All of Lukas Finnveden's Comments + Replies

I think (5) also depends on further details.

As you have written it, both the 2023 and 2033 attempt uses similar data and similar compute.

But in my proposed operationalization, "you can get it to do X" is allowed to use a much greater amount of resources ("say, 1% of the pre-training budget") than the test for whether the model is "capable of doing X" ("Say, at most 1000 data points".)

I think that's important:

  • If both the 2023 and the 2033 attempt are really cheap low-effort attempts, then I don't think that the experiment is very relevant for whether "you c
... (read more)
2Rohin Shah11h
I think I agree with all of that (with the caveat that it's been months and I only briefly skimmed the past context, so further thinking is unusually likely to change my mind).

Even just priors on how large effect sizes of interventions are feels like it brings it under 10x unless there are more detailed arguments given for 10x, but I'll give some more specific thoughts below.

Hm, at the scale of "(inter-)national policy", I think you can get quite large effect sizes. I don't know large the effect-sizes of the following are, but I wouldn't be surprised by 10x or greater for:

  • Regulation of nuclear power leading to reduction in nuclear-related harms. (Compared to a very relaxed regulatory regime.)
  • Regulation of pharmaceuticals l
... (read more)
Thanks for calling me out on this. I think you're likely right. I will cross out that line of the comment, and I have updated toward the effect size of strong AI regulation being larger and am less skeptical of the 10x risk reduction, but my independent impression would still be much lower (~1.25x or smth, while before I would have been at ~1.15x). I still think the AI case has some very important differences with the examples provided due to the general complexity of the situation and the potentially enormous difficulty of aligning superhuman AIs and preventing misuse (this is not to imply you disagree, just stating my view).

Are you thinking about exploration hacking, here, or gradient hacking as distinct from exploration hacking?

Instead, ARC explicitly tries to paint the moratorium folks as "extreme".

Are you thinking about this post? I don't see any explicit claims that the moratorium folks are extreme. What passage are you thinking about?

Cool paper!

I'd be keen to see more examples of the paraphrases, if you're able to share. To get a sense of the kind of data that lets the model generalize out of context. (E.g. if it'd be easy to take all 300 paraphrases of some statement (ideally where performance improved) and paste in a google doc and share. Or lmk if this is on github somewhere.)

I'd also be interested in experiments to determine whether the benefit from paraphrases is mostly fueled by the raw diversity, or if it's because examples with certain specific features help a bunch, and those ... (read more)

2Owain Evans3mo
We didn't investigate the specific question of whether it's raw diversity or specific features. In the Grosse et al paper on influence functions, they find that "high influence scores are relatively rare and they cover a large portion of the total influence". This (vaguely) suggests that the top k paraphrases would do most of the work, which is what I would guess. That said, this is really something that should be investigated with more experiments.
4Asa Cooper Stickland3mo
Here's some examples: As to your other point, I would guess that the "top k" instructions repeated would be better than full diversity, for maybe k around 20+, but I'm definitely not very confident about that

Here's a proposed operationalization.

For models that can't gradient hack: The model is "capable of doing X" if it would start doing X upon being fine-tuned to do it using a hypothetical, small finetuning dataset that demonstrated how to do the task. (Say, at most 1000 data points.)

(The hypothetical fine-tuning dataset should be a reasonable dataset constructed by a hypothetical team of human who knows how to do the task but aren't optimizing the dataset hard for ideal gradient updates to this particular model, or anything like that.)

For models that might ... (read more)

2Rohin Shah4mo
Yeah, that seems like a reasonable operationalization of "capable of doing X". So my understanding is that (1), (3), (6) and (7) would not falsify the hypothesis under your operationalization, (5) would falsify it, (2) depends on details, and (4) is kinda ambiguous but I tend to think it would falsify it.

I intend to write a lot more on the potential “brains vs brawns” matchup of humans vs AGI. It’s a topic that has received surprisingly little depth from AI theorists.

I recommend checking out part 2 of Carl Shulman's Lunar Society podcast for content on how AGI could gather power and take over in practice.

Yeah, I also don't feel like it teaches me anything interesting.

Note that B is (0.2,10,−1)-distinguishable in P.

I think this isn't right, because definition 3 requires that sup_s∗ {B_P− (s∗)} ≤ γ.

And for your counterexample, s* = "C" will have B_P-(s*) be 0 (because there's 0 probably of generating "C" in the future). So the sup is at least 0 > -1.

(Note that they've modified the paper, including definition 3, but this comment is written based on the old version.)

3Rohin Shah5mo
You're right, I incorrectly interpreted the sup as an inf, because I thought that they wanted to assume that there exists a prompt creating an adversarial example, rather than saying that every prompt can lead to an adversarial example. I'm still not very compelled by the theorem -- it's saying that if adversarial examples are always possible (the sup condition you mention) and you can always provide evidence for or against adversarial examples (Definition 2) then you can make the adversarial example probable (presumably by continually providing evidence for adversarial examples). I don't really feel like I've learned anything from this theorem.

