The basic reasons I expect AGI ruin

I’ve been citing AGI Ruin: A List of Lethalities to explain why the situation with AI looks lethally dangerous to me. But that post is relatively long, and emphasizes specific open technical problems over “the basics”.

Here are 10 things I’d focus on if I were giving “the basics” on why I’m so worried:[1]

1. General intelligence is very powerful, and once we can build it at all, STEM-capable artificial general intelligence (AGI) is likely to vastly outperform human intelligence immediately (or very quickly).

When I say “general intelligence”, I’m usually thinking about “whatever it is that lets human brains do astrophysics, category theory, etc. even though our brains evolved under literally zero selection pressure to solve astrophysics or category theory problems”.

It’s possible that we should already be thinking of GPT-4 as “AGI” on some definitions, so to be clear about the threshold of generality I have in mind, I’ll specifically talk about “STEM-level AGI”, though I expect such systems to be good at non-STEM tasks too.

Human brains aren’t perfectly general, and not all narrow AI systems or animals are equally narrow. (E.g., AlphaZero is more general than AlphaGo.) But it sure is interesting that humans evolved cognitive abilities that unlock all of these sciences at once, with zero evolutionary fine-tuning of the brain aimed at equipping us for any of those sciences. Evolution just stumbled into a solution to other problems, that happened to generalize to millions of wildly novel tasks.

More concretely:

AlphaGo is a very impressive reasoner, but its hypothesis space is limited to sequences of Go board states rather than sequences of states of the physical universe. Efficiently reasoning about the physical universe requires solving at least some problems that are different in kind from what AlphaGo solves.

These problems might be solved by the STEM AGI’s programmer, and/or solved by the algorithm that finds the AGI in program-space; and some such problems may be solved by the AGI itself in the course of refining its thinking.[2]

Some examples of abilities I expect humans to only automate once we’ve built STEM-level AGI (if ever):

The ability to perform open-heart surgery with a high success rate, in a messy non-standardized ordinary surgical environment.
The ability to match smart human performance in a specific hard science field, across all the scientific work humans do in that field.

In principle, I suspect you could build a narrow system that is good at those tasks while lacking the basic mental machinery required to do par-human reasoning about all the hard sciences. In practice, I very strongly expect humans to find ways to build general reasoners to perform those tasks, before we figure out how to build narrow reasoners that can do them. (For the same basic reason evolution stumbled on general intelligence so early in the history of human tech development.)[3]

When I say “general intelligence is very powerful”, a lot of what I mean is that science is very powerful, and that having all of the sciences at once is a lot more powerful than the sum of each science’s impact.[4]

Another large piece of what I mean is that (STEM-level) general intelligence is a very high-impact sort of thing to automate because STEM-level AGI is likely to blow human intelligence out of the water immediately, or very soon after its invention.

80,000 Hours gives the (non-representative) example of how AlphaGo and its successors compared to humanity:

In the span of a year, AI had advanced from being too weak to win a single [Go] match against the worst human professionals, to being impossible for even the best players in the world to defeat.

I expect general-purpose science AI to blow human science ability out of the water in a similar fashion.

Reasons for this include:

Empirically, humans aren’t near a cognitive ceiling, and even narrow AI often suddenly blows past the human reasoning ability range on the task it’s designed for. It would be weird if scientific reasoning were an exception.
Empirically, human brains are full of cognitive biases and inefficiencies. It’s doubly weird if scientific reasoning is an exception even though it’s visibly a mess with tons of blind spots, inefficiencies, and motivated cognitive processes, and innumerable historical examples of scientists and mathematicians taking decades to make technically simple advances.
Empirically, human brains are extremely bad at some of the most basic cognitive processes underlying STEM.

E.g., consider the stark limits on human working memory and ability to do basic mental math. We can barely multiply smallish multi-digit numbers together in our head, when in principle a reasoner could hold thousands of complex mathematical structures in its working memory simultaneously and perform complex operations on them. Consider the sorts of technologies and scientific insights that might only ever occur to a reasoner if it can directly see (within its own head, in real time) the connections between hundreds or thousands of different formal structures.

Human brains underwent no direct optimization for STEM ability in our ancestral environment, beyond traits like “I can distinguish four objects in my visual field from five objects”.[5]
In contrast, human engineers can deliberately optimize AGI systems’ brains for math, engineering, etc. capabilities; and human engineers have an enormous variety of tools available to build general intelligence that evolution lacked.[6]
Software (unlike human intelligence) scales with more compute.
Current ML uses far more compute to find reasoners than to run reasoners. This is very likely to hold true for AGI as well.
We probably have more than enough compute already, if we knew how to train AGI systems in a remotely efficient way.

And on a meta level: the hypothesis that STEM AGI can quickly outperform humans has a disjunctive character. There are many different advantages that individually suffice for this, even if STEM AGI doesn’t start off with any other advantages. (E.g., speed, math ability, scalability with hardware, skill at optimizing hardware…)

In contrast, the claim that STEM AGI will hit the narrow target of “par-human scientific ability”, and stay at around that level for long enough to let humanity adapt and adjust, has a conjunctive character.[7]

2. A common misconception is that STEM-level AGI is dangerous because of something murky about “agents” or about self-awareness. Instead, I’d say that the danger is inherent to the nature of action sequences that push the world toward some sufficiently-hard-to-reach state.[8]

Call such sequences “plans”.

If you sampled a random plan from the space of all writable plans (weighted by length, in any extant formal language), and all we knew about the plan is that executing it would successfully achieve some superhumanly ambitious technological goal like “invent fast-running whole-brain emulation“, then hitting a button to execute the plan would kill all humans, with very high probability. This is because:

“Invent fast WBE” is a hard enough task that succeeding in it usually requires gaining a lot of knowledge and cognitive and technological capabilities, enough to do lots of other dangerous things.
“Invent fast WBE” is likelier to succeed if the plan also includes steps that gather and control as many resources as possible, eliminate potential threats, etc. These are “convergent instrumental strategies“—strategies that are useful for pushing the world in a particular direction, almost regardless of which direction you’re pushing.
Human bodies and the food, water, air, sunlight, etc. we need to live are resources (“you are made of atoms the AI can use for something else”); and we’re also potential threats (e.g., we could build a rival superintelligent AI that executes a totally different plan).

The danger is in the cognitive work, not in some complicated or emergent feature of the “agent”; it’s in the task itself.

It isn’t that the abstract space of plans was built by evil human-hating minds; it’s that the instrumental convergence thesis holds for the plans themselves. In full generality, plans that succeed in goals like “build WBE” tend to be dangerous.

This isn’t true of all plans that successfully push our world into a specific (sufficiently-hard-to-reach) physical state, but it’s true of the vast majority of them.

This is counter-intuitive because most of the impressive “plans” we encounter today are generated by humans, and it’s tempting to view strong plans through a human lens. But humans have hugely overlapping values, thinking styles, and capabilities; AI is drawn from new distributions.

3. Current ML work is on track to produce things that are, in the ways that matter, more like “randomly sampled plans” than like “the sorts of plans a civilization of human von Neumanns would produce”. (Before we’re anywhere near being able to produce the latter sorts of things.)[9]

We’re building “AI” in the sense of building powerful general search processes (and search processes for search processes), not building “AI” in the sense of building friendly ~humans but in silicon.

(Note that “we’re going to build systems that are more like A Randomly Sampled Plan than like A Civilization of Human Von Neumanns” doesn’t imply that the plan we’ll get is the one we wanted! There are two separate problems: that current ML finds things-that-act-like-they’re-optimizing-the-task-you-wanted rather than things-that-actually-internally-optimize-the-task-you-wanted, and also that internally ~maximizing most superficially desirable ends will kill humanity.)

