# AGI Ruin: A List of Lethalities

### Preamble:

(If you’re already familiar with all basics and don’t want any preamble, skip ahead to Section B for technical difficulties of alignment proper.)

I have several times failed to write up a well-organized list of reasons why AGI will kill you. People come in with different ideas about why AGI would be survivable, and want to hear different obviously key points addressed first. Some fraction of those people are loudly upset with me if the obviously most important points aren’t addressed immediately, and I address different points first instead.

Having failed to solve this problem in any good way, I now give up and solve it poorly with a poorly organized list of individual rants. I’m not particularly happy with this list; the alternative was publishing nothing, and publishing this seems marginally more dignified.

Three points about the general subject matter of discussion here, numbered so as not to conflict with the list of lethalities:

-3. I’m assuming you are already familiar with some basics, and already know what ‘orthogonality’ and ‘instrumental convergence’ are and why they’re true. People occasionally claim to me that I need to stop fighting old wars here, because, those people claim to me, those wars have already been won within the important-according-to-them parts of the current audience. I suppose it’s at least true that none of the current major EA funders seem to be visibly in denial about orthogonality or instrumental convergence as such; so, fine. If you don’t know what ‘orthogonality’ or ‘instrumental convergence’ are, or don’t see for yourself why they’re true, you need a different introduction than this one.

-2. When I say that alignment is lethally difficult, I am not talking about ideal or perfect goals of ‘provable’ alignment, nor total alignment of superintelligences on exact human values, nor getting AIs to produce satisfactory arguments about moral dilemmas which sorta-reasonable humans disagree about, nor attaining an absolute certainty of an AI not killing everyone. When I say that alignment is difficult, I mean that in practice, using the techniques we actually have, “please don’t disassemble literally everyone with probability roughly 1” is an overly large ask that we are not on course to get. So far as I’m concerned, if you can get a powerful AGI that carries out some pivotal superhuman engineering task, with a less than fifty percent change of killing more than one billion people, I’ll take it. Even smaller chances of killing even fewer people would be a nice luxury, but if you can get as incredibly far as “less than roughly certain to kill everybody”, then you can probably get down to under a 5% chance with only slightly more effort. Practically all of the difficulty is in getting to “less than certainty of killing literally everyone”. Trolley problems are not an interesting subproblem in all of this; if there are any survivors, you solved alignment. At this point, I no longer care how it works, I don’t care how you got there, I am cause-agnostic about whatever methodology you used, all I am looking at is prospective results, all I want is that we have justifiable cause to believe of a pivotally useful AGI ‘this will not kill literally everyone’. Anybody telling you I’m asking for stricter ‘alignment’ than this has failed at reading comprehension. The big ask from AGI alignment, the basic challenge I am saying is too difficult, is to obtain by any strategy whatsoever a significant chance of there being any survivors.

-1. None of this is about anything being impossible in principle. The metaphor I usually use is that if a textbook from one hundred years in the future fell into our hands, containing all of the simple ideas that actually work robustly in practice, we could probably build an aligned superintelligence in six months. For people schooled in machine learning, I use as my metaphor the difference between ReLU activations and sigmoid activations. Sigmoid activations are complicated and fragile, and do a terrible job of transmitting gradients through many layers; ReLUs are incredibly simple (for the unfamiliar, the activation function is literally max(x, 0)) and work much better. Most neural networks for the first decades of the field used sigmoids; the idea of ReLUs wasn’t discovered, validated, and popularized until decades later. What’s lethal is that we do not have the Textbook From The Future telling us all the simple solutions that actually in real life just work and are robust; we’re going to be doing everything with metaphorical sigmoids on the first critical try. No difficulty discussed here about AGI alignment is claimed by me to be impossible—to merely human science and engineering, let alone in principle—if we had 100 years to solve it using unlimited retries, the way that science usually has an unbounded time budget and unlimited retries. This list of lethalities is about things we are not on course to solve in practice in time on the first critical try; none of it is meant to make a much stronger claim about things that are impossible in principle.

That said:

Here, from my perspective, are some different true things that could be said, to contradict various false things that various different people seem to believe, about why AGI would be survivable on anything remotely remotely resembling the current pathway, or any other pathway we can easily jump to.

### Section A:

This is a very lethal problem, it has to be solved one way or another, it has to be solved at a minimum strength and difficulty level instead of various easier modes that some dream about, we do not have any visible option of ‘everyone’ retreating to only solve safe weak problems instead, and failing on the first really dangerous try is fatal.

1. Alpha Zero blew past all accumulated human knowledge about Go after a day or so of self-play, with no reliance on human playbooks or sample games. Anyone relying on “well, it’ll get up to human capability at Go, but then have a hard time getting past that because it won’t be able to learn from humans any more” would have relied on vacuum. AGI will not be upper-bounded by human ability or human learning speed. Things much smarter than human would be able to learn from less evidence than humans require to have ideas driven into their brains; there are theoretical upper bounds here, but those upper bounds seem very high. (Eg, each bit of information that couldn’t already be fully predicted can eliminate at most half the probability mass of all hypotheses under consideration.) It is not naturally (by default, barring intervention) the case that everything takes place on a timescale that makes it easy for us to react.