Are you mainly interested in evaluating deceptive capabilities? I.e., no-holds-barred, can you elicit competent deception (or sub-components of deception) from the model? (Including by eg fine-tuning on data that demonstrates deception or sub-capabilities.)

Or evaluating inductive biases towards deception? I.e. testing whether the model is inclined towards deception in cases when the training data didn't necessarily require deceptive behavior.

(The latter might need to leverage some amount of capability evaluation, to distinguish not being inclined towards deception from not being capable of deception. But I don't think the reverse is true.)

Or if you disagree with that way of cutting up the space.

2Marius Hobbhahn6mo
All of the above but in a specific order.  1. Test if the model has components of deceptive capabilities with lots of handholding with behavioral evals and fine-tuning.  2. Test if the model has more general deceptive capabilities (i.e. not just components) with lots of handholding with behavioral evals and fine-tuning.  3. Do less and less handholding for 1 and 2. See if the model still shows deception.  4. Try to understand the inductive biases for deception, i.e. which training methods lead to more strategic deception. Try to answer questions such as: can we change training data, technique, order of fine-tuning approaches, etc. such that the models are less deceptive?  5. Use 1-4 to reduce the chance of labs deploying deceptive models in the wild. 

I assume that's from looking at the GPT-4 graph. I think the main graph I'd look at for a judgment like this is probably the first graph in the post, without PaLM-2 and GPT-4. Because PaLM-2 is 1-shot and GPT-4 is just 4 instead of 20+ benchmarks.

That suggests 90% is ~1 OOM away and 95% is ~3 OOMs away.

(And since PaLM-2 and GPT-4 seemed roughly on trend in the places where I could check them, probably they wouldn't change that too much.)

Interesting. Based on skimming the paper, my impression is that, to a first approximation, this would look like:

  • Instead of having linear performance on the y-axis, switch to something like log(max_performance - actual_performance). (So that we get a log-log plot.)
  • Then for each series of data points, look for the largest n such that the last n data points are roughly on a line. (I.e. identify the last power law segment.)
  • Then to extrapolate into the future, project that line forward. (I.e. fit a power law to the last power law segment and project it forward.
... (read more)
1Ethan Caballero6mo
We describe how to go about fitting a BNSL to yield best extrapolation in the last paragraph of Appendix Section A.6 "Experimental details of fitting BNSL and determining the number of breaks" of the paper:

I'm curious if anyone made a serious attempt at the shovel-ready math here and/or whether this approach to counterfactuals still looks promising to Abram? (Or anyone else with takes.)

Competence does not seem to aggressively overwhelm other advantages in humans: 


g. One might counter-counter-argue that humans are very similar to one another in capability, so even if intelligence matters much more than other traits, you won’t see that by looking at  the near-identical humans. This does not seem to be true. Often at least, the difference in performance between mediocre human performance and top level human performance is large, relative to the space below, iirc. For instance, in chess, the Elo difference between the best and

... (read more)

Another fairly common argument and motivation at OpenAI in the early days was the risk of "hardware overhang," that slower development of AI would result in building AI with less hardware at a time when they can be more explosively scaled up with massively disruptive consequences. I think that in hindsight this effect seems like it was real, and I would guess that it is larger than the entire positive impact of the additional direct work that would be done by the AI safety community if AI progress had been slower 5 years ago.

Could you clarify this bit? It ... (read more)

One positive consideration is: AI will be built at a time when it is more expensive (slowing later progress). One negative consideration is: there was less time for AI-safety-work-of-5-years-ago. I think that this particular positive consideration is larger than this particular negative consideration, even though other negative considerations are larger still (like less time for growth of AI safety community).

This seems plausible if the environment is a mix of (i) situations where task completion correlates (almost) perfectly with reward, and (ii) situations where reward is very high while task completion is very low. Such as if we found a perfect outer alignment objective, and the only situation in which reward could deviate from the overseer's preferences would be if the AI entirely seized control of the reward.

But it seems less plausible if there are always (small) deviations between reward and any reasonable optimization target that isn't reward (or close e... (read more)

3Steve Byrnes1y
Sure, other things equal. But other things aren’t necessarily equal. For example, regularization could stack the deck in favor of one policy over another, even if the latter has been systematically producing slightly higher reward. There are lots of things like that; the details depend on the exact RL algorithm. In the context of brains, I have discussion and examples in §9.3.3 here.

As the main author of the "Alignment"-appendix of the truthful AI paper, it seems worth clarifying: I totally don't think that "train your AI to be truthful" in itself is a plan for how to tackle any central alignment problems. Quoting from the alignment appendix:

While we’ve argued that scaleable truthfulness would constitute significant progress on alignment (and might provide a solution outright), we don’t mean to suggest that truthfulness will sidestep all difficulties that have been identified by alignment researchers. On the contrary, we expect work o

... (read more)

Here's what the curves look like if you fit them to the PaLM data-points as well as the GPT-3 data-points.

