Filler tokens don't allow for serially deeper cognition than what architectural limits allow

This depends on your definition of serial cognition, under the definitions I like most the serial depth scales logarithmically with the number of tokens. This is because as you increase parallelism (in the sense you use above), that also increases serial depth logarithmically.

The basic intuitions for this are:

• If you imagine mechanistically how you would add N numbers together, it seems like you would need logarithmic depth (where you recursively split it in half, compute the sums of each half, and then add the results together). Note that attention heads compute sums of numbers that scale with the number of tokens.
• There are physical limits to the total amount of local computation that can be done in a given amount of time, due to speed-of-light constraints. So inasmuch as "serial depth" is supposed to capture intuitively what computation you can do with "time", it seems like serial depth should increase as total computation goes up (reflecting the fact that you have to break up the computation into local pieces, as in the addition example above).

Ah, I realized there was something else I should have highlighted. You mention you care about pre-ChatGPT takes towards shorter timelines -- while compute-centric takeoff was published two months after ChatGPT, I expect that the basic argument structure and conclusions were present well before the release of ChatGPT.

While I didn't observe that report in particular, in general Open Phil worldview investigations took > 1 year of serial time and involved a pretty significant and time-consuming "last mile" step where they get a bunch of expert review before publication. (You probably observed this "last mile" step with Joe Carlsmith's report, iirc Nate was one of the expert reviewers for that report.) Also, Tom Davidson's previous publications were in March 2021 and June 2021, so I expect he was working on the topic for some of 2021 and ~all of 2022.

I suppose a sufficiently cynical observer might say "ah, clearly Open Phil was averse to publishing this report that suggests short timelines and intelligence explosions until after the ChatGPT moment". I don't buy it, based on my observations of the worldview investigations team (I realize that it might not have been up to the worldview investigations team, but I still don't buy it).

I guess one legible argument I could make to the cynic would be that on the cynical viewpoint, it should have taken Open Phil a lot longer to realize they should publish the compute-centric takeoff post. Does the cynic really think that, in just two months, a big broken org would be able to:

• Observe that people no longer make faces at shorter timelines
• Have the high-level strategy-setting execs realize that they should change their strategy to publish more shorter timelines stuff
• Communicate this to the broader org
• Have a lower-level person realize that the internal compute-centric takeoff report can now be published when previously it was squashed
• Update the report to give it the level of polish that it observably has
• Get it through the comms bureaucracy that are probably still operating on the past heuristics and haven't figured out what to do in this new world

That's just so incredibly fast for big broken orgs to move.

I think I agree with all of that under the definitions you're using (and I too prefer the bounded rationality version). I think in practice I was using words somewhat differently than you.

(The rest of this comment is at the object level and is mostly for other readers, not for you)

Saying it's "crazy" means it's low probability of being (part of) the right world-description.

The "right" world-description is a very high bar (all models are wrong but some are useful), but if I go with the spirit of what you're saying I think I might not endorse calling bio anchors "crazy" by this definition, I'd say more like "medium" probability of being a generally good framework for thinking about the domain, plus an expectation that lots of the specific details would change with more investigation.

Honestly I didn't have any really precise meaning by "crazy" in my original comment, I was mainly using it as a shorthand to gesture at the fact that the claim is in tension with reductionist intuitions, and also that the legibly written support for the claim is weak in an absolute sense.

Saying it's "the best we have" means it's the clearest model we have--the most fleshed-out hypothesis.

I meant a higher bar than this; more like "the most informative and relevant thing for informing your views on the topic" (beyond extremely basic stuff like observing that humanity can do science at all, or things like reference class priors). Like, I also claim it is better than "query your intuitions about how close we are to AGI, and how fast we are going, to come up with a time until we get to AGI". So it's not just the clearest / most fleshed-out, it's also the one that should move you the most, even including various illegible or intuition-driven arguments. (Obviously scoped only to the arguments I know about; for all I know other people have better arguments that I haven't seen.)

If it were merely the clearest model or most fleshed-out hypothesis, I agree it would usually be a mistake to make a large belief update or take big consequential actions on that basis.