2. A cognitive system with sufficiently high cognitive powers, given any medium-bandwidth channel of causal influence, will not find it difficult to bootstrap to overpowering capabilities independent of human infrastructure. The concrete example I usually use here is nanotech, because there’s been pretty detailed analysis of what definitely look like physically attainable lower bounds on what should be possible with nanotech, and those lower bounds are sufficient to carry the point. My lower-bound model of “how a sufficiently powerful intelligence would kill everyone, if it didn’t want to not do that” is that it gets access to the Internet, emails some DNA sequences to any of the many many online firms that will take a DNA sequence in the email and ship you back proteins, and bribes/​persuades some human who has no idea they’re dealing with an AGI to mix proteins in a beaker, which then form a first-stage nanofactory which can build the actual nanomachinery. (Back when I was first deploying this visualization, the wise-sounding critics said “Ah, but how do you know even a superintelligence could solve the protein folding problem, if it didn’t already have planet-sized supercomputers?” but one hears less of this after the advent of AlphaFold 2, for some odd reason.) The nanomachinery builds diamondoid bacteria, that replicate with solar power and atmospheric CHON, maybe aggregate into some miniature rockets or jets so they can ride the jetstream to spread across the Earth’s atmosphere, get into human bloodstreams and hide, strike on a timer. Losing a conflict with a high-powered cognitive system looks at least as deadly as “everybody on the face of the Earth suddenly falls over dead within the same second”. (I am using awkward constructions like ‘high cognitive power’ because standard English terms like ‘smart’ or ‘intelligent’ appear to me to function largely as status synonyms. ‘Superintelligence’ sounds to most people like ‘something above the top of the status hierarchy that went to double college’, and they don’t understand why that would be all that dangerous? Earthlings have no word and indeed no standard native concept that means ‘actually useful cognitive power’. A large amount of failure to panic sufficiently, seems to me to stem from a lack of appreciation for the incredible potential lethality of this thing that Earthlings as a culture have not named.)

3. We need to get alignment right on the ‘first critical try’ at operating at a ‘dangerous’ level of intelligence, where unaligned operation at a dangerous level of intelligence kills everybody on Earth and then we don’t get to try again. This includes, for example: (a) something smart enough to build a nanosystem which has been explicitly authorized to build a nanosystem; or (b) something smart enough to build a nanosystem and also smart enough to gain unauthorized access to the Internet and pay a human to put together the ingredients for a nanosystem; or (c) something smart enough to get unauthorized access to the Internet and build something smarter than itself on the number of machines it can hack; or (d) something smart enough to treat humans as manipulable machinery and which has any authorized or unauthorized two-way causal channel with humans; or (e) something smart enough to improve itself enough to do (b) or (d); etcetera. We can gather all sorts of information beforehand from less powerful systems that will not kill us if we screw up operating them; but once we are running more powerful systems, we can no longer update on sufficiently catastrophic errors. This is where practically all of the real lethality comes from, that we have to get things right on the first sufficiently-critical try. If we had unlimited retries—if every time an AGI destroyed all the galaxies we got to go back in time four years and try again—we would in a hundred years figure out which bright ideas actually worked. Human beings can figure out pretty difficult things over time, when they get lots of tries; when a failed guess kills literally everyone, that is harder. That we have to get a bunch of key stuff right on the first try is where most of the lethality really and ultimately comes from; likewise the fact that no authority is here to tell us a list of what exactly is ‘key’ and will kill us if we get it wrong. (One remarks that most people are so absolutely and flatly unprepared by their ‘scientific’ educations to challenge pre-paradigmatic puzzles with no scholarly authoritative supervision, that they do not even realize how much harder that is, or how incredibly lethal it is to demand getting that right on the first critical try.)

4. We can’t just “decide not to build AGI” because GPUs are everywhere, and knowledge of algorithms is constantly being improved and published; 2 years after the leading actor has the capability to destroy the world, 5 other actors will have the capability to destroy the world. The given lethal challenge is to solve within a time limit, driven by the dynamic in which, over time, increasingly weak actors with a smaller and smaller fraction of total computing power, become able to build AGI and destroy the world. Powerful actors all refraining in unison from doing the suicidal thing just delays this time limit—it does not lift it, unless computer hardware and computer software progress are both brought to complete severe halts across the whole Earth. The current state of this cooperation to have every big actor refrain from doing the stupid thing, is that at present some large actors with a lot of researchers and computing power are led by people who vocally disdain all talk of AGI safety (eg Facebook AI Research). Note that needing to solve AGI alignment only within a time limit, but with unlimited safe retries for rapid experimentation on the full-powered system; or only on the first critical try, but with an unlimited time bound; would both be terrifically humanity-threatening challenges by historical standards individually.