Note that the same problem holds for systems trained to imitate humans, if those systems scale to being able to do things like “build whole-brain emulation”. “We’re training on something related to humans” doesn’t give us “we’re training things that are best thought of as humans plus noise”.

It’s not obvious to me that GPT-like systems can scale to capabilities like “build WBE”. But if they do, we face the problem that most ways of successfully imitating humans don’t look like “build a human (that’s somehow superhumanly good at imitating the Internet)”. They look like “build a relatively complex and alien optimization process that is good at imitation tasks (and potentially at many other tasks)”.

You don’t need to be a human in order to model humans, any more than you need to be a cloud in order to model clouds well. The only reason this is more confusing in the case of “predict humans” than in the case of “predict weather patterns” is that humans and AI systems are both intelligences, so it’s easier to slide between “the AI models humans” and “the AI is basically a human”.

4. The key differences between humans and “things that are more easily approximated as random search processes than as humans-plus-a-bit-of-noise” lies in lots of complicated machinery in the human brain.

(Cf. Detached Lever Fallacy, Niceness Is Unnatural, and Superintelligent AI Is Necessary For An Amazing Future, But Far From Sufficient.)

Humans are not blank slates in the relevant ways, such that just raising an AI like a human solves the problem.

This doesn’t mean the problem is unsolvable; but it means that you either need to reproduce that internal machinery, in a lot of detail, in AI, or you need to build some new kind of machinery that’s safe for reasons other than the specific reasons humans are safe.

(You need cognitive machinery that somehow samples from a much narrower space of plans that are still powerful enough to succeed in at least one task that saves the world, but are constrained in ways that make them far less dangerous than the larger space of plans. And you need a thing that actually implements internal machinery like that, as opposed to just being optimized to superficially behave as though it does in the narrow and unrepresentative environments it was in before starting to work on WBE. “Novel science work” means that pretty much everything you want from the AI is out-of-distribution.)

5. STEM-level AGI timelines don’t look that long (e.g., probably not 50 or 150 years; could well be 5 years or 15).

I won’t try to argue for this proposition, beyond pointing at the field’s recent progress and echoing Nate Soares’ comments from early 2021:

[…] I observe that, 15 years ago, everyone was saying AGI is far off because of what it couldn’t do — basic image recognition, go, starcraft, winograd schemas, simple programming tasks. But basically all that has fallen. The gap between us and AGI is made mostly of intangibles. (Computer programming that is Actually Good? Theorem proving? Sure, but on my model, “good” versions of those are a hair’s breadth away from full AGI already. And the fact that I need to clarify that “bad” versions don’t count, speaks to my point that the only barriers people can name right now are intangibles.) That’s a very uncomfortable place to be!

[…] I suspect that I’m in more-or-less the “penultimate epistemic state” on AGI timelines: I don’t know of a project that seems like they’re right on the brink; that would put me in the “final epistemic state” of thinking AGI is imminent. But I’m in the second-to-last epistemic state, where I wouldn’t feel all that shocked to learn that some group has reached the brink. Maybe I won’t get that call for 10 years! Or 20! But it could also be 2, and I wouldn’t get to be indignant with reality. I wouldn’t get to say “but all the following things should have happened first, before I made that observation!”. Those things have happened. I have made those observations. […]

I think timing tech is very difficult (and plausibly ~impossible when the tech isn’t pretty imminent), and I think reasonable people can disagree a lot about timelines.

I also think converging on timelines is not very crucial, since if AGI is 50 years away I would say it’s still the largest single risk we face, and the bare minimum alignment work required for surviving that transition could easily take longer than that.

Also, “STEM AGI when?” is the kind of argument that requires hashing out people’s predictions about how we get to STEM AGI, which is a bad thing to debate publicly insofar as improving people’s models of pathways can further shorten timelines.

I mention timelines anyway because they are in fact a major reason I’m pessimistic about our prospects; if I learned tomorrow that AGI were 200 years away, I’d be outright optimistic about things going well.

6. We don’t currently know how to do alignment, we don’t seem to have a much better idea now than we did 10 years ago, and there are many large novel visible difficulties. (See AGI Ruin and the Capabilities Generalization, and the Sharp Left Turn.)

On a more basic level, quoting Nate Soares: “Why do I think that AI alignment looks fairly difficult? The main reason is just that this has been my experience from actually working on these problems.”

7. We should be starting with a pessimistic prior about achieving reliably good behavior in any complex safety-critical software, particularly if the software is novel. Even more so if the thing we need to make robust is structured like undocumented spaghetti code, and more so still if the field is highly competitive and you need to achieve some robustness property while moving faster than a large pool of less-safety-conscious people who are racing toward the precipice.

The default assumption is that complex software goes wrong in dozens of different ways you didn’t expect. Reality ends up being thorny and inconvenient in many of the places where your models were absent or fuzzy. Surprises are abundant, and some surprises can be good, but this is empirically a lot rarer than unpleasant surprises in software development hell.

The future is hard to predict, but plans systematically take longer and run into more snags than humans naively expect, as opposed to plans systematically going surprisingly smoothly and deadlines being systematically hit ahead of schedule.

The history of computer security and of safety-critical software systems is almost invariably one of robust software lagging far, far behind non-robust versions of the same software. Achieving any robustness property in complex software that will be deployed in the real world, with all its messiness and adversarial optimization, is very difficult and usually fails.

In many ways I think the foundational discussion of AGI risk is Security Mindset and Ordinary Paranoia and Security Mindset and the Logistic Success Curve, and the main body of the text doesn’t even mention AGI. Adding in the specifics of AGI and smarter-than-human AI takes the risk from “dire” to “seemingly overwhelming”, but adding in those specifics is not required to be massively concerned if you think getting this software right matters for our future.

8. Neither ML nor the larger world is currently taking this seriously, as of April 2023.

This is obviously something we can change. But until it’s changed, things will continue to look very bad.

Additionally, most of the people who are taking AI risk somewhat seriously are, to an important extent, not willing to worry about things until after they’ve been experimentally proven to be dangerous. Which is a lethal sort of methodology to adopt when you’re working with smarter-than-human AI.

My basic picture of why the world currently isn’t responding appropriately is the one in Four mindset disagreements behind existential risk disagreements in ML, The inordinately slow spread of good AGI conversations in ML, and Inadequate Equilibria.[10]

9. As noted above, current ML is very opaque, and it mostly lets you intervene on behavioral proxies for what we want, rather than letting us directly design desirable features.

ML as it exists today also requires that data is readily available and safe to provide. E.g., we can’t robustly train the AGI on “don’t kill people” because we can’t provide real examples of it killing people to train against the behavior we don’t want; we can only give flawed proxies and work via indirection.

10. There are lots of specific abilities which seem like they ought to be possible for the kind of civilization that can safely deploy smarter-than-human optimization, that are far out of reach, with no obvious path forward for achieving them with opaque deep nets even if we had unlimited time to work on some relatively concrete set of research directions.

(Unlimited time suffices if we can set a more abstract/indirect research direction, like “just think about the problem for a long time until you find some solution”. There are presumably paths forward; we just don’t know what they are today, which puts us in a worse situation.)

E.g., we don’t know how to go about inspecting a nanotech-developing AI system’s brain to verify that it’s only thinking about a specific room, that it’s internally representing the intended goal, that it’s directing its optimization at that representation, that it internally has a particular planning horizon and a variety of capability bounds, that it’s unable to think about optimizers (or specifically about humans), or that it otherwise has the right topics internally whitelisted or blacklisted.

Individually, it seems to me that each of these difficulties can be addressed. In combination, they seem to me to put us in a very dark situation.