I also want to qualify / explain my statement about it being a crazy argument. The specific part that worries me (and Eliezer, iiuc) is the claim that, at a given point in time, the delta between natural and artificial artifacts will tend to be approximately constant across different domains. This is quite intuition-bending from a mechanistic / reductionist viewpoint, and the current support for it seems very small and fragile (this 8 page doc). However, I can see a path where I would believe in it much more, which would involve things like:

• Pinning down the exact methodology: Which metrics are we making this "approximately constant delta" claim for? It's definitely not arbitrary metrics.
  • The doc has an explanation, but I don't currently feel like I could go and replicate it myself while staying faithful to the original intuitions, so I currently feel like I am deferring to the authors of the doc on how they are choosing their metrics.
  • Once we do this, can we explain how we're applying it in the AI case? Why should we anchor brain size to neural net size, instead of e.g. brain training flops to neural net training flops?
• More precise estimates: Iirc there is a lot of "we used this incredibly heuristic argument to pull out a number, it's probably not off by OOMs so that's fine for our purposes", which I think is reasonable but makes me uneasy.
• Get more data points: Surely there are more artifacts to compare.
• Check for time invariance: Repeat the analysis at different points in time -- do we see a similar "approximately constant gap" if we look at manmade artifacts from (say) 2000 or 1950? Equivalently, this theory predicts that the rate of progress on the chosen metrics should be similar across domains; is that empirically supported? 
• Flesh out a semi-mechanistic theory (that makes this prediction).
  • The argument would be something like: even if you have two very very different optimization procedures (evolution vs human intelligence), as long as they are far from optimality, it is reasonable to model them via a single quantity ("optimization power" / "effective fraction of the search space covered") which is the primary determinant of the performance you get, irrespective of what domain you are in (as long as the search space in the domain is sufficiently large / detailed / complicated). As a result, as long as you focus on metrics that both evolution and humans were optimizing, you should expect the difference in performance on the metric to be primarily a function of the difference in optimization power between evolution and humans-at-that-point-in-time, and to be approximately independent of the domain.
  • Once you have such a theory, check whether the bio anchors application is sensible according to the theory.

I anticipate someone asking the followup "why didn't Open Phil do that, then?" I don't know what Open Phil was thinking, but I don't think I'd have made a very different decision. It's a lot of work, not many people can do it, and many of those people had better things to do, e.g. imo the compute-centric takeoff work was indeed more important and caused bigger updates than I think the work above would have done (and was probably easier to do).

Would you agree that updating / investing "a lot" in an argument that's kind of crazy in some absolute sense, would be an epistemic / strategic mistake, even if that argument is the best available specific argument in a relative sense?

Hmm, maybe? What exactly is the alternative?

Some things that I think would usually be epistemic / strategic mistakes in this situation:

• Directly adopting the resulting distribution
• Not looking into other arguments
• Taking actions that would be significantly negative in "nearby" worlds that the argument suggests are unlikely. (The "nearby" qualifier is to avoid problem-of-induction style issues.)

Some things that I don't think would immediately qualify as epistemic / strategic mistakes (of course they could still be mistakes depending on further details):

• Making a large belief update. Generally it seems plausible that you start with some very inchoate opinions (or in Bayesian terms an uninformed prior) and so any argument that seems to have some binding to reality, even a pretty wild and error-prone one, can still cause a big update. (Related: The First Sample Gives the Most Information.)
  • This is not quite how I felt about bio anchors in 2020 -- I do think there was some binding-to-reality in arguments like "make a wild guess about how far we are, and then extrapolate AI progress out and see when it reaches that point" -- but it is close.
• Taking consequential actions as a result of the argument. Ultimately we are often faced with consequential decisions, and inaction is also a choice. Obviously you should try to find actions that don't depend on the particular axis you're uncertain about (in this case timelines), but sometimes that would still leave significant potential value on the table. In that case you should use the best info available to you.
  • I actually think this is one of the biggest strengths of Open Phil around 2016-2020. At that time, (1) the vast, vast majority of people believed that AGI was a long time away (especially as revealed by their actions, but also via stated beliefs), and (2) AI timelines was an incredibly cursed topic where there were few arguments that had any semblance of binding to reality. Nevertheless, they saw some arguments that had some tiny bit of binding to reality, concluded that there was a non-trivial chance of AGI within 2 decades, and threw a bunch of effort behind acting on that scenario because it would be so important if it happened.
  • So imo they did invest "a lot" in the belief that AGI could be soon (though of course that wasn't based just on bio anchors), and I think this looks like a great call in hindsight.