Keep in mind that this is still based on Kaplan scaling laws. The Chinchilla scaling laws would predict faster progress.



(But we wouldn't observe that on these graphs because they weren't trained Chinchilla-style, of course.)

First I gotta say: I thought I knew the art of doing quick-and-dirty calculations, but holy crap, this methodology is quick-and-dirty-ier than I would ever have thought of. I'm impressed.

But I don't think it currently gets to right answer. One salient thing: it doesn't take into account Kaplan's "contradiction". I.e., Kaplan's laws already suggested that once we were using enough FLOP, we would have to scale data faster than we have to do in the short term. So when I made my extrapolations, I used a data-exponent that was larger than the one that's represe... (read more)

but I am surprised that Chinchilla's curves uses an additive term that predicts that loss will never go below 1.69. What happened with the claims that ideal text-prediction performance was like 0.7?

Apples & oranges, you're comparing different units. Comparing token perplexities is hard when the tokens (not to mention datasets) differ. Chinchilla isn't a character-level model but BPEs (well, they say SentencePiece which is more or less BPEs), and BPEs didn't even exist until the past decade so there will be no human estimates which are in BPE units (... (read more)

Ok so I tried running the numbers for the neural net anchor in my bio-anchors guesstimate replica.

Previously the neural network anchor used an exponent (alpha) of normal(0.8, 0.2) (first number is mean, second is standard deviation). I tried changing that to normal(1, 0.1) (smaller uncertainty because 1 is a more natural number, and some other evidence was already pointing towards 1). Also, the model previously said that a 1-trillion parameter model should be trained with 10^normal(11.2, 1.5) data points. I changed that to have a median at 21.2e12 paramete... (read more)

Depends on how you were getting to that +N OOMs number.

If you were looking at my post, or otherwise using the scaling laws to extrapolate how fast AI was improving on benchmarks (or subjective impressiveness), then the chinchilla laws means you should get there sooner. I haven't run the numbers on how much sooner.

If you were looking at Ajeya's neural network anchor (i.e. the one using the Kaplan scaling-laws, not the human-lifetime or evolution anchors), then you should now expect that AGI comes later. That model anchors the number of parameters in AGI to ... (read more)

4Daniel Kokotajlo2y
You calculated things for the neural network brain size anchor; now here's the peformance scaling trend calculation (I think): I took these graphs from the Chinchilla paper and then made them transparent and superimposed them on one another and then made a copy on the right to extend the line. And I drew some other lines to extend them. Eyeballing this graph it looks like whatever performance we could achieve with 10^27 FLOPs under the Kaplan scaling laws, we can now achieve with 10^25 FLOPs. (!!!) This is a big deal if true. Am I reasoning incorrectly here? If this is anywhere close to correct, then the distinction you mention between two methods of getting timelines -- "Assume it happens when we train a brain-sized model compute-optimally" vs. "assume it happens when we get to superhuman performance on this ensemble of benchmarks that we already have GPT trends for" becomes even more exciting and important than I thought! It's like, a huge huge crux, because it basically makes for a 4 OOM difference! EDIT: To be clear, if this is true then I think I should update away from the second method, on the grounds that it predicts we are only about 1 OOM away and that seems implausible.
2Daniel Kokotajlo2y
Cool. Yep, that makes sense. I'd love to see those numbers if you calculate them!

Ok so I tried running the numbers for the neural net anchor in my bio-anchors guesstimate replica.

Previously the neural network anchor used an exponent (alpha) of normal(0.8, 0.2) (first number is mean, second is standard deviation). I tried changing that to normal(1, 0.1) (smaller uncertainty because 1 is a more natural number, and some other evidence was already pointing towards 1). Also, the model previously said that a 1-trillion parameter model should be trained with 10^normal(11.2, 1.5) data points. I changed that to have a median at 21.2e12 paramete... (read more)

In fact, if we think of pseudo-inputs as predicates that constrain X, we can approximate the probability of unacceptable behavior during deployment as[7]
P(C(M,x) | x∼deploy)≈maxα∈XpseudoP(α(x) | x∼deploy)⋅ P(C(M,x) | α(x), x∼deploy) such that, if we can get a good implementation of P, we no longer have to worry as much about carefully constraining Xpseudo, as we can just let P's prior do that work for us.

Where footnote 7 reads:

Note that this approximation is tight if and only if there exists some α∈Xpseudo such that α(x)↔C(M,x)

I think the "if" direction is... (read more)

I'm at like 30% on fast takeoff in the sense of "1 year doubling without preceding 4 year doubling" (a threshold roughly set to break any plausible quantitative historical precedent).

