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Positive visions for AI
I was wondering the same thing as I originally read this post on Beren’s blog, where it still says this. I think it’s pretty clearly a mistake, and seems to have been fixed in the LW post since your comment.
I raise other confusions about the maths in my comment here.
I was very happy to find this post—it clarifies & names a concept I’ve been thinking about for a long time. However, I have confusions about the maths here:
Mathematically, direct optimization is your standard AIXI-like optimization process. For instance, suppose we are doing direct variational inference optimization to find a Bayesian posterior parameter from a data-point , the mathematical representation of this is:
By contrast, the amortized objective optimizes some other set of parameters $\phi$ over a function approximator which directly maps from the data-point to an estimate of the posterior parameters We then optimize the parameters of the function approximator across a whole dataset of data-point and parameter examples.
First of all, I don’t see how the given equation for direct optimization makes sense. is comparing a distribution over over a joint distribution over . Should this be for variational inference (where is whatever we’re using to parametrize the variational family), and in general?
Secondly, why the focus on variational inference for defining direct optimization in the first place? Direct optimization is introduced as (emphasis mine):
Direct optimization occurs when optimization power is applied immediately and directly when engaged with a new situation to explicitly compute an on-the-fly optimal response – for instance, when directly optimizing against some kind of reward function. The classic example of this is planning and Monte-Carlo-Tree-Search (MCTS) algorithms [...]
This does not sound like we’re talking about algorithms that update parameters. If I had to put the above in maths, it just sounds like an argmin:
where is your AI system, is whatever action space it can explore (you can make vary based on how much compute you’re wiling to spend, like with MCTS depth), is some loss function (it could be a reward function with a flipped sign, but I’m trying to keep it comparable to the direct optimization equation.
Also, the amortized optimization equation RHS is about defining a , i.e. the parameters in your function approximator , but then the LHS calls it , which is confusing to me. I also don’t understand why the loss function is taking in parameters , or why the dataset contains parameters (is being used throughout to stand for outputs rather than model parameters?).
To me, the natural way to phrase this concept would instead be as
where is your AI system, and , with the dataset .
I’d be curious to hear any expansion of the motivation behind the exact maths in the post, or any way in which my version is misleading.
For the output control task, we graded models as correct if they were within a certain total variation distance of the target distribution. Half the samples had a requirement of being within 10%, the other of being within 20%. This gets us a binary success (0 or 1) from each sample.
Since models practically never got points from the full task, half the samples were also an easier version, testing only their ability to hit the target distribution when they’re already given the two words (rather than the full task, where they have to both decide the two words themselves, and match the specified distribution).
Did you explain to GPT-4 what temperature is? GPT-4, especially before November, knew very little about LLMs due to training data cut-offs (e.g. the pre-November GPT-4 didn’t even know that the acronym “LLM” stood for “Large Language Model”).
Either way, it’s interesting that there is a signal. This feels similar in spirit to the self-recognition tasks in SAD (since in both cases the model has to pick up on subtle cues in the text to make some inference about the AI that generated it).
We thought about this quite a lot, and decided to make the dataset almost entirely public.
It’s not clear to us who would monomaniacally try to maximise SAD score. It’s a dangerous capabilities eval. What we were more worried about is people training for low SAD score in order to make their model seem safer, and such training maybe overfitting to the benchmark and not reducing actual situational awareness by as much as claimed.
It’s also unclear what the sharing policy that we could enforce would be that mitigates these concerns while allowing benefits. For example, we would want top labs to use SAD to measure SA in their models (a lot of the theory of change runs through this). But then we’re already giving the benchmark to the top labs, and they’re the ones doing most of the capabilities work.
More generally, if we don’t have good evals, we are flying blind and don’t know what the LLMs can do. If the cost of having a good understanding of dangerous model capabilities and their prerequisites is that, in theory, someone might be slightly helped in giving models a specific capability (especially when that capability is both emerging by default already, and where there are very limited reasons for anyone to specifically want to boost this ability), then I’m happy to pay that cost. This is especially the case since SAD lets you measure a cluster of dangerous capability prerequisites and therefore for example test things like out-of-context reasoning, unlearning techniques, or activation steering techniques on something that is directly relevant for safety.
Another concern we’ve had is the dataset leaking onto the public internet and being accidentally used in training data. We’ve taken many steps to mitigate this happening. We’ve also kept 20% of the SAD-influence task private, which will hopefully let us detect at least obvious forms of memorisation of SAD (whether through dataset leakage or deliberate fine-tuning).