Some takes:

• To answer the main question, I agree circa 2020 if you had to ascribe beliefs to Open Phil the org it would be something like "~30 year median timelines" and "~10% utter AI ruin". (And I had some visibility on this beyond public info.) I think this throws away tons of detail about actual Open Phil at the time but imo it's a reasonable move to make.
• I don't think Open Phil had a goal of "promoting" those beliefs, and don't recall them doing things that would be reasonably considered to be primarily about "promoting" those beliefs.
  • Obviously they published reports about those topics, but the reason for that is to advance discourse / collective epistemics.
  • Iirc, bio anchors was the only one that said ~30 year median timelines, I believe semi-informative priors was significantly longer and compute-centric takeoff was significantly shorter.
• I don't think these beliefs made much of a difference to their grant making on object level technical research (relative to beliefs of shorter timelines / higher probability of ruin), just because people almost always want to act on shorter timelines / higher ruin probabilities (than 10%) because those worlds are easier to influence. This is similar to Buck's take so see his comment for elaboration.
• I kinda suspect that upon reading the previous bullet you will say "sure, but the researchers will have contorted their beliefs to make themselves palatable to Open Phil".
  • They cared a lot about avoiding this. At one point while I was at CHAI (funded by Open Phil), we were talking with someone from Open Phil and I asked about their belief on something, and they declined to answer because they didn't want their grantees overfitting to their beliefs. I actually found it quite frustrating and tried to argue with them that we were in fact very opinionated and weren't about to simply adopt their beliefs without questioning them.
  • Of course it is possible that researchers conformed to Open Phil's beliefs anyway.
  • Certainly there was a lot of deference from some communities to Open Phil's views here, though I would guess that was driven more by their reputation as good thinkers rather than their ability to direct funding. I think there's a ton of deference to you (Eliezer) in the rationalist community, for similar reasons. Both groups tend to underestimate how much deference they are doing.
• Maybe you are also making some claim about Open Phil's (un)willingness to fund work more focused on comms or influencing social reality towards shorter timelines / higher probabilities of ruin? I don't have any knowledge about that, but mostly I don't recall many shovel-ready opportunities being available in 2020.

On the actual object level beliefs:

• On timelines, my rough take is "nobody knows, it's not particularly important for technical work beyond 'could be soon', it's often actively negative to discuss because it polarizes people and wastes time, so ignore it where possible". But if I'm forced to give an opinion then I'm most influenced by the bio anchors lineage of work and the METR time horizon work.
  • Generally my stance is roughly: Bio anchors / compute-centric timelines is the worst form of timelines prediction, except all those other approaches that have been tried from time to time.
  • I agree that the timelines in the original bio anchors report were too long, but mainly because it failed to capture acceleration caused by AI prior to TAI. This was pointed out by Open Phil, specifically in Tom Davidson's compute-centric takeoff speeds report.
  • Your views are presumably captured by your critique of bio anchors. I don't find it convincing; my feelings about that critique are similar to Scott Alexander's. In the section "Response 4: me", he says: "Given these two assumptions - that natural artifacts usually have efficiencies within a few OOM of artificial ones, and that compute drives progress pretty reliably - I am proud to be able to give Ajeya’s report the coveted honor of “I do not make an update of literally zero upon reading it”."
    • In particular, the primary rejoinder to "why should bio anchors bind to reality; people do things via completely different mechanisms than nature" is "we tried applying this style of reasoning in a few cases where we know the answer, and it seems like it kinda sorta works".
    • (Remember that I only want to defend "worst form of timelines prediction except all the other approaches". I agree this is kind of a crazy argument in some absolute sense.)
  • So in my view Open Phil looks great in retrospect on this question.
• I still have a pretty low probability on AI ruin (by the standards of LessWrong), as I have for a long time, since we haven't gotten much empirical evidence on the matter. So the 10% AI ruin seems fine in retrospect to me.
  • Best source on the reasons for disagreement is Where I agree and disagree with Eliezer (written by Paul but I endorsed almost all of it in a comment).
  • Best source that comes from me is this conversation with AI Impacts, though note it was in 2019 and I wouldn't endorse all of it any more.

