Researcher at the Center on Long-Term Risk. All opinions my own.
I think once an AI is extremely good at AI R&D, lots of these skills will transfer to other domains, so it won’t have to be that much more capable to generalize to all domains, especially if trained in environments designed for teaching general skills.
This step, especially, really struck me as under-argued relative to how important it seems to be for the conclusion. This isn't to pick on the authors of AI 2027 in particular. I'm generally confused as to why arguments for an (imminent) intelligence explosion don't say more on this point, as far as I've read. (I'm reminded of this comic.) But I might well have missed something!
arguing against having probabilistic beliefs about events which are unprecedented
Sorry, I'm definitely not saying this. First, in the linked post (see here), I argue that our beliefs should still be probabilistic, just imprecisely so. Second, I'm not drawing a sharp line between "precedented" and "unprecedented." My point is: Intuitions are only as reliable as the mechanisms that generate them. And given the sparsity of feedback loops[1] and unusual complexity here, I don't see why the mechanisms generating AGI/ASI forecasting intuitions would be truth-tracking to a high degree of precision. (Cf. Violet Hour's discussion in Sec. 3 here.)
the level of precedentedness is continous
Right, and that's consistent with my view. I'm saying, roughly, the degree of imprecision (/width of the interval-valued credence) should increase continuously with the depth of unprecedentedness, among other things.
forecasters have successfully done OK at predicting increasingly unprecedented events
As I note here, our direct evidence only tells us (at best) that people can successfully forecast up to some degree of precision, in some domains. How we ought to extrapolate from this to the case of AGI/ASI forecasting is very underdetermined.
On the actual information of interest (i.e. information about AGI/ASI), that is, not just proxies like forecasting progress in weaker or narrower AI.
Despite (2), it's important to try anyways
FWIW this is the step I disagree with, if I understand what you mean by "try". See this post.
Forecasts which include intuitive estimations are commonplace and often useful (see e.g. intelligence analysis, Superforecasting, prediction markets, etc.).
In this context, we're trying to forecast radically unprecedented events, occurring on long subjective time horizons, where we have little reason to expect these intuitive estimates to be honed by empirical feedback. Peer disagreement is also unusually persistent in this domain. So it's not at all obvious to me that, based on superforecasting track records, we can trust that our intuitions pin down these parameters to a sufficient degree of precision.[1] More on this here (this is not a comprehensive argument for my view, tbc; hoping to post something spelling this out more soon-ish!).
As the linked post explains, "high precision" here does not mean "the credible interval for the parameter is narrow". It means that your central/point estimate of the parameter is pinned down to a narrow range, even if you have lots of uncertainty.
Assumptions that highly constrain the model should be first and foremost rather than absent from publicly facing write-ups and only in appendices.
Strongly agree — cf. nostalgebraist's posts making this point on the bio anchors and AI 2027 models. I have the sense this is a pretty fundamental epistemic crux between camps of people making predictions (or suspending judgment!) about AI takeoff.
It sounds like you're viewing the goal of thinking about DT as: "Figure out your object-level intuitions about what to do in specific abstract problem structures. Then, when you encounter concrete problems, you can ask which abstract problem structure the concrete problems correspond to and then act accordingly."
I think that approach has its place. But there's at least another very important (IMO more important) goal of DT: "Figure out your meta-level intuitions about why you should do one thing vs. another, across different abstract problem structures." (Basically figuring out our "non-pragmatic principles" as discussed here.) I don't see how just asking Claude helps with that, if we don't have evidence that Claude's meta-level intuitions match ours. Our object-level verdicts would just get reinforced without probing their justification. Garbage in, garbage out.
often, we don't think of commitment as literally closing off choice -- e.g., it's still a "choice" to keep a promise
For what it's worth, if a "commitment" is of this form, I struggle to see what the motivation for paying in Parfit's hitchhiker would be. The "you want to be the sort of person who pays" argument doesn't do anything for me, because that's answering a different question than "should you choose to pay [insofar as you have a 'choice']?" I worry there's a motte-and-bailey between different notions of "commitment" going on. I'd be curious for your reactions to my thoughts on this here.
Isn't the "you get what you measure" problem a problem for capabilities progress too, not just alignment? I.e.: Some tasks are sufficiently complex (hence hard to evaluate) and lacking in unambiguous ground-truth feedback that, when you turn the ML crank on them, you're not necessarily going to select for actually doing the task well. You'll select for "appearing to do the task well," and it's open question how well this correlates with actually doing the task well. ("Doing the task" here can include something much higher-level, like "being 'generally intelligent'.")
Which isn't to say this problem wouldn't bite especially hard for alignment. Alignment seems harder to verify than lots of things. But this is one reason I'm not fully sold that once you get human-level AI, capabilities progress will get faster.
(I'm hardly an expert on this, so might well have missed existing discourse on & answers to this question.)
No, at some point you "jump all the way" to AGI
I'm confused as to what the actual argument for this is. It seems like you've just kinda asserted it. (I realize in some contexts all you can do is offer an "incredulous stare," but this doesn't seem like the kind of context where that suffices.)
I'm not sure if the argument is supposed to be the stuff you say in the next paragraph (if so, the "Also" is confusing).
I worry there's kind of a definitional drift going on here. I guess Holden doesn't give a super clean definition in the post, but AFAICT these quotes get at the heart of the distinction:
Sequence thinking involves making a decision based on a single model of the world ...
Cluster thinking – generally the more common kind of thinking – involves approaching a decision from multiple perspectives (which might also be called “mental models”), observing which decision would be implied by each perspective, and weighing the perspectives in order to arrive at a final decision. ... [T]he different perspectives are combined by weighing their conclusions against each other, rather than by constructing a single unified model that tries to account for all available information.
"Making a decision based on a single model of the world" vs. "combining different perspectives by weighing their conclusions against each other" seems orthogonal to the failure mode you mention. (Which is a failure to account for a mechanism that the "cluster thinker" here explicitly foresees.) I'm not sure if you're claiming that empirically, people who follow sequence thinking have a track record of this failure mode? If so, I guess I'm just suspicious of that claim and would expect it's grounded mostly in vibes.
My main recommendation would be, "Don't pin down probability distributions that are (significantly) more precise than seems justified." I can't give an exact set of guidelines for what constitutes "more precise than seems justified" (such is life as a bounded agent!). But to a first approximation:
I'm not sure I understand your position, then. Do you endorse imprecise probabilities in principle, but report precise distributions for some illustrative purpose? (If so, I'd worry that's misleading.) My guess is that we're not yet on the same page about what "pin down the parameters to high precision" means.
Agreed! I appreciate your detailed transparency in communicating the structure of the model, even if I disagree about the formal epistemology.
If our beliefs about this domain ought to be significantly imprecise, not just uncertain, then I'd think the more transparent way to communicate your reasoning would be to report an imprecise (yet still quantitative) forecast.
I don't want to overstate this, tbc. I think this intuition is only trustworthy to the extent that I think it's a compression of (i) lots of cached understanding I've gathered from engaging with timelines research, and (ii) conservative-seeming projections of AI progress that pass enough of a sniff test. If I came into this domain with no prior background, just having a vibe of "2060 is way too far off" wouldn't be a sufficient justification, I think.