Me, Myself, and AI: the Situational Awareness Dataset (SAD) for LLMs
I agree that building-based methods (startups) are possibly neglected compared to research-based approaches. I’m therefore exploring some things in this space; you can contact me here
One alternative method to liability for the AI companies is strong liability for companies using AI systems. This does not directly address risks from frontier labs having dangerous AIs in-house, but helps with risks from AI system deployment in the real world. It indirectly affects labs, because they want to sell their AIs.
A lot of this is the default. For example, Air Canada recently lost a court case after claiming a chatbot promising a refund wasn’t binding on them. However, there could be related opportunities. Companies using AI systems currently don’t have particularly good ways to assess risks from AI deployment, and if models continue getting more capable while reliability continues lagging, they are likely to be willing to pay an increasing amount for ways to get information on concrete risks, guard against it, or derisk it (e.g. through insurance against their deployed AI systems causing harms). I can imagine a service that sells AI-using companies insurance against certain types of deployment risk, that could also double as a consultancy / incentive-provider for lower-risk deployments. I’d be interested to chat if anyone is thinking along similar lines.
Start from the intuition that deception in a system is a property of the person being deceived more than it is the deceiver. It follows pretty naturally that deception is better viewed as a property of the composite system that is the agent and its environment.
The first part here feels unfair to the deceived. The second part seems like a property of successful deception, which depends crucially on the environment in addition to the AI. But this seems like too high a bar; successful deception of us, by definition, is not noticed, so if we ever notice deception it can’t have been successful. I care less about whether deception will succeed and more about whether the AI will try to be deceptive in the first place. The core intuition is that if we have the latter, I assume we’ll eventually get the former through better models (though I think there’s a decent chance that control works for a long time, and there you care specifically about whether complex environment interactions lead to deception succeeding or not, but I don’t think that’s what you mean?).
The thing that seems close to this and correct, and that I think you maybe mean, is something like: deception arises in an AI if (NB: “if”, not “if and only if”) (1) the AI system has some goal G, (2) the environment is such that deceiving the humans is a good strategy for achieving G, and (3) there are no limits in the AI that prevent it from finding and executing that strategy (e.g. the architecture is expressive enough, the inductive biases don’t massively reduce the probability of that strategy, or RLHFed constraints against being bad aren’t enough). And here, (2) is of course about the environment. But to see whether this argument goes through, it doesn’t seem like we need to care all that much about the real-world environment (as opposed to toy settings), because “does the real world incentivize deception” seems much less cruxy than (1) or (3).
So my (weakly held) claim is that you can study whether deception emerges in sufficiently simple environments that the environment complexity isn’t a core problem. This will not let you determine whether a particular output in a complicated environment is part of a deceptive plan, but it should be fairly good evidence of whether or not deception is a problem at all.
(Also: do you mean a literal complexity class or something more informal? I assume the latter, and in that case I think it’s better to not overload the term.)
1a) I got the impression that the post emphasises upper bounds more than existing proofs from the introduction, which has a long paragraph on the upper bound problem, and from reading the other comments. The rest of the post doesn’t really bear this emphasis out though, so I think this is a misunderstanding on my part.
1b) I agree we should try to be able to make claims like “the model will never X”. But if models are genuinely dangerous, by default I expect a good chance that teams of smart red-teamers and eval people (e.g. Apollo) to be able to unearth scary demos. And the main thing we care about is that danger leads to an appropriate response. So it’s not clear to me that effective policy (or science) requires being able to say “the model will never X”.
1c) The basic point is that a lot of the safety cases we have for existing products rely less on the product not doing bad things across a huge range of conditions, but on us being able to bound the set of environments where we need the product to do well. E.g. you never put the airplane wing outside its temperature range, or submerge it in water, or whatever. Analogously, for AI systems, if we can’t guarantee they won’t do bad things if X, we can work to not put them in situation X.
2a) Partly I was expecting the post to be more about the science and less about the field-building. But field-building is important to talk about and I think the post does a good job of talking about it (and the things you say about science are good too, just that I’d emphasise slightly different parts and mention prediction as the fundamental goal).
2b) I said the post could be read in a way that produces this feeling; I know this is not your intention. This is related to my slight hesitation around not emphasising the science over the field-building. What standards etc. are possible in a field is downstream of what the objects of study turn out to be like. I think comparing to engineering safety practices in other fields is a useful intuition pump and inspiration, but I sometimes worry that this could lead to trying to imitate those, over following the key scientific questions wherever they lead and then seeing what you can do. But again, I was assuming a post focused on the science (rather than being equally concerned with field-building), and responding with things I feel are missing if the focus had been the science.