The first two paragraphs of my original comment were trying to do this.

(I have the same critique of the first two paragraphs, but thanks for the edit, it helps)

The second is where you summarize my position as being that we need deep scientific understanding or else everyone dies (which I think you can predict is a pretty unlikely position for me in particular to hold).

Fwiw, I am actively surprised that you have a p(doom) < 50%, I can name several lines of evidence in the opposite direction:

• You've previously tried to define alignment based on worst-case focus and scientific approach. This suggests you believe that "marginalist" / "engineering" approaches are ~useless, from which I inferred (incorrectly) that you would have a high p(doom).
  • I still find the conjunction of the two positions you hold pretty weird.
  • I'm a strong believer in logistic success curves for complex situations. If you're in the middle part of a logistic success curve in a complex situation, then there should be many things that can be done to improve the situation, and it seems like "engineering" approaches should work.
  • It's certainly possible to have situations that prevent this. Maybe you have a bimodal distribution, e.g. 70% on "near-guaranteed fine by default" and 30% on "near-guaranteed doom by default". Maybe you think that people have approximately zero ability to tell which things are improvements. Maybe you think we are at the far end of the logistic success curve today, but timelines are long and we'll do the necessary science in time. But these views seem kinda exotic and unlikely to be someone's actual views. (Idk maybe you do believe the second one.)
  • Obviously I had not thought through this in detail when I originally wrote my comment, and my wordless inference was overconfident in hindsight. But I stand by my overall sense that a person who thinks "engineering" approaches are near-useless will likely also have high p(doom) -- not just as a sociological observation, but also as a claim about which positions are consistent with each other.
• In your writing you sometimes seem to take as a background assumption that alignment will be very hard. For example, I recall you critiquing assistance games because (my paraphrase) "that's not what progress on a hard problem looks like". (I failed to dig up the citation though.)
• You're generally taking a strategy that appears to me to be high variance, which people usually justify via high p(doom) / playing to your outs.
• A lot of your writing is similarly flavored to other people who have high p(doom).

In terms of evidence that you have a p(doom) < 50%, I think the main thing that comes to mind is that you argued against Eliezer about this in late 2021, but that was quite a while ago (relative to the evidence above) and I thought you had changed your mind. (Also iirc the stuff you said then was consistent with p(doom) ~ 50%, but it's long enough ago that I could easily be forgetting things.)

However, I'd guess that I'm more willing than you to point to a broad swathe of people pursuing marginalist approaches and claim that they won't reliably improve things.

You could point to ~any reasonable subcommunity within AI safety (or the entire community) and I'd still be on board with the claim that there's at least a 10% chance that will make things worse, which I might summarize as "they won't reliably improve things", so I still feel like this isn't quite capturing the distinction. (I'd include communities focused on "science" in that, but I do agree that they are more likely not to have a negative sign.) So I still feel confused about what exactly your position is.

On reflection, it's not actually about which position is more common. My real objection is that imo it was pretty obvious that something along these lines would be the crux between you and Neel (and the fact that it is a common position is part of why I think it was obvious).

Inasmuch as you are actually trying to have a conversation with Neel or address Neel's argument on its merits, it would be good to be clear that this is the crux. I guess perhaps you might just not care about that and are instead trying to influence readers without engaging with the OP's point of view, in which case fair enough. Personally I would find that distasteful / not in keeping with my norms around collective-epistemics but I do admit it's within LW norms.

(Incidentally, I feel like you still aren't quite pinning down your position -- depending on what you mean by "reliably" I would probably agree with "marginalist approaches don't reliably improve things". I'd also agree with "X doesn't reliably improve things" for almost any interesting value of X.)