to try to reassure them.