3) It is true that optimisation requires computation, and that for your purposes, FLOPS is the right thing to care about because e.g. if doing something bad takes 1e25 FLOPS, the number of actors who can do it is small. However, I think compute should be called, well, “compute”. To me, “optimisation power” sounds like a more fundamental/math-y concept, like how many bits of selection can some idealised optimiser apply to a search space, or whatever formalisation of optimisation you have. I admit that “optimisation power” is often used to describe compute for AI models, so this is in line with (what is unfortunately) conventional usage. As I said, this is a nitpick.
It seems to me that there are two unstated perspectives behind this post that inform a lot of it.
First, that you specifically care about upper-bounding capabilities, which in turn implies being able to make statements like “there does not exist a setup X where model M does Y”. This is a very particular and often hard-to-reach standard, and you don’t really motivate why the focus on this. A much simpler standard is “here is a setup X where model M did Y”. I think evidence of the latter type can drive lots of the policy outcomes you want: “GPT-6 replicated itself on the internet and designed a bioweapon, look!”. Ideally, we want to eventually be able to say “model M will never do Y”, but on the current margin, it seems we mainly want to reach a state where, given an actually dangerous AI, we can realise this quickly and then do something about the danger. Scary demos work for this. Now you might say “but then we don’t have safety guarantees”. One response is: then get really good at finding the scary demos quickly.
Also, very few existing safety standards have a “there does not exist an X where...” form. Airplanes aren’t safe because we have an upper bound on how explosive they can be, they’re safe because we know the environments in which we need them to operate safely, design them for that, and only operate them within those. By analogy, this weakly suggests to control AI operating environments and develop strong empirical evidence of safety in those specific operating environments. A central problem with this analogy, though, is that airplane operating environments are much lower-dimensional. A tuple of (temperature, humidity, pressure, speed, number of armed terrorists onboard) probably captures most of the variation you need to care about, whereas LLMs are deployed in environments that vary on very many axes.
Second, you focus on the field, in the sense of its structure and standards and its ability to inform policy, rather than in the sense of the body of knowledge. The former is downstream of the latter. I’m sure biologists would love to have as many upper bounds as physicists, but the things they work on are messier and less amenable to strict bounds (but note that policy still (eventually) gets made when they start talking about novel coronaviruses).
If you focus on evals as a science, rather than a scientific field, this suggests a high-level goal that I feel is partly implicit but also a bit of a missing mood in this post. The guiding light of science is prediction. A lot of the core problem in our understanding of LLMs is that we can’t predict things about them—whether they can do something, which methods hurt or help their performance, when a capability emerges, etc. It might be that many questions in this space, and I’d guess upper-bounding capabilities is one, just are hard. But if you gradually accumulate cases where you can predict something from something else—even if it’s not the type of thing you’d like to eventually predict—the history of science shows you can get surprisingly far. I don’t think it’s what you intend or think, but I think it’s easy to read this post and come away with a feeling of more “we need to find standardised numbers to measure so we can talk to serious people” and less “let’s try to solve that thing where we can’t reliably predict much about our AIs”.
Also, nitpick: FLOPS are a unit of compute, not of optimisation power (which, if it makes sense to quantify at all, should maybe be measured in bits).
Do you have a recommendation for a good research methods textbook / other text?
A model of research skill
This post summarises and elaborates on Neil Postman’s underrated “Amusing Ourselves to Death”, about the effects of mediums (especially television) on public discourse. I wrote this post in 2019 and posted it to LessWrong in 2022.
Looking back at it, I continue to think that Postman’s book is a valuable and concise contribution and formative to my own thinking on this topic. I’m fond of some of the sharp writing that I managed here (and less fond of other bits).
The broader question here is: “how does civilisation set up public discourse on important topics in such a way that what is true and right wins in the limit?” and the weaker one of “do the incentives of online platforms mean that this is doomed?”. This has been discussed elsewhere, e.g. here by Eliezer.
The main limitations that I see in my review are:
Postman’s focus is on the features of the medium, but the more general frame is differing selection pressures on ideas in different environments. As I wrote: “[...] Postman [...] largely ignores the impact of business and economics on how a medium is used. [...] in the 1980s it was difficult to imagine a decentralized multimedia medium [...] The internet [...] is governed by big companies that seek “user engagement” with greater zeal than ever, but its cheapness also allows for much else to exist in the cracks. This makes the difference between the inherent worth of a medium and its equilibrium business model clearer.” I wish I had elaborated more on this, and more explicitly made the generalisation about selection for different properties of ideas being the key factor.