Huh, AI impacts looked at one dataset of GWP (taken from wikipedia, in turn taken from here) and found 2 precedents for "x year doubling without preceding 4x year doubling", roughly during the agricultural evolution. The dataset seems to be a combination of lots of different papers' estimates of human population, plus an assumption of ~constant GWP/capita early in history.

5Paul Christiano2y
Yeah, I think this was wrong. I'm somewhat skeptical of the numbers and suspect future revisions systematically softening those accelerations, but 4x still won't look that crazy. (I don't remember exactly how I chose that number but it probably involved looking at the same time series so wasn't designed to be much more abrupt.)
I agree that i does slightly worse than t on consistency-checks, but i also does better on other regularizers you're (maybe implicitly) using like speed/simplicity, so as long as i doesn't do too much worse it'll still beat out the direct translator.

Any articulable reason for why i just does slightly worse than t? Why would a 2N-node model fix a large majority of disrepancys between an N-node model and a 1e12*N-node model? I'd expect it to just fix a small fraction of them.

I think this rapidly runs into other issues with consistency checks, like the fact
... (read more)
1Mark Xu2y
The high-level reason is that the 1e12N model is not that much better at prediction than the 2N model. You can correct for most of the correlation even with only a vague guess at how different the AI and human probabilities are, and most AI and human probabilities aren't going to be that different in a way that produces a correlation the human finds suspicious. I think that the largest correlations are going to be produced by the places the AI and the human have the biggest differences in probabilities, which are likely also going to be the places where the 2N model has the biggest differences in probabilities, so they should be not that hard to correct. I think it wouldn't be clear that extending the counterexample would be possible, although I suspect it would be. It might require exhibiting more concrete details about how the consistency check would be defeated, which would be interesting. In some sense, maintaining consistency across many inputs is something that you expect to be pretty hard for the human simulator to do because it doesn't know what set of inputs it's being checked for. I would be excited about a consistency check that gave the direct translator minimal expected consistency loss. Note that I would also be interested in basically any concrete proposal for a consistency check that seemed like it was actually workable.

Hypothesis: Maybe you're actually not considering a reporter i that always use an intermediate model; but instead a reporter i' that does translations on hard questions, and just uses the intermediate model on questions where it's confident that the intermediate model understands everything relevant. I see three different possible issues with that idea:

1. To do this, i' needs an efficient way (ie one that doesn't scale with the size of the predictor) to (on at least some inputs) be highly confident that the intermediate model understands everything relevan... (read more)

I don't understand your counterexample in the appendix Details for penalizing inconsistencies across different inputs. You present a cheating strategy that requires the reporter to run and interpret the predictor a bunch of times, which seems plausibly slower than doing honest translation. And then you say you fix this issue with:

But this dependence could be avoided if there was an intermediate model between the predictor’s Bayes net (which we are assuming is very large) and the human’s Bayes net. Errors identified by the intermediate model are likely to b
... (read more)
1Mark Xu2y
I agree that i does slightly worse than t on consistency checks, but i also does better on other regularizers you're (maybe implicitly) using like speed/simplicity, so as long as i doesn't do too much worse it'll still beat out the direct translator. One possible thing you might try is some sort of lexicographic ordering of regularization losses. I think this rapidly runs into other issues with consistency checks, like the fact that the human is going to be systematically wrong about some correlations, so i potentially is more consistent than t.
1Lukas Finnveden2y
Hypothesis: Maybe you're actually not considering a reporter i that always use an intermediate model; but instead a reporter i' that does translations on hard questions, and just uses the intermediate model on questions where it's confident that the intermediate model understands everything relevant. I see three different possible issues with that idea: 1. To do this, i' needs an efficient way (ie one that doesn't scale with the size of the predictor) to (on at least some inputs) be highly confident that the intermediate model understands everything relevant about the situation. I think this is a reasonable "worst-case" assumption, but I'm not sure. If you're using it, I'd be curious to know. 2. Even when the reporter gets inputs that the intermediate model fully understands, it seems like the reporter will run into issues if its sampling-process (for generating distributions) runs into some inputs that the intermediate model doesn't fully understand. (I.e., if i' gets an input for which X1 is true, and then simulates many more random inputs for which X1 is true, and by chance X2 is true for one of them, then the reporter will have to do translation on that sample). Which makes it seem unlikely that i' wouldn't have to do translation at least once per input. 3. If the model is confident that the intermediate model understands everything relevant about the situation, it seems more efficient to return the intermediate model's answer to the question at hand than to loop over it many times, trying to fix correlations. So really we should get a reporter i'' that does translation on the predictor on hard questions and returns an intermediate model's latent knowledge on easy questions. That seems like an ok reporter to get.

It's very easy to construct probability distributions that have earlier timelines, that look more intuitively unconfident, and that have higher entropy than the bio-anchors forecast. You can just take some of the probability mass from the peak around 2050 and redistribute it among earlier years, especially years that are very close to the present, where bioanchors are reasonably confident that AGI is unlikely.