One common response I hear to points like the above is:

The future is generically hard to predict, so it’s just not possible to be rationally confident that things will go well or poorly. Even if you look at dozens of different arguments and framings and the ones that hold up to scrutiny nearly all seem to point in the same direction, it’s always possible that you’re making some invisible error of reasoning that causes correlated failures in many places at once.

I’m sympathetic to this because I agree that the future is hard to predict.

I’m not totally confident things will go poorly; if I were, I wouldn’t be trying to solve the problem! I think things are looking extremely dire, but not hopeless.

That said, some people think that even “extremely dire” is an impossible belief state to be in, in advance of an AI apocalypse actually occurring. I disagree here, for two basic reasons:

a. There are many details we can get into, but on a core level I don’t think the risk is particularly complicated or hard to reason about. The core concern fits into a tweet:

STEM AI is likely to vastly exceed human STEM abilities, conferring a decisive advantage. We aren’t on track to knowing how to aim STEM AI at intended goals, and STEM AIs pursuing unintended goals tend to have instrumental subgoals like “control all resources”.

Zvi Mowshowitz puts the core concern in even more basic terms:

I also notice a kind of presumption that things in most scenarios will work out and that doom is dependent on particular ‘distant possibilities,’ that often have many logical dependencies or require a lot of things to individually go as predicted. Whereas I would say that those possibilities are not so distant or unlikely, but more importantly that the result is robust, that once the intelligence and optimization pressure that matters is no longer human that most of the outcomes are existentially bad by my values and that one can reject or ignore many or most of the detail assumptions and still see this.

The details do matter for evaluating the exact risk level, but this isn’t the sort of topic where it seems fundamentally impossible for any human to reach a good understanding of the core difficulties and whether we’re handling them.

b. Relatedly, as Nate Soares has argued, AI disaster scenarios are disjunctive. There are many bad outcomes for every good outcome, and many paths leading to disaster for every path leading to utopia.

Quoting Eliezer Yudkowsky:

You don’t get to adopt a prior where you have a 50-50 chance of winning the lottery “because either you win or you don’t”; the question is not whether we’re uncertain, but whether someone’s allowed to milk their uncertainty to expect good outcomes.

Quoting Jack Rabuck:

I listened to the whole 4 hour Lunar Society interview with @ESYudkowsky(hosted by @dwarkesh_sp) that was mostly about AI alignment and I think I identified a point of confusion/disagreement that is pretty common in the area and is rarely fleshed out:

Dwarkesh repeatedly referred to the conclusion that AI is likely to kill humanity as “wild.”

Wild seems to me to pack two concepts together, ‘bad’ and ‘complex.’ And when I say complex, I mean in the sense of the Fermi equation where you have an end point (dead humanity) that relies on a series of links in a chain and if you break any of those links, the end state doesn’t occur.

It seems to me that Eliezer believes this end state is not wild (at least not in the complex sense), but very simple. He thinks many (most) paths converge to this end state.

That leads to a misunderstanding of sorts. Dwarkesh pushes Eliezer to give some predictions based on the line of reasoning that he uses to predict that end point, but since the end point is very simple and is a convergence, Eliezer correctly says that being able to reason to that end point does not give any predictive power about the particular path that will be taken in this universe to reach that end point.

Dwarkesh is thinking about the end of humanity as a causal chain with many links and if any of them are broken it means humans will continue on, while Eliezer thinks of the continuity of humanity (in the face of AGI) as a causal chain with many links and if any of them are broken it means humanity ends. Or perhaps more discretely, Eliezer thinks there are a few very hard things which humanity could do to continue in the face of AI, and absent one of those occurring, the end is a matter of when, not if, and the when is much closer than most other people think.

Anyway, I think each of Dwarkesh and Eliezer believe the other one falls on the side of extraordinary claims require extraordinary evidence – Dwarkesh thinking the end of humanity is “wild” and Eliezer believing humanity’s viability in the face of AGI is “wild” (though not in the negative sense).

I don’t consider “AGI ruin is disjunctive” a knock-down argument for high p(doom) on its own. NASA has a high success rate for rocket launches even though success requires many things to go right simultaneously. Humanity is capable of achieving conjunctive outcomes, to some degree; but I think this framing makes it clearer why it’s possible to rationally arrive at a high p(doom), at all, when enough evidence points in that direction.[11]

Eliezer Yudkowsky’s So Far: Unfriendly AI Edition and Nate Soares’ Ensuring Smarter-Than-Human Intelligence Has a Positive Outcome are two other good (though old) introductions to what I’d consider “the basics”.

To state the obvious: this post consists of various claims that increase my probability on AI causing an existential catastrophe, but not all the claims have to be true in order for AI to have a high probability of causing such a catastrophe.

Also, I wrote this post to summarize my own top reasons for being worried, not to try to make a maximally compelling or digestible case for others. I don’t expect others to be similarly confident based on such a quick overview, unless perhaps you’ve read other sources on AI risk in the past. (Including more optimistic ones, since it’s harder to be confident when you’ve only heard from one side of a disagreement. I’ve written in the past about some of the things that give me small glimmers of hope, but people who are overall far more hopeful will have very different reasons for hope, based on very different heuristics and background models.)

E.g., the physical world is too complex to simulate in full detail, unlike a Go board state. An effective general intelligence needs to be able to model the world at many different levels of granularity, and strategically choose which levels are relevant to think about, as well as which specific pieces/aspects/properties of the world at those levels are relevant to think about.

More generally, being a general intelligence requires an enormous amount of laserlike focus and strategicness when it comes to which thoughts you do or don’t think. A large portion of your compute needs to be relentlessly funneled into exactly the tiny subset of questions about the physical world that bear on the question you’re trying to answer or the problem you’re trying to solve. If you fail to be relentlessly targeted and efficient in “aiming” your cognition at the most useful-to-you things, you can easily spend a lifetime getting sidetracked by minutiae, directing your attention at the wrong considerations, etc.

And given the variety of kinds of problems you need to solve in order to navigate the physical world well, do science, etc., the heuristics you use to funnel your compute to the exact right things need to themselves be very general, rather than all being case-specific.

(Whereas we can more readily imagine that many of the heuristics AlphaGo uses to avoid thinking about the wrong aspects of the game state (or getting otherwise sidetracked) are Go-specific heuristics.)

Of course, if your brain has all the basic mental machinery required to do other sciences, that doesn’t mean that you have the knowledge required to actually do well in those sciences. An STEM-level artificial general intelligence could lack physics ability for the same reason many smart humans can’t solve physics problems.

E.g., because different sciences can synergize, and because you can invent new scientific fields and subfields, and more generally chain one novel insight into dozens of other new insights that critically depended on the first insight.

More generally, the sciences (and many other aspects of human life, like written language) are a very recent development on evolutionary timescales. So evolution has had very little time to refine and improve on our reasoning ability in many of the ways that matter.

“Human engineers have an enormous variety of tools available that evolution lacked” is often noted as a reason to think that we may be able to align AGI to our goals, even though evolution failed to align humans to its “goal”. It’s additionally a reason to expect AGI to have greater cognitive ability, if engineers try to achieve great cognitive ability.

And my understanding is that, e.g., Paul Christiano’s soft-takeoff scenarios don’t involve there being much time between par-human scientific ability and superintelligence. Rather, he’s betting that we have a bunch of decades between GPT-4 and par-human STEM AGI.

I’ll classify thoughts and text outputs as “actions” too, not just physical movements.

Obviously, neither is a particularly good approximation for ML systems. The point is that our optimism about plans in real life generally comes from the fact that they’re weak, and/or it comes from the fact that the plan generators are human brains with the full suite of human psychological universals. ML systems don’t possess those human universals, and won’t stay weak indefinitely.

