My main point was that I thought recent progress in LLMs had demonstrated progress at the problem of building such a function, and solving the value identification problem, and that this progress goes beyond the problem of getting an AI to understand or predict human values.

I want to push back on this a bit. I suspect that "demonstrated progress" is doing a lot of work here, and smuggling an assumption that current trends with LLMs will continue and can be extrapolated straightforwardly.

It's true that LLMs have some nice properties for encapsulating fuzzy and complex concepts like human values, but I wouldn't actually want to use any current LLMs as a referent or in a rating system like the one you propose, for obvious reasons.

Maybe future LLMs will retain all the nice properties of current LLMs while also solving various issues with jailbreaking, hallucination, robustness, reasoning about edge cases, etc. but declaring victory already (even on a particular and narrow point about value identification) seems premature to me.

Separately, I think some of the nice properties you list don't actually buy you that much in practice, even if LLM progress does continue straightforwardly. 

A lot of the properties you list follow from the fact that LLMs are pure functions of their input (at least with a temperature of 0).

Functional purity is a very nice property, and traditional software that encapsulates complex logic in pure functions is often easier to reason about, debug, and formally verify vs. software that uses lots of global mutable state and / or interacts with the outside world through a complex I/O interface. But when the function in question is 100s of GB of opaque floats, I think it's a bit of a stretch to call it transparent and legible just because it can be evaluated outside of the IO monad.

Aside from purity, I don't think your point about an LLM being a "particular function" that can be "hooked up to the AI directly" is doing much work - input() (i.e. asking actual humans) seems just as direct and particular as llm(). If you want your AI system to actually do something in the messy real world, you have to break down the nice theoretical boundary and guarantees you get from functional purity somewhere.

More concretely, given your proposed rating system, simply replace any LLM calls with a call that just asks actual humans to rate a world state given some description, and it seems like you get something that is at least as legible and transparent (in an informal sense) as the LLM version. The main advantage with using an LLM here is that you could potentially get lots of such ratings cheaply and quickly. Replay-ability, determinism and the relative ease of interpretability vs. doing neuroscience on the human raters are also nice, but none of these properties are very reassuring or helpful if the ratings themselves aren't all that good. (Also, if you're doing something with such low sample efficiency that you can't just use actual humans, you're probably on the wrong track anyway.)

Economic / atomic takeoff

For specifically discussing the takeoff models in the original Yudkowsky / Christiano discussion, what about:

Economic vs. atomic takeoff

Economic takeoff because Paul's model implies rapid and transformative economic growth prior to the point at which AIs can just take over completely. Whereas Eliezer's model is that rapid economic growth prior to takeover is not particularly necessary - a sufficiently capable AI could act quickly or amass resources while keeping a low profile, such that from the perspective of almost all humanity, takeover is extremely sudden.

Note: "atomic" here doesn't necessarily mean "nanobots" - the goal of the term is to connote that an AI does something physically transformative, e.g. releasing a super virus, hacking / melting all uncontrolled GPUs, constructing a Dyson sphere, etc. A distinguishing feature of Eliezer's model is that those kinds of things could happen prior to the underlying AI capabilities that enable them having more widespread economic effects.

IIUC, both Eliezer and Paul agree that you get atomic takeoff of some kind eventually, so one of the main disagreements between Paul and Eliezer could be framed as their answer to the question: "Will economic takeoff precede atomic takeoff?" (Paul says probably yes, Eliezer says maybe.)

Separately, an issue I have with smooth / gradual vs. sharp / abrupt (the current top-voted terms) is that they've become a bit overloaded and conflated with a bunch of stuff related to recent AI progress, namely scaling laws and incremental / iterative improvements to chatbots and agents. IMO, these aren't actually closely related nor particularly suggestive of Christiano-style takeoff - if anything it seems more like the opposite:

• Scaling laws and the current pace of algorithmic improvement imply that labs can continue improving the underlying cognitive abilities of AI systems faster than those systems can actually be deployed into the world to generate useful economic growth. e.g. o1 is already "PhD level" in many domains, but doesn't seem to be on pace to replace a significant amount of human labor or knowledge work before it is obsoleted by Opus 3.5 or whatever.
• Smooth scaling of underlying cognition doesn't imply smooth takeoff. Predictable, steady improvements on a benchmark via larger models or more compute don't tell you which point on the graph you get something economically or technologically transformative.