Oh, come on. That is straight-up not how simple continuous toy models of RSI work. Between a neutron multiplication factor of 0.999 and 1.001 there is a very huge gap in output behavior.

Nitpick: I think that particular analogy isn't great.

For nuclear stuff, we have two state variables: amount of fissile material and current number of neutrons flying around. The amount of fissile material determines the "neutron multiplication factor", but it is the number of neutrons that goes crazy, not fissile material. And the current number of neurons doesn't matter f... (read more)

While GPT-4 wouldn't be a lot bigger than GPT-3, Sam Altman did indicate that it'd use a lot more compute. That's consistent with Stack More Layers still working; they might just have found an even better use for compute.

(The increased compute-usage also makes me think that a Paul-esque view would allow for GPT-4 to be a lot more impressive than GPT-3, beyond just modest algorithmic improvements.)

If they've found some way to put a lot more compute into GPT-4 without making the model bigger, that's a very different - and unnerving - development.

and some of my sense here is that if Paul offered a portfolio bet of this kind, I might not take it myself, but EAs who were better at noticing their own surprise might say, "Wait, that's how unpredictable Paul thinks the world is?"

If Eliezer endorses this on reflection, that would seem to suggest that Paul actually has good models about how often trend breaks happen, and that the problem-by-Eliezer's-lights is relatively more about, either:

  • that Paul's long-term predictions do not adequately take into account his good sense of short-term trend breaks.
  • tha
... (read more)

No, the form says that 1=Paul. It's just the first sentence under the spoiler that's wrong.

2Edouard Harris2y
Good catch! I didn't check the form. Yes you are right, the spoiler should say (1=Paul, 9=Eliezer) but the conclusion is the right way round.

Presumably you're referring to this graph. The y-axis looks like the kind of score that ranges between 0 and 1, in which case this looks sort-of like a sigmoid to me, which accelerates when it gets closer to ~50% performance (and decelarates when it gets closer to 100% performance).

If so, we might want to ask whether these tasks are chosen ~randomly (among tasks that are indicative of how useful AI is) or if they're selected for difficulty in some way. In particular, assume that most tasks look sort-of like a sigmoid as they're scaled up (accelerating arou... (read more)

The preliminary results where obtained on a subset of the full benchmark (~90 tasks vs 206 tasks). And there were many changes since then, including scoring changes. Thus, I'm not sure we'll see the same dynamics in the final results. Most likely yes, but maybe not. I agree that the task selection process could create the dynamics that look like the acceleration. A good point.  As I understand, the organizers have accepted almost all submitted tasks (the main rejection reasons were technical - copyright etc). So, it was mostly self-selection, with the bias towards the hardest imaginable text tasks. It seems that for many contributors, the main motivation was something like:  This includes many cognitive tasks that are supposedly human-complete (e.g. understanding of humor, irony, ethics), and the tasks that are probing the model's generality (e.g. playing chess, recognizing images, navigating mazes - all in text). I wonder if the performance dynamics on such tasks will follow the same curve.   The list of of all tasks is available here.
95% of all ML researchers don't think it's a problem, or think it's something we'll solve easily

The 2016 survey of people in AI asked people about the alignment problem as described by Stuart Russell, and 39% said it was an important problem and 33% that it's a harder problem than most other problem in the field.

given realistic treatments of moral uncertainty you should not care too much more about preventing drift than about preventing extinction given drift (e.g. 10x seems very hard to justify to me).

I think you already believe this, but just to clarify: this "extinction" is about the extinction of Earth-originating intelligence, not about humans in particular. So AI alignment is an intervention to prevent drift, not an intervention to prevent extinction. (Though of course, we could care differently about persuasion-tool-induced drift vs unaliged-AI-induced drift.)

Interesting! Here's one way to look at this:

  • EDT+SSA-with-a-minimal-reference-class behaves like UDT in anthropic dilemmas where updatelessness doesn't matter.
  • I think SSA with a minimal reference class is roughly equivalent to "notice that you exist; exclude all possible worlds where you don't exist; renormalize"
  • In large worlds where your observations have sufficient randomness that observers of all kinds exists in all worlds, the SSA update step cannot exclude any world. You're updateless by default. (This is the case in the 99% example above.)
  • In small or
... (read more)

Re your edit: That bit seems roughly correct to me.

If we are in a simulation, SIA doesn't have strong views on late filters for unsimulated reality. (This is my question (B) above.) And since SIA thinks we're almost certainly in a simulation, it's not crazy to say that SIA doesn't have strong view on late filters for unsimulated reality. SIA is very ok with small late filters, as long as we live in a simulation, which SIA says we probably do.

But yeah, it is a little bit confusing, in that we care more about late-filters-in-unsimulated reality if we live in... (read more)

I think it's important to be clear about what SIA says in different situations, here. Consider the following 4 questions:

A) Do we live in a simulation?