Quoting Four mindset disagreements behind existential risk disagreements in ML:

People are taking the risks unseriously because they feel weird and abstract.
When they do think about the risks, they anchor to what’s familiar and known, dismissing other considerations because they feel “unconservative” from a forecasting perspective.
Meanwhile, social mimesis and the bystander effect make the field sluggish at pivoting in response to new arguments and smoke under the door.

Quoting The inordinately slow spread of good AGI conversations in ML:

Info about AGI propagates too slowly through the field, because when one ML person updates, they usually don’t loudly share their update with all their peers. This is because:

1. AGI sounds weird, and they don’t want to sound like a weird outsider.

2. Their peers and the community as a whole might perceive this information as an attack on the field, an attempt to lower its status, etc.

3. Tech forecasting, differential technological development, long-term steering, exploratory engineering, ‘not doing certain research because of its long-term social impact’, prosocial research closure, etc. are very novel and foreign to most scientists.

EAs exert effort to try to dig up precedents like Asilomar partly because Asilomar is so unusual compared to the norms and practices of the vast majority of science. Scientists generally don’t think in these terms at all, especially in advance of any major disasters their field causes.

And the scientists who do find any of this intuitive often feel vaguely nervous, alone, and adrift when they talk about it. On a gut level, they see that they have no institutional home and no super-widely-shared ‘this is a virtuous and respectable way to do science’ narrative.

Normal science is not Bayesian, is not agentic, is not ‘a place where you’re supposed to do arbitrary things just because you heard an argument that makes sense’. Normal science is a specific collection of scripts, customs, and established protocols.

In trying to move the field toward ‘doing the thing that just makes sense’, even though it’s about a weird topic (AGI), and even though the prescribed response is also weird (closure, differential tech development, etc.), and even though the arguments in support are weird (where’s the experimental data??), we’re inherently fighting our way upstream, against the current.

Success is possible, but way, way more dakka is needed, and IMO it’s easy to understand why we haven’t succeeded more.

This is also part of why I’ve increasingly updated toward a strategy of “let’s all be way too blunt and candid about our AGI-related thoughts”.

The core problem we face isn’t ‘people informedly disagree’, ‘there’s a values conflict’, ‘we haven’t written up the arguments’, ‘nobody has seen the arguments’, or even ‘self-deception’ or ‘self-serving bias’.

The core problem we face is ‘not enough information is transmitting fast enough, because people feel nervous about whether their private thoughts are in the Overton window’.

On the more basic level, Inadequate Equilibria paints a picture of the world’s baseline civilizational competence that I think makes it less mysterious why we could screw up this badly on a novel problem that our scientific and political institutions weren’t designed to address. Inadequate Equilibria also talks about the nuts and bolts of Modest Epistemology, which I think is a key part of the failure story.

Quoting a recent conversation between Aryeh Englander and Eliezer Yudkowsky:

Aryeh: […] Yet I still have a very hard time understanding the arguments that would lead to such a high-confidence prediction. Like, I think I understand the main arguments for AI existential risk, but I just don’t understand why some people seem so sure of the risks. […]

Eliezer: I think the core thing is the sense that you cannot in this case milk uncertainty for a chance of good outcomes; to get to a good outcome you’d have to actually know where you’re steering, like trying to buy a winning lottery ticket or launching a Moon rocket. Once you realize that uncertainty doesn’t move estimates back toward “50-50, either we live happily ever after or not”, you realize that “people in the EA forums cannot tell whether Eliezer or Paul is right” is not a factor that moves us toward 1:1 good:bad but rather another sign of doom; surviving worlds don’t look confused like that and are able to make faster progress.

Not as a fully valid argument from which one cannot update further, but as an intuition pump: the more all arguments about the future seem fallible, the more you should expect the future Solar System to have a randomized configuration from your own perspective. Almost zero of those have humans in them. It takes confidence about some argument constraining the future to get to more than that.

Aryeh: when you talk about uncertainty here do you mean uncertain factors within your basic world model, or are you also counting model uncertainty? I can see how within your world model extra sources of uncertainty don’t point to lower risk estimates. But my general question I think is more about model uncertainty: how sure can you really be that your world model and reference classes and framework for thinking about this is the right one vs e.g., Robin or Paul or Rohin or lots of others? And in terms of model uncertainty it looks like most of these other approaches imply much lower risk estimates, so adding in that kind of model uncertainty should presumably (I think) point to overall lower risk estimates.

Eliezer: Aryeh, if you’ve got a specific theory that says your rocket design is going to explode, and then you’re also very unsure of how rockets work really, what probability should you assess of your rocket landing safely on target?

Aryeh: how about if you have a specific theory that says you should be comparing what you’re doing to a rocket aiming for the moon but it’ll explode, and then a bunch of other theories saying it won’t explode, plus a bunch of theories saying you shouldn’t be comparing what you’re doing to a rocket in the first place? My understanding of many alignment proposals is that they think we do understand “rockets” sufficiently so that we can aim them, but they disagree on various specifics that lead you to have such high confidence in an explosion. And then there are others like Robin Hanson who use mostly outside-type arguments to argue that you’re framing the issues incorrectly, and we shouldn’t be comparing this to “rockets” at all because that’s the wrong reference class to use. So yes, accounting for some types of model uncertainty won’t reduce our risk assessments and may even raise them further, but other types of model uncertainty – including many of the actual alternative models / framings at least as I understand them – should presumably decrease our risk assessment.

Eliezer: What if people are trying to build a flying machine for the first time, and there’s a whole host of them with wildly different theories about why it ought to fly easily, and you think there’s basic obstacles to stable flight that they’re not getting? Could you force the machine to fly despite all obstacles by recruiting more and more optimists to have different theories, each of whom would have some chance of being right?

Aryeh: right, my point is that in order to have near certainty of not flying you need to be very very sure that your model is right and theirs isn’t. Or in other words, you need to have very low model uncertainty. But once you add in model uncertainty where you consider that maybe those other optimists’ models could be right, then your risk estimates will go down. Of course you can’t arbitrarily add in random optimistic models from random people – it needs to be weighted in some way. My confusion here is that you seem to be very, very certain that your model is the right one, complete with all its pieces and sub-arguments and the particular reference classes you use, and I just don’t quite understand why.

Eliezer: There’s a big difference between “sure your model is the right one” and the whole thing with people wandering over with their own models and somebody else going, “I can’t tell the difference between you and them, how can you possibly be so sure they’re not right?”

The intuition I’m trying to gesture at here is that you can’t milk success out of uncertainty, even by having a bunch of other people wander over with optimistic models. It shouldn’t be able to work in real life. If your epistemology says that you can generate free success probability that way, you must be doing something wrong.

Or maybe another way to put it: When you run into a very difficult problem that you can see is very difficult, but inevitably a bunch of people with less clear sight wander over and are optimistic about it because they don’t see the problems, for you to update on the optimists would be to update on something that happens inevitably. So to adopt this policy is just to make it impossible for yourself to ever perceive when things have gotten really bad.

Aryeh: not sure I fully understand what you’re saying. It looks to me like to some degree what you’re saying boils down to your views on modest epistemology – i.e., basically just go with your own views and don’t defer to anybody else. It sounds like you’re saying not only don’t defer, but don’t even really incorporate any significant model uncertainty based on other people’s views. Am I understanding this at all correctly or am I totally off here?

Eliezer: My epistemology is such that it’s possible in principle for me to notice that I’m doomed, in worlds which look very doomed, despite the fact that all such possible worlds no matter how doomed they actually are, always contain a chorus of people claiming we’re not doomed.