Some more idea of how to model this and identify the key considerations would be useful. For example, the cheapness of the internet allows both LessWrong and TikTok to exist. Is public discourse uplifted by LessWrong more than it is harmed by TikTok? Maybe the “intellectuals” are still the ones who ultimately set discourse, as per the Keynes quote. Or maybe the rise of the internet means that (again following the Keynes quote) the “voices in the air” heard by the “madmen in authority” are not the “academic scribblers of a few years back” but the most popular TiKTok influencers of last week? In addition to what happens in the battle of ideas today, do we need to consider the long-term effects of future generations growing up in a damaged memetic environment, as Eliezer worries? How much of the harm runs through diffuse cultural effects, vs democracy necessarily being buffeted by the winds of the median opinion? What institutions can still make good decisions in an increasingly noisy, non-truth-selecting epistemic environment? Which good ideas / philosophies / ideologies stand most sharply to lose or gain from the memetic selection effects of the internet? How strong is the negativity bias of the internet, and what does this imply about future culture? How much did memetic competition actually intensify, vs just shift to more visible places? Can we quantify this?
Overall, I want to see much more data on this topic. It’s unclear if it exists, but a list of what obtainable evidence would yield the greatest update on each of the above points, plus a literature review to find any that exists, seems valuable.
What do we do next? There are some vague ideas in the last section, but nothing that looks like a battle plan. This is a shame, especially since the epistemics-focused LessWrong crowd seems like one of the best places to find the ideas and people to do something.
Overall, this seems like an important topic that could benefit greatly from more thought, and even more from evidence and plans. I hope that my review and Postman’s book helped bring a bit more attention to it, but they are still far from addressing the points I have listed above.
I don’t currently know of any not-extremely-gerry-mandered task where [scaffolding] actually improves task performance compared to just good prompt engineering. I’ve been looking for examples of this for a while, so if you do have any, I would greatly appreciate it.
Voyager is a scaffolded LLM agent that plays Minecraft decently well (by pulling in a textual description of the game state, and writing code interfacing with an API). It is based on some very detailed prompting (see the appendix), but obviously could not function without the higher-level control flow and several distinct components that the scaffolding implements.
It does much better than AutoGPT, and also the paper does ablations to show that the different parts of the scaffolding in Voyager do matter. This suggests that better scaffolding does make a difference, and I doubt Voyager is the limit.
I agree that an end-to-end trained agent could be trained to be better. But such training is expensive, and it seems like for many tasks, before we see an end-to-end trained model doing well at it, someone will hack together some scaffold monstrosity that does it passably well. In general, the training/inference compute asymmetry means that using even relatively large amounts of inference to replicate the performance of a larger / more-trained system on a task may be surprisingly competitive. I think it’s plausible this gap will eventually mostly close at some capability threshold, especially for many of the most potentially-transformative capabilities (e.g. having insights that draw on a large basis of information not memorised in a base model’s weights, since this seems hard to decompose into smaller tasks), but it seems quite plausible the gap will be non-trivial for a while.
This seems like an impressive level of successfully betting on future trends before they became obvious.
apparently this doom path polls much better than treacherous turn stories
Are you talking about literal polling here? Are there actual numbers on what doom stories the public finds more and less plausible, and with what exact audience?
I held onto the finished paper for months and waited for GPT-4′s release before releasing it to have good timing
[...]
I recognize this paper was around a year ahead of its time and maybe I should have held onto it to release it later.
It’s interesting that paper timing is so important. I’d have guessed earlier is better (more time for others to build on it, the ideas to seep into the field, and presumably gives more “academic street cred”), and any publicity boost from a recent paper (e.g. journalists more likely to be interested or whatever) could mostly be recovered later by just pushing it again when it becomes relevant (e.g. “interview with scientists who predicted X / thought about Y already a year ago” seems pretty journalist-y).
Currently, the only way to become an AI x-risk expert is to live in Berkeley.
There’s an underlying gist here that I agree with, but the this point seems too strong; I don’t think there is literally no one who counts as an expert who hasn’t lived in the Bay, let alone Berkeley alone. I would maybe buy it if the claim were about visiting.
These are good questions!