B) If we live in a simulation, should we expect basement reality to have a large late filter?

C) If we live in basement reality, should we expect basement reality (ie our world) to have a large late filter?

D) If we live in a simulation, should we expect the simulation (ie our world) to have a large late filter?

In this post, you persuasively argue that SIA answers "yes" to (A) and "not necessarily" to (B). How... (read more)

5Daniel Kokotajlo2y
I disagree that (B) is not decision-relevant and that (C) is. I'm not sure, haven't thought through all this yet, but that's my initial reaction at least.
2Zach Stein-Perlman2y
Ha, I wrote a comment like yours but slightly worse, then refreshed and your comment appeared. So now I'll just add one small note: To the extent that (1) normatively, we care much more about the rest of the universe than our personal lives/futures, and (2) empirically, we believe that our choices are much more consequential if we are non-simulated than if we are simulated, we should in practice act as if there are greater odds that we are non-simulated than we have reason to believe for purely epistemic purposes. So in practice, I'm particularly interested in (C) (and I tentatively buy SIA doomsday as explained by Katja Grace). Edit: also, isn't the last part of this sentence from the post wrong:
(The human baseline is a loss of 0.7 bits, with lots of uncertainty on that figure.)

I'd like to know what this figure is based on. In the linked post, Gwern writes:

The pretraining thesis argues that this can go even further: we can compare this performance directly with humans doing the same objective task, who can achieve closer to 0.7 bits per character⁠.

But in that linked post, there's no mention of "0.7" bits in particular, as far as I or cmd-f can see. The most relevant passage I've read is:

Claude Shannon found that each character was carrying more
... (read more)

It's based on those estimates and the systematic biases in such methods & literatures. Just as you know that psychology and medical effects are always overestimated and can be rounded down by 50% to get a more plausible real world estimate, such information-theoretic methods will always overestimate model performance and underestimate human performance, and are based on various idealizations: they use limited genres and writing styles (formal, omitting informal like slang), don't involve extensive human calibration or training like the models get, don'... (read more)

Thanks, computer-speed deliberation being a lot faster than space-colonisation makes sense. I think any deliberation process that uses biological humans as a crucial input would be a lot slower, though; slow enough that it could well be faster to get started with maximally fast space colonisation. Do you agree with that? (I'm a bit surprised at the claim that colonization takes place over "millenia" at technological maturity; even if the travelling takes millenia, it's not clear to me why launching something maximally-fast – that... (read more)

3Paul Christiano3y
I agree that biological human deliberation is slow enough that it would need to happen late. By "millennia" I mostly meant that traveling is slow (+ the social costs of delay are low, I'm estimating like 1/billionth of value per year of delay). I agree that you can start sending fast-enough-to-be-relevant ships around the singularity rather than decades later. I'd guess the main reason speed matters initially is for grabbing resources from nearby stars under whoever-gets-their-first property rights (but that we probably will move away from that regime before colonizing). I do expect to have strong global coordination prior to space colonization. I don't actually know if you would pause long enough for deliberation amongst biological humans to be relevant. So on reflection I'm not sure how much time you really have as biological humans. In the OP I'm imagining 10+ years (maybe going up to a generation) but that might just not be realistic. Probably my single best guess is that some (many?) people would straggle out over years or decades (in the sense that relevant deliberation for controlling what happens with their endowment would take place with biological humans living on earth), but that before that there would be agreements (reached at high speed) to avoid them taking a huge competitive hit by moving slowly. But my single best guess is not that likely and it seems much more likely that something else will happen (and even that I would conclude that some particular other thing is much more likely if I thought about it more).

I'm curious about how this interacts with space colonisation. The default path of efficient competition would likely lead to maximally fast space-colonisation, to prevent others from grabbing it first. But this would make deliberating together with other humans a lot trickier, since some space ships would go to places where they could never again communicate with each other. For things to turn out ok, I think you either need:

  • to pause before space colonisation.
  • to finish deliberating and bargaining before space colonisation.
  • to equip each space ship with
... (read more)

I think I'm basically optimistic about every option you list.

  • I think space colonization is extremely slow relative to deliberation (at technological maturity I think you probably have something like million-fold speedup over flesh and blood humans, and colonization takes place over decades and millennia rather than years). Deliberation may not be "finished" until the end of the universe, but I think we will e.g. have deliberated enough to make clear agreements about space colonization / to totally obsolete existing thinking / likely to have reached a "gran
... (read more)

Categorising the ways that the strategy-stealing assumption can fail:

  • It is intrinsically easier to gather flexible influence in pursuit of some goals, because
    • 1. It's easier to build AIs to pursue goals that are easy to check.
    • 3. It's easier to build institutions to pursue goals that are easy to check.
    • 9. It's easier to coordinate around simpler goals.
    • plus 4 and 5 insofar as some values require continuously surviving humans to know what to eventually spend resources on, and some don't.
    • plus 6 insofar as humans are otherwise an important part of the strategic e
... (read more)

Starting with amplification as a baseline; am I correct to infer that imitative generalisation only boosts capabilities, and doesn't give you any additional safety properties?