I'm curious what you think of Paul's points (2) and (3) here:

• Eliezer often talks about AI systems that are able to easily build nanotech and overpower humans decisively, and describes a vision of a rapidly unfolding doom from a single failure. This is what would happen if you were magically given an extraordinarily powerful AI and then failed to aligned it, but I think it’s very unlikely what will happen in the real world. By the time we have AI systems that can overpower humans decisively with nanotech, we have other AI systems that will either kill humans in more boring ways or else radically advanced the state of human R&D. More generally, the cinematic universe of Eliezer’s stories of doom doesn’t seem to me like it holds together, and I can’t tell if there is a more realistic picture of AI development under the surface.
• One important factor seems to be that Eliezer often imagines scenarios in which AI systems avoid making major technical contributions, or revealing the extent of their capabilities, because they are lying in wait to cause trouble later. But if we are constantly training AI systems to do things that look impressive, then SGD will be aggressively selecting against any AI systems who don’t do impressive-looking stuff. So by the time we have AI systems who can develop molecular nanotech, we will definitely have had systems that did something slightly-less-impressive-looking.

And specifically to what degree you think future AI systems will make "major technical contributions" that are legible to their human overseers before they're powerful enough to take over completely.

You write:

I expect that, shortly after AIs are able to autonomously develop, analyze and code numerical algorithms better than humans, there’s going to be some pretty big (like, multiple OOMs) progress in AI algorithmic efficiency (even ignoring a likely shift in ML/AI paradigm once AIs start doing the AI research). That’s the sort of thing which leads to a relatively discontinuous takeoff.

But how likely do you think it is that these OOM jumps happen before vs. after a decisive loss of control? 

My own take: I think there will probably be enough selection pressure and sophistication in primarily human-driven R&D processes alone to get to uncontrollable AI. Weak AGIs might speed the process along in various ways, but by the time an AI itself can actually drive the research process autonomously (and possibly make discontinuous progress), the AI will already also be capable of escaping or deceiving its operators pretty easily, and deception / escape seems likely to happen first for instrumental reasons.

But my own view isn't based on the difficulty of verification vs. generation, and I'm not specifically skeptical of bureaucracies / delegation. Doing bad / fake R&D that your overseers can't reliably check does seem somewhat easier than doing real / good R&D, but not always, and as a strategy seems like it would usually be dominated by "just escape first and do your own thing".

That sounds like a frustrating dynamic. I think hypothetical dialogues like this can be helpful in resolving disagreements or at least identifying cruxes when fleshed out though.  As someone who has views that are probably more aligned with your interlocutors, I'll try articulating my own views in a way that might steer this conversation down a new path. (Points below are intended to spur discussion rather than win an argument, and are somewhat scattered / half-baked.)

My own view is that the behavior of current LLMs is not much evidence either way about the behavior of future, more powerful AI systems, in part because current LLMs aren't very impressive in a mundane-utility sense.

Current LLMs look to me like they're just barely capable enough to be useful at all - it's not that they "actually do what we want", rather, it's that they're just good enough at following simple instructions when placed in the right setup / context (i.e. carefully human-designed chatbot interfaces, hooked up to the right APIs, outputs monitored and used appropriately, etc.) to be somewhat / sometimes useful for a range of relatively simple tasks.

So the absence of more exotic / dangerous failure modes can be explained mostly as a lack of capabilities, and there's just not that much else to explain or update on once the current capability level is accounted for.

I can sort of imagine possible worlds where current-generation LLMs all stubbornly behave like Sydney Bing, and / or fall into even weirder failure modes that are very resistant to RLHF and the like. But I think it would also be wrong to update much in the other direction in a "stubborn Sydney" world.

Do you mind giving some concrete examples of what you mean by "actually do what we want" that you think are most relevant, and / or what it would have looked like concretely to observe evidence in the other direction?

A somewhat different reason I think current AIs shouldn't be a big update about future AIs is that current AIs lack the ability to bargain realistically. GPT-4 may behaviorally do what the user or developer wants when placed in the right context, but without the ability to bargain in a real way, I don't see much reason to treat this observation very differently from the fact that my washing machine does what I want when I press the right buttons. The novelty of GPT-4 vs. a washing machine is in its generality and how it works internally, not the literal sense in which it does what the user and / or developer wants, which is a common feature of pretty much all useful technology.

I can imagine worlds in which the observation of AI system behavior at roughly similar capability levels to the LLMs we actually have would cause me to update differently and particularly towards your views, but in those worlds the AI systems themselves would look very different.

For example, suppose someone built an AI system with ~GPT-4 level verbal intelligence, but as a natural side effect of something in the architecture, training process, or setup (as opposed to deliberate design by the developers), the system also happened to want resources of some kind (energy, hardware, compute cycles, input tokens, etc.) for itself, and could bargain for or be incentivized by those resources in the way that humans and animals can often be incentivized by money or treats.