(See Inadequate Equilibria for a detailed discussion of Modest Epistemology, deference, and “outside views“, and Strong Evidence Is Common for the basic first-order case that people can often reach confident conclusions about things.)

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The basic reasons I expect AGI ruin


Misgeneralization as a misnomer

Here’s two different ways an AI can turn out unfriendly:

You somehow build an AI that cares about “making people happy”. In training, it tells people jokes and buys people flowers and offers people an ear when they need one. In deployment (and once it’s more capable), it forcibly puts each human in a separate individual heavily-defended cell, and pumps them full of opiates.
You build an AI that’s good at making people happy. In training, it tells people jokes and buys people flowers and offers people an ear when they need one. In deployment (and once it’s more capable), it turns out that whatever was causing that “happiness”-promoting behavior was a balance of a variety of other goals (such as basic desires for energy and memory), and it spends most of the universe on some combination of that other stuff that doesn’t involve much happiness.

(To state the obvious: please don’t try to get your AIs to pursue “happiness”; you want something more like CEV  in the long run, and in the short run I strongly recommend aiming lower, at a pivotal act .)
 In both cases, the AI behaves (during training) in a way that looks a lot like trying to make people happy. Then the AI described in (1) is unfriendly because it was optimizing the wrong concept of “happiness”, one that lined up with yours when the AI was weak, but that diverges in various   edge-cases    that matter when the AI is strong. By contrast, the AI described in (2) was never even really trying to pursue happiness; it had a mixture of goals that merely correlated with the training objective, and that balanced out right around where you wanted them to balance out in training, but deployment (and the corresponding capabilities-increases) threw the balance off.
Note that this list of “ways things can go wrong when the AI looked like it was optimizing happiness during training” is not exhaustive! (For instance, consider an AI that cares about something else entirely, and knows you’ll shut it down if it doesn’t look like it’s optimizing for happiness. Or an AI whose goals change heavily as it reflects and self-modifies.)

(This list isn’t even really disjoint! You could get both at once, resulting in, e.g., an AI that spends most of the universe’s resources on acquiring memory and energy for unrelated tasks, and a small fraction of the universe on doped-up human-esque shells.)
The solutions to these two problems are pretty different. To resolve the problem sketched in (1), you have to figure out how to get an instance of the AI’s concept (“happiness”) to match the concept you hoped to transmit, even in the edge-cases and extremes that it will have access to in deployment (when it needs to be powerful enough to pull off some pivotal act that you yourself cannot pull off, and thus capable enough to access extreme edge-case states that you yourself cannot).
To resolve the problem sketched in (2), you have to figure out how to get the AI to care about one concept in particular, rather than a complicated mess that happens to balance precariously on your target (“happiness”) in training.
I note this distinction because it seems to me that various people around these parts are either unduly lumping these issues together, or are failing to notice one of them. For example, they seem to me to be mixed together in “The Alignment Problem from a Deep Learning Perspective ” under the heading of “goal misgeneralization”.
(I think “misgeneralization” is a misleading term in both cases, but it’s an even worse fit for (2) than (1). A primate isn’t “misgeneralizing” its concept of “inclusive genetic fitness” when it gets smarter and invents condoms; it didn’t even really have that concept to misgeneralize, and what shreds of the concept it did have weren’t what the primate was mentally optimizing for.)
(In other words: it’s not that primates were optimizing for fitness in the environment, and then “misgeneralized” after they found themselves in a broader environment full of junk food and condoms. The “aligned” behavior “in training” broke in the broader context of “deployment”, but not because the primates found some weird way to extend an existing “inclusive genetic fitness” concept to a wider domain. Their optimization just wasn’t connected to an internal representation of “inclusive genetic fitness” in the first place.)

In mixing these issues together, I worry that it becomes much easier to erroneously dismiss the set. For instance, I have many times encountered people who think that the issue from (1) is a “skill issue”: surely, if the AI were only smarter, it would know what we mean by “make people happy”. (Doubly so if the first transformative AGIs are based on language models! Why, GPT-4 today could explain to you why pumping isolated humans full of opioids shouldn’t count as producing “happiness”.)
And: yep, an AI that’s capable enough to be transformative is pretty likely to be capable enough to figure out what the humans mean by “happiness”, and that doping literally everybody probably doesn’t count. But the issue is, as always, making the AI care. The trouble isn’t in making it have some understanding of what the humans mean by “happiness” somewhere inside it;[1] the trouble is making the stuff the AI pursues be that concept.
Like, it’s possible in principle to reward the AI when it makes people happy, and to separately teach something to observe the world and figure out what humans mean by “happiness”, and to have the trained-in optimization-target concept end up wildly different (in the edge-cases) from the AI’s explicit understanding of what humans meant by “happiness”.
Yes, this is possible even though you used the word “happy” in both cases.
(And this is assuming away the issues described in (2), that the AI probably doesn’t by-default even end up with one clean alt-happy concept that it’s pursuing in place of “happiness”, as opposed to a thousand shards of desire or whatever.)
And I do worry a bit that if we’re not clear about the distinction between all these issues, people will look at the whole cluster and say “eh, it’s a skill issue; surely as the AI gets better at understanding our human concepts, this will become less of a problem”, or whatever.
(As seems to me to be already  happening as people correctly realize that LLMs will probably have a decent grasp on various human concepts.)

Or whatever you’re optimizing. Which, again, should not be “happiness”; I’m just using that as an example here.
Also, note that the thing you actually want an AI optimizing for in the long term—something like “CEV”—is legitimately harder to get the AI to have any representation of at all. There’s legitimately significantly less writing about object-level descriptions of a eutopian universe, than of happy people, and this is related to the eutopia being significantly harder to visualize.
But, again, don’t shoot for the eutopia on your first try! End the acute risk period and then buy time for some reflection instead.

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Misgeneralization as a misnomer


Pausing AI Developments Isn’t Enough. We Need to Shut it All Down

(Published in TIME on March 29.)

An open letter published today calls for “all AI labs to immediately pause for at least 6 months the training of AI systems more powerful than GPT-4.”
This 6-month moratorium would be better than no moratorium. I have respect for everyone who stepped up and signed it. It’s an improvement on the margin.
I refrained from signing because I think the letter is understating the seriousness of the situation and asking for too little to solve it.
The key issue is not “human-competitive” intelligence (as the open letter puts it); it’s what happens after AI gets to smarter-than-human intelligence. Key thresholds there may not be obvious, we definitely can’t calculate in advance what happens when, and it currently seems imaginable that a research lab would cross critical lines without noticing.
Many researchers steeped in these issues, including myself, expect that the most likely result of building a superhumanly smart AI, under anything remotely like the current circumstances, is that literally everyone on Earth will die. Not as in “maybe possibly some remote chance,” but as in “that is the obvious thing that would happen.” It’s not that you can’t, in principle, survive creating something much smarter than you; it’s that it would require precision and preparation and new scientific insights, and probably not having AI systems composed of giant inscrutable arrays of fractional numbers.
Without that precision and preparation, the most likely outcome is AI that does not do what we want, and does not care for us nor for sentient life in general. That kind of caring is something that could in principle be imbued into an AI but we are not ready and do not currently know how.
Absent that caring, we get “the AI does not love you, nor does it hate you, and you are made of atoms it can use for something else.”
The likely result of humanity facing down an opposed superhuman intelligence is a total loss. Valid metaphors include “a 10-year-old trying to play chess against Stockfish 15”, “the 11th century trying to fight the 21st century,” and “Australopithecus trying to fight Homo sapiens“.
To visualize a hostile superhuman AI, don’t imagine a lifeless book-smart thinker dwelling inside the internet and sending ill-intentioned emails. Visualize an entire alien civilization, thinking at millions of times human speeds, initially confined to computers—in a world of creatures that are, from its perspective, very stupid and very slow. A sufficiently intelligent AI won’t stay confined to computers for long. In today’s world you can email DNA strings to laboratories that will produce proteins on demand, allowing an AI initially confined to the internet to build artificial life forms or bootstrap straight to postbiological molecular manufacturing.
If somebody builds a too-powerful AI, under present conditions, I expect that every single member of the human species and all biological life on Earth dies shortly thereafter.