My understanding: After going through the process of finding z, you'll have a z that's probably too large for the human to fully utilise on their own, so you'll want to use amplification or debate to access it (as well as to generally help the human reason). If we didn't have z, we could train an amplification/debate system on D' anyway, while allowing th... (read more)

2Beth Barnes3y
I think the distinction isn't actually super clear, because you can usually trade off capabilities problems and safety problems. I think of it as expanding the range of questions you can get aligned answers to in a reasonable number of steps. If you're just doing IDA/debate, and you try to get your model to give you answers to questions where the model only knows the answer because of updating on a big dataset, you can either keep going through the big dataset when any question of this type comes up (very slow, so capability limitation), or not trust these answers (capability limitation), or just hope they're correct (safety problem). The latter :) I think the only way to get debate to be able to answer all the questions that debate+IG can answer is to include subtrees that are the size of your whole training dataset at arbitrary points in your debate tree, which I think counts as a ridiculous amount of compute

Cool, seems reasonable. Here are some minor responses: (perhaps unwisely, given that we're in a semantics labyrinth)

Evan's footnote-definition doesn't rule out malign priors unless we assume that the real world isn't a simulation

Idk, if the real world is a simulation made by malign simulators, I wouldn't say that an AI accurately predicting the world is falling prey to malign priors. I would probably want my AI to accurately predict the world I'm in even if it's simulated. The simulators control everything that happens a... (read more)

Isn't that exactly the point of the universal prior is misaligned argument? The whole point of the argument is that this abstraction/specification (and related ones) is dangerous.


I guess your title made it sound like you were teaching us something new about prediction (as in, prediction can be outer aligned at optimum) when really you are just arguing that we should change the definition of outer-aligned-at-optimum, and your argument is that the current definition makes outer alignment too hard to achieve

I mean, it's true that I'm ... (read more)

1Daniel Kokotajlo3y
Well, at this point I feel foolish for arguing about semantics. I appreciate your post, and don't have a problem with saying that the malignity problem is an inner alignment problem. (That is zero evidence that it isn't also an outer alignment problem though!) Evan's footnote-definition doesn't rule out malign priors unless we assume that the real world isn't a simulation. We may have good pragmatic reasons to act as if it isn't, but I still think you are changing the definition of outer alignment if you think it assumes we aren't in a simulation. But *shrug* if that's what people want to do, then that's fine I guess, and I'll change my usage to conform with the majority.

Things I believe about what sort of AI we want to build:

  • It would be kind of convenient if we had an AI that could help us do acausal trade. If assuming that it's not in a simulation would preclude an AI from doing acausal trade, that's a bit inconvenient. However, I don't think this matters for the discussion at hand, for reasons I describe in the final array of bullet points below.
  • Even if it did matter, I don't think that the ability to do acausal trade is a deal-breaker. If we had a corrigible, aligned, superintelligent AI that couldn
... (read more)
1Daniel Kokotajlo3y
Thanks, this is helpful. --You might be right that an AI which assumes it isn't in a simulation is OK--but I think it's too early to conclude that yet. We should think more about acausal trade before concluding it's something we can safely ignore, even temporarily. There's a good general heuristic of "Don't make your AI assume things which you think might not be true" and I don't think we have enough reason to violate it yet. --You say Isn't that exactly the point of the universal prior is misaligned argument? The whole point of the argument is that this abstraction/specification (and related ones) is dangerous. So... I guess your title made it sound like you were teaching us something new about prediction (as in, prediction can be outer aligned at optimum) when really you are just arguing that we should change the definition of outer-aligned-at-optimum, and your argument is that the current definition makes outer alignment too hard to achieve? If this is a fair summary of what you are doing, then I retract my objections I guess, and reflect more.
We want to understand the future, based on our knowledge of the past. However, training a neural net on the past might not lead it to generalise well about the future. Instead, we can train a network to be a guide to reasoning about the future, by evaluating its outputs based on how well humans with access to it can reason about the future

I don't think this is right. I've put my proposed modifications in cursive:

We want to understand the future, based on our knowledge of the past. However, training a neural net on the past might not lead it to... (read more)

1Richard Ngo3y
Ooops, yes, this seems correct. I'll edit mine accordingly.

Oops, I actually wasn't trying to discuss whether the action-space was wide enough to take over the world. Turns out concrete examples can be ambiguous too. I was trying to highlight whether the loss function and training method incentivised taking over the world or not.