In the world we're actually in, you can sometimes get better performance out of GPT-4 at inference time by promising to pay it money or threatening it in various ways, but all of those threats and promises are extremely fake - you couldn't follow through even if you wanted to, and GPT-4 has no way of perceiving your follow-through or lack thereof anyway. In some ways, GPT-4 is much smarter than a dog or a young child, but you can bargain with dogs and children in very real ways, and if you tried to fake out a dog or a child by pretending to give them a treat without following through, they would quickly notice and learn not to trust you.

(I realize there are some ways in which you could analogize various aspects of real AI training processes to bargaining processes, but I would find optimistic analogies between AI training and human child-rearing more compelling in worlds where AI systems at around GPT-4 level were already possible to bargain with or incentivize realistically at runtime, in ways more directly analogous to how we can directly bargain with natural intelligences of roughly comparable level or lower already.)

Zooming out a bit, "not being able to bargain realistically at runtime" is just one of the ways that LLMs appear to be not like known natural intelligence once you look below surface-level behavior. There's a minimum level of niceness / humanlikeness / "do what we want" ability that any system necessarily has to have in order to be useful to humans at all, and for tasks that can be formulated as text completion problems, the minimum amount seems to be something like "follows basic instructions, most of the time". But I have not personally seen a strong argument for why current LLMs have much more than the minimum amount of humanlike-ness / niceness, nor why we should expect future LLMs to have more.

Suppose we think of ourselves as having many different subagents that focus on understanding the world in different ways - e.g. studying different disciplines, using different styles of reasoning, etc. The subagent that thinks about AI from first principles might come to a very strong opinion. But this doesn't mean that the other subagents should fully defer to it (just as having one very confident expert in a room of humans shouldn't cause all the other humans to elect them as the dictator). E.g. maybe there's an economics subagent who will remain skeptical unless the AI arguments can be formulated in ways that are consistent with their knowledge of economics, or the AI subagent can provide evidence that is legible even to those other subagents (e.g. advance predictions).

Do "subagents" in this paragraph refer to different people, or different reasoning modes / perspectives within a single person? (I think it's the latter, since otherwise they would just be "agents" rather than subagents.)

Either way, I think this is a neat way of modeling disagreement and reasoning processes, but for me it leads to a different conclusion on the object-level question of AI doom.

A big part of why I find Eliezer's arguments about AI compelling is that they cohere with my own understanding of diverse subjects (economics, biology, engineering, philosophy, etc.) that are not directly related to AI - my subagents for these fields are convinced and in agreement.

Conversely, I find many of the strongest skeptical arguments about AI doom to be unconvincing precisely because they seem overly reliant on a "current-paradigm ML subagent" that their proponents feel should be dominant, or at least more heavily weighted than I think is justified.

That will push P(doom) lower because most frames from most disciplines, and most styles of reasoning, don't predict doom.

This might be true and useful for getting some kind of initial outside-view estimate, but I think you need some kind of weighting rule to make this work as reasoning strategy even at a meta level. Otherwise, aren't you vulnerable to other people inventing lots of new frames and disciplines? I think the answer in geometric rationality terms is that some subagents will perform poorly and quickly lose their Nash bargaining resources, and then their contribution to future decision-making / conclusion-making will be down-weighted. But I don't think the only way for a subagent to "perform" for the purposes of deciding on a weight is by making externally legible advance predictions.

Maybe a better question than "time to AGI" is time to mundanely transformative AGI. I think a lot of people have a model of the near future in which a lot of current knowledge work (and other work) is fully or almost-fully automated, but at least as of right this moment, that hasn't actually happened yet (despite all the hype).

For example, one of the things current A(G)Is are supposedly strongest at is writing code, but I would still rather hire a (good) junior software developer than rely on currently available AI products for just about any real programming task, and it's not a particularly close call. I do think there's a pretty high likelihood that this will change imminently as products like Devin improve and get more widely deployed, but it seems worth noting (and finding a term for) the fact that this kind of automation so far (mostly) hasn't actually happened yet, aside from certain customer support and copyediting jobs.

I think when someone asks "what is your time to AGI", they're usually asking about when you expect either (a) AI to radically transform the economy and potentially usher in a golden age of prosperity and post-scarcity or (b) the world to end.