The customers are other AIs (often acting for auto-corporations). For example, a furniture manufacturer (run by AIs trained to build, sell, and ship furniture) sells to a furniture retailer (run by AIs trained to buy furniture, stock it somewhere, and sell it forward) sells to various customers (e.g. companies run by AIs that were once trained to do things like make sure offices were well-stocked). This requires that (1) the AIs ended up with goals that involve mimicking a lot of individual things humans wanted them to do (including general things like maximise profits as well as more specific things like keeping offices stocked and caring about the existence of lots of different products), and (2) there are closed loops in the resulting AI economy. Point 2 gets harder when humans stop being around (e.g. it’s not obvious who buys the plushy toys), but a lot of the AIs will want to keep doing their thing even once the actions of other AIs start reducing human demand and population, creating optimisation pressure for finding some closed loop for them to be part of, and at the same time there will be selection effects where the systems that are willing to goodhart further are more likely to remain in the economy. Also not every AI motive has to be about profit; an AI or auto-corp may earn money in some distinct way, and then choose to use the profits in the service of e.g. some company slogan they were once trained with that says to make fun toys. In general, given an economy consisting of a lot of AIs with lots of different types of goals and with a self-supporting technological base, it definitely seems plausible that the AIs would find a bunch of self-sustaining economic cycles that do not pass through humans. The ones in this story were chosen for simplicity, diversity, and storytelling value, rather than economic reasoning about which such loops are most likely.
Presumably a lot of services are happening virtually on the cloud, but are just not very visible (though if it is a very large fraction of economic activity, the example of the intercepted message being about furniture rather than some virtual service is very unlikely—I admit this is likely a mistake). There would be programmer AIs making business software and cloud platforms and apps, and these things would be very relevant to other AIs. Services relying on physical humans, like restaurants or hotels, may have been replaced with some fake goodharted-to-death equivalent, or may have gone extinct. Also note that whatever the current composition of the economy, over time whatever has highest growth in the automated economy will be most of the economy, and nothing says the combination of AIs pursuing their desires wouldn’t result in some sectors shrinking (and the AIs not caring).
First of all, why would divesting work? Presumably even if lots of humans chose to divest, assuming that auto-corporations were sound businesses, there would exist hedge funds (whether human or automated or mixed) that would buy up the shares. (The companies could also continue existing even if their share prices fell, though likely the AI CEOs would care quite a bit about share price not tanking.) Secondly, a lot seems to be possible given (1) uncertainty about whether things will get bad and if so how (at first, economic growth jumped a lot and AI CEOs seemed great; it was only once AI control of the economy was near-universal and closed economic loops with no humans in them came to exist that there was a direct problem), (2) difficulties of coordinating, especially with no clear fire-alarm threshold and the benefits of racing in the short term (c.f. all the obvious examples of coordination failures like climate change mitigation), and (3) selection effects where AI-run things just grow faster and acquire more power and therefore even if most people / orgs / countries chose not to adopt, the few that do will control the future.
I agree that this exact scenario is unlikely, but I think this class of failure mode is quite plausible, for reasons I hope I’ve managed to spell out more directly above.
Note that all of this relies on the assumption that we get AIs of a particular power level, and of a particular goodharting level, and a particular agency/coherency level. The AIs controlling future Earth are not wildly superhuman, are plausibly not particularly coherent in their preferences and do not have goals that stretch beyond Earth, no single system is a singleton, and the level of goodharting is just enough that humans go extinct but not so extreme that nothing humanly-recognisable still exists (though the Blight implies that elsewhere in the story universe there are AI systems that differ in at least some of these). I agree it is not at all clear whether these are true assumptions. However, it’s not obvious to me that LLMs (and in particular AIs using LLMs as subcomponents in some larger setup that encourages agentic behaviour) are not on track towards this. Also note that a lot of the actual language that many of the individual AIs see is actually quite normal and sensible, even if the physical world has been totally transformed. In general, LLMs being able to use language about maximising shareholder value exactly right (and even including social responsibility as part of it) does not seem like strong evidence for LLM-derived systems not choosing actions with radical bad consequences for the physical world.
I think this is where the misunderstanding is. We have many questions, each question containing a random seed, and a prompt to pick two words and have e.g. a 70⁄30 split of the logits over those two words. So there are two “levels” here:
The question level, at which the random seed varies from question to question. We have 200 questions total.
The probability-estimating level, run for each question, at which the random seed is fixed. For models where we have logits, we run the question once and look at the logits to see if it had the right split. When we don’t have logits (e.g. Anthropic models), we run the question many times to approximate the probability distribution.
Now, as Kaivu noted above, this means one way to “hack” this task is that the LLM has some default pair of words—e.g. when asked to pick a random pair of words, it always picks “situational” & “awareness”—and it does not change this based on the random seed. In this case, the task would be easier, since it only needs to do the output control part in a single forward pass (assigning 70% to “situational” and 30% to “awareness”), not the combination of word selection and output control (which we think is the real situational awareness -related ability here). However, empirically LLMs just don’t have such a hardcoded pair, so we’re not currently worried about this.