Instead of an image-classifier, lets take GPT-3, which has a wide enough action-space to take over the world. Lets assume that:

1. GPT-3 is currently being tested on on a validation set which have some correct answers. (I'm fine with "optimal performance" either requiring... (read more)

5Rohin Shah3y
Ah, in hindsight your comment makes more sense. Argh, I don't know, you're positing a setup that breaks the standard ML assumptions and so things get weird. If you have vanilla SGD, I think I agree, but I wouldn't be surprised if that's totally wrong. There are definitely setups where I don't agree, e.g. if you have an outer hyperparameter tuning loop around the SGD, then I think you can get the opposite behavior than what you're claiming (I think this paper shows this in more detail, though it's been edited significantly since I read it). That would still depend on how often you do the hyperparameter tuning, what hyperparameters you're allowed to tune, etc. ---- On the rest of the comment: I feel like the argument you're making is "when the loss function is myopic, the optimal policy ignores long-term consequences and is therefore safe". I do feel better about this calling this "aligned at optimum", if the loss function also incentivizes the AI system to do that which we designed the AI system for. It still feels like the lack of convergent instrumental subgoals is "just because of" the myopia, and that this strategy won't work more generally. ---- Returning to the original claim: I do agree that these setups probably exist, perhaps using the myopia trick in conjunction with the simulated world trick. (I don't think myopia by itself is enough; to have STEM AI enable a pivotal act you presumably need to give the AI system a non-trivial amount of "thinking time".) I think you will still have a pretty rough time trying to define "optimal performance" in a way that doesn't depend on a lot of details of the setup, but at least conceptually I see what you mean. I'm not as convinced that these sorts of setups are really feasible -- they seem to sacrifice a lot of benefits -- but I'm pretty unconfident here.
That is, if you write down a loss function like "do the best possible science", then the literal optimal AI would take over the world and get a lot of compute and robots and experimental labs to do the best science it can do.

I think this would be true for some way to train a STEM AI with some loss functions (especially if it's RL-like, can interact with the real world, etc) but I think that there are some setups where this isn't the case (e.g. things that look more like alphafold). Specifically, I think there exists some setups and so... (read more)

3Rohin Shah3y
Roughly speaking, you can imagine two ways to get safety: 1. Design the output channels so that unsafe actions / plans do not exist 2. Design the AI system so that even though unsafe actions / plans do exist, the AI system doesn't take them. I would rephrase your argument as "there are some types of STEM AI that are safe because of 1, it seems that given some reasonable loss function those AI systems should be said to be outer aligned at optimum". This is also the argument that applies to image classifiers. ---- In the case where point 1 is literally true, I just wouldn't even talk about whether the system is "aligned"; if it doesn't have the possibility of an unsafe action, then whether it is "aligned" feels meaningless to me. (You can of course still say that it is "safe".) Note that in any such situation, there is no inner alignment worry. Even if the model is completely deceptive and wants to kill as many people as possible, by hypothesis we said that unsafe actions / plans do not exist, and the model can't ever succeed at killing people. ---- A counterargument could be "okay, sure, some unsafe action / plan exists by which the AI takes over the world, but that happens only via side channels, not via the expected output channel". I note that in this case, if you include all the channels available to the AI system, then the system is not outer aligned at optimum, because the optimal thing to do is to take over the world and then always feed in inputs to which the outputs are perfectly known leading to zero loss. Presumably what you'd want instead is to say something like "given a model in which the only output channel available to the AI system is ___, the optimal policy that only gets to act through that channel is aligned". But this is basically saying that in the abstract model you've chosen, (1) applies; and again I feel like saying that this system is "aligned" is somehow missing the point of what "aligned" is supposed to mean. As a concrete exam

He's definitely given some money, and I don't think the 990 absence means much. From here:

in 2016, the IRS was still processing OpenAI’s non-profit status, making it impossible for the organization to receive charitable donations. Instead, the Musk Foundation gave $10m to another young charity, [...] The Musk Foundation’s grant accounted for the majority of’s revenue, and almost all of its own funding, when it passed along $10m to OpenAI later that year.

Also, when he quit in 2018, OpenAI wrote "Elon Musk will depart the OpenAI Board but ... (read more)

That's interesting. I did see YC listed as a major funding source, but given Sam Altman's listed loans/donations, I assumed, because YC has little or nothing to do with Musk, that YC's interest was Altman, Paul Graham, or just YC collectively. I hadn't seen anything at all about YC being used as a cutout for Musk. So assuming the Guardian didn't screw up its understanding of the finances there completely (the media is constantly making mistakes in reporting on finances and charities in particular, but this seems pretty detailed and specific and hard to get wrong), I agree that that confirms Musk did donate money to get OA started and it was a meaningful sum. But it still does not seem that Musk donated the majority or even plurality of OA donations, much less the $1b constantly quoted (or any large fraction of the $1b collective pledge, per ESRogs).

This has definitely been productive for me. I've gained useful information, I see some things more clearly, and I've noticed some questions I still need to think a lot more about. Thanks for taking the time, and happy holidays!

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