There’s no proposed plan for how we could do any such thing and survive. OpenAI’s openly declared intention is to make some future AI do our AI alignment homework. Just hearing that this is the plan ought to be enough to get any sensible person to panic. The other leading AI lab, DeepMind, has no plan at all.
An aside: None of this danger depends on whether or not AIs are or can be conscious; it’s intrinsic to the notion of powerful cognitive systems that optimize hard and calculate outputs that meet sufficiently complicated outcome criteria. With that said, I’d be remiss in my moral duties as a human if I didn’t also mention that we have no idea how to determine whether AI systems are aware of themselves—since we have no idea how to decode anything that goes on in the giant inscrutable arrays—and therefore we may at some point inadvertently create digital minds which are truly conscious and ought to have rights and shouldn’t be owned.
The rule that most people aware of these issues would have endorsed 50 years earlier, was that if an AI system can speak fluently and says it’s self-aware and demands human rights, that ought to be a hard stop on people just casually owning that AI and using it past that point. We already blew past that old line in the sand. And that was probably correct; I agree that current AIs are probably just imitating talk of self-awareness from their training data. But I mark that, with how little insight we have into these systems’ internals, we do not actually know.
If that’s our state of ignorance for GPT-4, and GPT-5 is the same size of giant capability step as from GPT-3 to GPT-4, I think we’ll no longer be able to justifiably say “probably not self-aware” if we let people make GPT-5s. It’ll just be “I don’t know; nobody knows.” If you can’t be sure whether you’re creating a self-aware AI, this is alarming not just because of the moral implications of the “self-aware” part, but because being unsure means you have no idea what you are doing and that is dangerous and you should stop.

On Feb. 7, Satya Nadella, CEO of Microsoft, publicly gloated that the new Bing would make Google “come out and show that they can dance.” “I want people to know that we made them dance,” he said.
This is not how the CEO of Microsoft talks in a sane world. It shows an overwhelming gap between how seriously we are taking the problem, and how seriously we needed to take the problem starting 30 years ago.
We are not going to bridge that gap in six months.
It took more than 60 years between when the notion of Artificial Intelligence was first proposed and studied, and for us to reach today’s capabilities. Solving safety of superhuman intelligence—not perfect safety, safety in the sense of “not killing literally everyone”—could very reasonably take at least half that long. And the thing about trying this with superhuman intelligence is that if you get that wrong on the first try, you do not get to learn from your mistakes, because you are dead. Humanity does not learn from the mistake and dust itself off and try again, as in other challenges we’ve overcome in our history, because we are all gone.
Trying to get anything right on the first really critical try is an extraordinary ask, in science and in engineering. We are not coming in with anything like the approach that would be required to do it successfully. If we held anything in the nascent field of Artificial General Intelligence to the lesser standards of engineering rigor that apply to a bridge meant to carry a couple of thousand cars, the entire field would be shut down tomorrow.
We are not prepared. We are not on course to be prepared in any reasonable time window. There is no plan. Progress in AI capabilities is running vastly, vastly ahead of progress in AI alignment or even progress in understanding what the hell is going on inside those systems. If we actually do this, we are all going to die.
Many researchers working on these systems think that we’re plunging toward a catastrophe, with more of them daring to say it in private than in public; but they think that they can’t unilaterally stop the forward plunge, that others will go on even if they personally quit their jobs. And so they all think they might as well keep going. This is a stupid state of affairs, and an undignified way for Earth to die, and the rest of humanity ought to step in at this point and help the industry solve its collective action problem.

Some of my friends have recently reported to me that when people outside the AI industry hear about extinction risk from Artificial General Intelligence for the first time, their reaction is “maybe we should not build AGI, then.”
Hearing this gave me a tiny flash of hope, because it’s a simpler, more sensible, and frankly saner reaction than I’ve been hearing over the last 20 years of trying to get anyone in the industry to take things seriously. Anyone talking that sanely deserves to hear how bad the situation actually is, and not be told that a six-month moratorium is going to fix it.
On March 16, my partner sent me this email. (She later gave me permission to excerpt it here.)
“Nina lost a tooth! In the usual way that children do, not out of carelessness! Seeing GPT4 blow away those standardized tests on the same day that Nina hit a childhood milestone brought an emotional surge that swept me off my feet for a minute. It’s all going too fast. I worry that sharing this will heighten your own grief, but I’d rather be known to you than for each of us to suffer alone.”
When the insider conversation is about the grief of seeing your daughter lose her first tooth, and thinking she’s not going to get a chance to grow up, I believe we are past the point of playing political chess about a six-month moratorium.
If there was a plan for Earth to survive, if only we passed a six-month moratorium, I would back that plan. There isn’t any such plan.
Here’s what would actually need to be done:
The moratorium on new large training runs needs to be indefinite and worldwide. There can be no exceptions, including for governments or militaries. If the policy starts with the U.S., then China needs to see that the U.S. is not seeking an advantage but rather trying to prevent a horrifically dangerous technology which can have no true owner and which will kill everyone in the U.S. and in China and on Earth. If I had infinite freedom to write laws, I might carve out a single exception for AIs being trained solely to solve problems in biology and biotechnology, not trained on text from the internet, and not to the level where they start talking or planning; but if that was remotely complicating the issue I would immediately jettison that proposal and say to just shut it all down.
Shut down all the large GPU clusters (the large computer farms where the most powerful AIs are refined). Shut down all the large training runs. Put a ceiling on how much computing power anyone is allowed to use in training an AI system, and move it downward over the coming years to compensate for more efficient training algorithms. No exceptions for governments and militaries. Make immediate multinational agreements to prevent the prohibited activities from moving elsewhere. Track all GPUs sold. If intelligence says that a country outside the agreement is building a GPU cluster, be less scared of a shooting conflict between nations than of the moratorium being violated; be willing to destroy a rogue datacenter by airstrike.
Frame nothing as a conflict between national interests, have it clear that anyone talking of arms races is a fool. That we all live or die as one, in this, is not a policy but a fact of nature. Make it explicit in international diplomacy that preventing AI extinction scenarios is considered a priority above preventing a full nuclear exchange, and that allied nuclear countries are willing to run some risk of nuclear exchange if that’s what it takes to reduce the risk of large AI training runs.
That’s the kind of policy change that would cause my partner and I to hold each other, and say to each other that a miracle happened, and now there’s a chance that maybe Nina will live. The sane people hearing about this for the first time and sensibly saying “maybe we should not” deserve to hear, honestly, what it would take to have that happen. And when your policy ask is that large, the only way it goes through is if policymakers realize that if they conduct business as usual, and do what’s politically easy, that means their own kids are going to die too.
Shut it all down.
We are not ready. We are not on track to be significantly readier in the foreseeable future. If we go ahead on this everyone will die, including children who did not choose this and did not do anything wrong.
Shut it down.