And maybe I am misremembering history or confused about what you are referring to, but in my mind, the promise of the "AGI community" has always been (implicitly or explicitly) that if you call something "human-level AGI", it should be able to get you to (a), or at least have a bigger economic and societal impact than currently-deployed AI systems have actually had so far. (Rightly or wrongly, the ballooning stock prices of AI and semiconductor companies seem to be mostly an expectation of earnings and impact from in-development and future products, rather than expected future revenues from wider rollout of any existing products in their current form.)

I actually agree that a lot of reasoning about e.g. the specific pathways by which neural networks trained via SGD will produce consequentialists with catastrophically misaligned goals is often pretty weak and speculative, including in highly-upvoted posts like Without specific countermeasures, the easiest path to transformative AI likely leads to AI takeover.

But to expand on my first comment, when I look around and see any kind of large effect on the world, good or bad (e.g. a moral catastrophe, a successful business, strong optimization around a MacGuffin), I can trace the causality through a path that is invariably well-modeled by applying concepts like expected utility theory (or geometric rationality, if you prefer), consequentialism, deception, Goodharting, maximization, etc. to the humans involved.

I read Humans provide an untapped wealth of evidence about alignment and much of your other writing as disagreeing with the (somewhat vague / general) claim that these concepts are really so fundamental, and that you think wielding them to speculate about future AI systems is privileging the hypothesis or otherwise frequently leads people astray. (Roughly accurate summary of your own views?)

Regardless of how well this describes your actual views or not, I think differing answers to the question of how fundamental this family of concepts is, and what kind of reasoning mistakes people typically make when they apply them to AI, is not really a disagreement about neural networks specifically or even AI generally.

There are a bunch of ways to "win the argument" or just clear up the students' object-level confusion about mechanics:

• Ask them to predict what happens if the experiment is repeated with the stand held more firmly in place.
• Ask them to work the problems in their textbook, using whatever method or theory they prefer. If they get the wrong answer (according to the answer key) for any of them, that suggests opportunities for further experiments (which the professor should take care to set up more carefully).
• Point out the specific place in the original on-paper calculation where the model of the pendulum system was erroneously over-simplified, and show that using a more precise model results in a calculation that agrees with the experimental results. Note that the location of the error is only in the model (and perhaps the students' understanding); the words in the textbook describing the theory itself remain fixed.
• Write a rigid body physics simulator which can model the pendulum system in enough detail to accurately simulate the experimental result for both the case that the stand is held in place and the case that it falls over. Reveal that the source code for the simulator uses only the principles of Newtonian mechanics.
• Ask the students to pass the ITT of a more experienced physicist. (e.g. ask a physicist to make up some standard physics problems with an answer key, and then challenge the students to accurately predict the contents of the answer key, regardless of whether the students themselves believe those answers would make good experimental predictions.)

These options require that the students and professor spend some time and effort to clear up the students' confusion about Newtonian mechanics, which may not be feasible if the lecture is ending soon. But the bigger issue is that clearing up the object-level confusion about physics doesn't necessarily clear up the more fundamental mistakes the students are making about valid reasoning under uncertainty.

I wrote a post recently on Bayesian updating in real life that the students might be interested in, but in short I would say that their biggest mistake is that they don't have a detailed enough understanding of their own hypotheses. Having failed to predict the outcome of their own experiment, they have strong evidence that they themselves do not possess an understanding of any theory of physics in enough mechanistic detail to make accurate predictions. However, strong evidence of their own ignorance is not strong evidence that any particular theory which they don't understand is actually false.

The students should also consider alternatives to the "everyone else throughout history has been rationalizing away problems with Newtonian mechanics" hypothesis. That hypothesis may indeed be one possible valid explanation of the students' own observations given everything else that they (don't) know, but are they willing to write down some odds ratios between that hypothesis and some others they can come up with? Some alternative hypotheses they could consider:

• they are mistaken about what the theory of Newtonian mechanics actually says
• they or their professor made a calculation or modelling error
• their professor is somehow trolling them
• they themselves are trolls inside of a fictional thought experiment 

They probably won't think of the last one on their own (unless the rest of the dialogue gets very weird), which just goes to show how often the true hypothesis lies entirely outside of one's consideration.

(Aside: the last bit of dialog from the students reminds me of the beginner computer programmer whose code isn't working for some unknown-to-them reason, and quickly concludes that it must be the compiler or operating system that is bugged. In real life, sometimes, it really is the compiler. But it's usually not, especially if you're a beginner just getting started with "Hello world". And even if you're more experienced, you probably shouldn't bet on it being the compiler at very large odds, unless you already have a very detailed model of the compiler, the OS, and your own code.)