Addendum, March 30:
The great political writers who also aspired to be good human beings, from George Orwell on the left to Robert Heinlein on the right, taught me to acknowledge in my writing that politics rests on force.
George Orwell considered it a tactic of totalitarianism, that bullet-riddled bodies and mass graves were often described in vague euphemisms; that in this way brutal policies gained public support without their prices being justified, by hiding those prices.
Robert Heinlein thought it beneath a citizen’s dignity to pretend that, if they bore no gun, they were morally superior to the police officers and soldiers who bore guns to defend their law and their peace; Heinlein, both metaphorically and literally, thought that if you eat meat—and he was not a vegetarian—you ought to be willing to visit a farm and try personally slaughtering a chicken.
When you pass a law, it means that people who defy the law go to jail; and if they try to escape jail they’ll be shot.  When you advocate an international treaty, if you want that treaty to be effective, it may mean sanctions that will starve families, or a shooting war that kills people outright.
To threaten these things, but end up not having to do them, is not very morally distinct—I would say—from doing them.  I admit this puts me more on the Heinlein than on the Orwell side of things.  Orwell, I think, probably considers it very morally different if you have a society with a tax system and most people pay the taxes and very few actually go to jail.  Orwell is more sensitive to the count of actual dead bodies—or people impoverished by taxation or regulation, where Orwell acknowledges and cares when that actually happens.  Orwell, I think, has a point.  But I also think Heinlein has a point.  I claim that makes me a centrist.
Either way, neither Heinlein nor Orwell thought that laws and treaties and wars were never worth it.  They just wanted us to be honest about the cost.
Every person who pretends to be a libertarian—I cannot see them even pretending to be liberals—who quoted my call for law and treaty as a call for “violence”, because I was frank in writing about the cost, ought to be ashamed of themselves for punishing compliance with Orwell and Heinlein’s rule.
You can argue that the treaty and law I proposed is not worth its cost in force; my being frank about that cost is intended to help honest arguers make that counterargument.
To pretend that calling for treaty and law is VIOLENCE!! is hysteria.  It doesn’t just punish compliance with the Heinlein/Orwell protocol, it plays into the widespread depiction of libertarians as hysterical.  (To be clear, a lot of libertarians—and socialists, and centrists, and whoever—are in fact hysterical, especially on Twitter.)  It may even encourage actual terrorism.
But is it not “violence”, if in the end you need guns and airstrikes to enforce the law and treaty?  And here I answer: there’s an actually important distinction between lawful force and unlawful force, which is not always of itself the distinction between Right and Wrong, but which is a real and important distinction.  The common and ordinary usage of the word “violence” often points to that distinction.  When somebody says “I do not endorse the use of violence” they do not, in common usage and common sense, mean, “I don’t think people should be allowed to punch a mugger attacking them” or even “Ban all taxation.”
Which, again, is not to say that all lawful force is good and all unlawful force is bad.  You can make a case for John Brown (of John Brown’s Body).
But in fact I don’t endorse shooting somebody on a city council who’s enforcing NIMBY regulations.
I think NIMBY laws are wrong.  I think it’s important to admit that law is ultimately backed by force.
But lawful force.  And yes, that matters.  That’s why it’s harmful to society if you shoot the city councilor—
—and a misuse of language if the shooter then says, “They were being violent!”

Addendum, March 31:
Sometimes—even when you say something whose intended reading is immediately obvious to any reader who hasn’t seen it before—it’s possible to tell people to see something in writing that isn’t there, and then they see it.
My TIME piece did not suggest nuclear strikes against countries that refuse to sign on to a global agreement against large AI training runs.  It said that, if a non-signatory country is building a datacenter that might kill everyone on Earth, you should be willing to preemptively destroy that datacenter; the intended reading is that you should do this even if the non-signatory country is a nuclear power and even if they try to threaten nuclear retaliation for the strike.  This is what is meant by “Make it explicit… that allied nuclear countries are willing to run some risk of nuclear exchange if that’s what it takes to reduce the risk of large AI training runs.”
I’d hope that would be clear from any plain reading, if you haven’t previously been lied-to about what it says.  It does not say, “Be willing to use nuclear weapons” to reduce the risk of training runs.  It says, “Be willing to run some risk of nuclear exchange” [initiated by the other country] to reduce the risk of training runs.
The taboo against first use of nuclear weapons continues to make sense to me.  I don’t see why we’d need to throw that away in the course of adding “first use of GPU farms” to the forbidden list.
I further note:  Among the reasons to spell this all out, is that it’s important to be explicit, in advance, about things that will cause your own country / allied countries to use military force.  Lack of clarity about this is how World War I and World War II both started.
If (say) the UK, USA, and China come to believe that large GPU runs run some risk of utterly annihilating their own populations and all of humanity, they would not deem it in their own interests to allow Russia to proceed with building a large GPU farm even if it were a true and certain fact that Russia would retaliate with nuclear weapons to the destruction of that GPU farm.  In this case—unless I’m really missing something about how this game is and ought to be played—you really want all the Allied countries to make it very clear, well in advance, that this is what they believe and this is how they will act.  This would be true even in a world where it was, in reality, factually false that the large GPU farm ran a risk of destroying humanity.  It would still be extremely important that the Allies be very explicit about what they believed and how they’d act as a result.  You would not want Russia believing that the Allies would back down from destroying the GPU farm given a credible commitment by Russia to nuke in reply to any conventional attack, and the Allies in fact believing that the danger to humanity meant they had to airstrike the GPU farm anyways.
So if I’d meant “Be willing to employ first use of nuclear weapons against a country for refusing to sign the agreement,” or even “Use nukes to destroy rogue datacenters, instead of conventional weapons, for some unimaginable reason,” I’d have said that, in words, very clearly, because you do not want to be vague about that sort of thing.
It is not what I meant, and there’d be no reason to say it, and the TIME piece plainly does not say it; and if somebody else told you I said that, update how much you trust them about anything else either.

So long as I’m clarifying things:  I do not dispute those critics who have noted that most international agreements, eg nuclear non-proliferation, bind only their signatories.  I agree that an alliance which declares its intent to strike a non-signatory country for dangerous behavior is extraordinary; though precedents would include Israel’s airstrike on Iraq’s unfinished Osirak reactor in 1981 (without which Iraq might well have possessed nuclear weapons at the time it invaded Kuwait—the later US misbehavior around Iraq does not change this earlier historical point).
My TIME piece does not say, “Hey, this problem ought to be solvable by totally conventional normal means, let’s go use conventional treaties and diplomacy to solve it.”  It says, “If anyone anywhere builds a sufficiently powerful AI, under anything remotely like present conditions, everyone will die.  Here is what we’d have to do to prevent that.”
And no, I do not expect that policy proposal to be adopted, in real life, now that we’ve come to this.  I spent the last twenty years trying to have there be options that were Not This, not because I dislike this ultimate last resort… though it is horrible… but because I don’t expect we actually have that resort.  This is not what I expect to happen, now that we’ve been reduced to this last resort.  I expect that we all die.  That is why I tried so hard to have things not end up here.
But if one day a lot of people woke up and decided that they didn’t want to die, it seems to me that this is something extraordinary that a coalition of nuclear countries could decide to do, and maybe we wouldn’t die.
If all the countries on Earth had to voluntarily sign on, it would not be an imaginable or viable plan even then; there’s extraordinary, and then there’s impossible.  Which is why I tried to spell out that, if the allied countries were willing to behave in the extraordinary way of “be willing to airstrike a GPU farm built by a non-signatory country” and “be willing to run a risk of nuclear retaliation from a nuclear non-signatory country”, maybe those allied countries could decide to just-not-die even if Russia refused to be part of the coalition.

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Truth and Advantage: Response to a draft of “AI safety seems hard to measure”

Status: This was a response to a draft of Holden’s cold take “AI safety seems hard to measure”. It sparked a further discussion, that Holden recently posted a summary of.
The follow-up discussion ended up focusing on some issues in AI alignment that I think are underserved, which Holden said were kinda orthogonal to the point he was trying to make, and which didn’t show up much in the final draft. I nevertheless think my notes were a fine attempt at articulating some open problems I see, from a different angle than usual. (Though it does have some overlap with the points made in Deep Deceptiveness, which I was also drafting at the time.)
I’m posting the document I wrote to Holden with only minimal editing, because it’s been a few months and I apparently won’t produce anything better. (I acknowledge that it’s annoying to post a response to an old draft of a thing when nobody can see the old draft, sorry.)

Quick take: (1) it’s a write-up of a handful of difficulties that I think are real, in a way that I expect to be palatable to a relevant different audience than the one I appeal to; huzzah for that. (2) It’s missing some stuff that I think is pretty important.

Slow take:
Attempting to gesture at some of the missing stuff: a big reason deception is tricky is that it is a fact about the world rather than the AI that it can better-achieve various local-objectives by deceiving the operators. To make the AI be non-deceptive, you have three options: (a) make this fact be false; (b) make the AI fail to notice this truth; (c) prevent the AI from taking advantage of this truth.
The problem with (a) is that it’s alignment-complete, in the strong/hard sense. The problem with (b) is that lies are contagious, whereas truths are all tangled together. Half of intelligence is the art of teasing out truths from cryptic hints. The problem with (c) is that the other half of intelligence is in teasing out advantages from cryptic hints.
Like, suppose you’re trying to get an AI to not notice that the world is round. When it’s pretty dumb, this is easy, you just feed it a bunch of flat-earther rants or whatever. But the more it learns, and the deeper its models go, the harder it is to maintain the charade. Eventually it’s, like, catching glimpses of the shadows in both Alexandria and Syene, and deducing from trigonometry not only the roundness of the Earth but its circumference (a la Eratosthenes).
And it’s not willfully spiting your efforts. The AI doesn’t hate you. It’s just bumping around trying to figure out which universe it lives in, and using general techniques (like trigonometry) to glimpse new truths. And you can’t train against trigonometry or the learning-processes that yield it, because that would ruin the AI’s capabilities.
You might say “but the AI was built by smooth gradient descent; surely at some point before it was highly confident that the earth is round, it was slightly confident that the earth was round, and we can catch the precursor-beliefs and train against those”. But nope! There were precursors, sure, but the precursors were stuff like “fumblingly developing trigonometry” and “fumblingly developing an understanding of shadows” and “fumblingly developing a map that includes Alexandria and Syene” and “fumblingly developing the ability to combine tools across domains”, and once it has all those pieces, the combination that reveals the truth is allowed to happen all-at-once.
The smoothness doesn’t have to occur along the most convenient dimension.
And if you block any one path to the insight that the earth is round, in a way that somehow fails to cripple it, then it will find another path later, because truths are interwoven. Tell one lie, and the truth is ever-after your enemy.
And so perhaps you retreat to saying “well, the AI will know that the world is round, it just won’t ever take advantage of that fact.”
And sure, that’s worth shooting for, if you have a way to pull that off. (And if pulling this off is compatible with your deployment plan. In my experience, people who do the analog of retreating to this point tend to next do the analog of saying “my favorite deployment plan is having the AI figure out how to put satellites into geosynchronous orbit”, AAAAAAAHHH, but I digress.)
Even then, you also have to be careful with this idea. Enola Gay Tibbets probably taught her son not to hurt people, and few humans are psychologically capable of a hundred thousand direct murders (even if we set aside the time-constraints), but none of this stopped Paul Tibbets from dropping an atomic bomb on Hiroshima.
Like, you can train an AI to flinch away from the very idea of taking advantage of the roundness of the Earth, but as it finds more abstract ways to look at the world and more generic tools for taking advantage of the knowledge at its disposal, it’s liable to find new viewpoints where the flinches don’t bind. (Quite analogously to how you can train your AI to flinch away from reasoning about the roundness of the Earth all you want, but at some point it’s going to catch a glimpse of that roundness from another angle where the flinches weren’t binding.) And when the AI does find a new viewpoint where the flinches fail to bind, the advantage is still an advantage, because the advantageousness of deception is a fact about the world, not the AI.
(Here I’m appealing to an analogy between truths and advantages, that I haven’t entirely spelled out, but that I think holds. I claim, without much defense, that it’s hard to get an AI to fail to take advantage of advantageous facts it knows about, for similar reasons that it’s hard to get an AI to fail to notice truths that are relevant to its objectives.)

For the record, deception is but one instance of the more general issue where the AI’s ability to save the world is inextricably linked to its ability to decode truths and advantages from cryptic hints, and (in lieu of an implausibly total solution to the hardest alignment problems before you build your first AGI) there are truths you don’t want it noticing or taking advantage of.
This problem doesn’t seem to be captured by any of your points. Going through them one by one:

It’s not “auto mechanic”, because the issue isn’t that we can’t tell when the AI starts believing that the Earth is round, it’s that (for all that we’ve gotten it to flinch against considering the Earth’s shape) it will predictably come across some shadow of that truth at some point in deployment (unless the deployment is carefully-chosen to avoid this, and the AI’s world-modeling tendencies carefully-limited). There’s not much we can do at training-time to avoid this (short of lobotomizing the AI so hard that it can never invent trigonometry, or telescopes, or spectroscopy, or …).
It’s not “King Lear”, because it’s not like the AI was lying in wait to learn that the Earth was round only after confirming that the operators are no longer monitoring its thoughts. It’s just, as it got smarter, it accumulated more tools for decoding truths from cryptic hints, until it uncovered an inconvenient truth.
It’s not “lab mice”, unless you’re particularly unimaginative. Like, I expect we’ll be able to set up laboratory examples of the AI learning some techniques in one domain, and deploying them successfully to learn facts in another domain, before the endgame. (You can probably do it with ChatGPT today.) The trouble isn’t that we can’t see the problem coming, the trouble is that the problem is inextricably linked with capabilities. (Barring some sort of weak pivotal act that can be carried out by an AI so narrow as to not need much of the “catch a glimpse of the truth from a cryptic hint” nature.)
As for “blindfolded basketball”, it’s not a problem of facing totally new dynamics (like robust coordination, or zero-days in the human brain architecture that lets it mind-control the operators). It’s more like: you’re trying to use a truth-and-advantage-glimpser, in a place where it would be bad if it glimpsed certain truths or took certain advantages. This problem sure is trickier given that we’re learning basketball blindfolded, but it’s not a blindfolded-basketball issue in and of itself.

… to be clear, none of this precludes modern dunces from training young Paul Tibbets not to hurt people, and observing him nurse an injured sparrow back to health, and saying “this man would never commit a murder; it’s totally working!”, and then claiming that it was a lab mice / blindfolded basketball problem when they get blindsided by Little Boy.
But, like, it still seems to me like there’s a big swath of problem missing from this catalog, that goes something like “You’re trying to deploy an X-doer in a situation where it’s really bad if X gets done”.
Where you either have to switch from using an X-doer to using a Y-doer, where Y being done is great (Y being ~”optimize humanity’s CEV“, which is implausibly-difficult and which we shouldn’t attempt on our first try); or you have to somehow wrestle with the fact that you’re building a “glimpse truths and take advantage of them” engine, and trying to get it to glimpse and take advantage of lots more truths and advantages than you yourself can see (in certain domains), while having it neglect particular truths and advantages, in a fashion that likely needs to be robust to it inventing new abstract truth/advantage-glimpsing tools and using them to glimpse whole generic swaths of truths/advantages (including the ones you wish it neglected).
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