5. We can’t just build a very weak system, which is less dangerous because it is so weak, and declare victory; because later there will be more actors that have the capability to build a stronger system and one of them will do so. I’ve also in the past called this the ‘safe-but-useless’ tradeoff, or ‘safe-vs-useful’. People keep on going “why don’t we only use AIs to do X, that seems safe” and the answer is almost always either “doing X in fact takes very powerful cognition that is not passively safe” or, even more commonly, “because restricting yourself to doing X will not prevent Facebook AI Research from destroying the world six months later”. If all you need is an object that doesn’t do dangerous things, you could try a sponge; a sponge is very passively safe. Building a sponge, however, does not prevent Facebook AI Research from destroying the world six months later when they catch up to the leading actor.

6. We need to align the performance of some large task, a ‘pivotal act’ that prevents other people from building an unaligned AGI that destroys the world. While the number of actors with AGI is few or one, they must execute some “pivotal act”, strong enough to flip the gameboard, using an AGI powerful enough to do that. It’s not enough to be able to align a weak system—we need to align a system that can do some single very large thing. The example I usually give is “burn all GPUs”. This is not what I think you’d actually want to do with a powerful AGI—the nanomachines would need to operate in an incredibly complicated open environment to hunt down all the GPUs, and that would be needlessly difficult to align. However, all known pivotal acts are currently outside the Overton Window, and I expect them to stay there. So I picked an example where if anybody says “how dare you propose burning all GPUs?” I can say “Oh, well, I don’t actually advocate doing that; it’s just a mild overestimate for the rough power level of what you’d have to do, and the rough level of machine cognition required to do that, in order to prevent somebody else from destroying the world in six months or three years.” (If it wasn’t a mild overestimate, then ‘burn all GPUs’ would actually be the minimal pivotal task and hence correct answer, and I wouldn’t be able to give that denial.) Many clever-sounding proposals for alignment fall apart as soon as you ask “How could you use this to align a system that you could use to shut down all the GPUs in the world?” because it’s then clear that the system can’t do something that powerful, or, if it can do that, the system wouldn’t be easy to align. A GPU-burner is also a system powerful enough to, and purportedly authorized to, build nanotechnology, so it requires operating in a dangerous domain at a dangerous level of intelligence and capability; and this goes along with any non-fantasy attempt to name a way an AGI could change the world such that a half-dozen other would-be AGI-builders won’t destroy the world 6 months later.

7. The reason why nobody in this community has successfully named a ‘pivotal weak act’ where you do something weak enough with an AGI to be passively safe, but powerful enough to prevent any other AGI from destroying the world a year later—and yet also we can’t just go do that right now and need to wait on AI—is that nothing like that exists. There’s no reason why it should exist. There is not some elaborate clever reason why it exists but nobody can see it. It takes a lot of power to do something to the current world that prevents any other AGI from coming into existence; nothing which can do that is passively safe in virtue of its weakness. If you can’t solve the problem right now (which you can’t, because you’re opposed to other actors who don’t want to be solved and those actors are on roughly the same level as you) then you are resorting to some cognitive system that can do things you could not figure out how to do yourself, that you were not close to figuring out because you are not close to being able to, for example, burn all GPUs. Burning all GPUs would actually stop Facebook AI Research from destroying the world six months later; weaksauce Overton-abiding stuff about ‘improving public epistemology by setting GPT-4 loose on Twitter to provide scientifically literate arguments about everything’ will be cool but will not actually prevent Facebook AI Research from destroying the world six months later, or some eager open-source collaborative from destroying the world a year later if you manage to stop FAIR specifically. There are no pivotal weak acts.

8. The best and easiest-found-by-optimization algorithms for solving problems we want an AI to solve, readily generalize to problems we’d rather the AI not solve; you can’t build a system that only has the capability to drive red cars and not blue cars, because all red-car-driving algorithms generalize to the capability to drive blue cars.

9. The builders of a safe system, by hypothesis on such a thing being possible, would need to operate their system in a regime where it has the capability to kill everybody or make itself even more dangerous, but has been successfully designed to not do that. Running AGIs doing something pivotal are not passively safe, they’re the equivalent of nuclear cores that require actively maintained design properties to not go supercritical and melt down.

### Section B:

Okay, but as we all know, modern machine learning is like a genie where you just give it a wish, right? Expressed as some mysterious thing called a ‘loss function’, but which is basically just equivalent to an English wish phrasing, right? And then if you pour in enough computing power you get your wish, right? So why not train a giant stack of transformer layers on a dataset of agents doing nice things and not bad things, throw in the word ‘corrigibility’ somewhere, crank up that computing power, and get out an aligned AGI?

Section B.1: The distributional leap.

10. You can’t train alignment by running lethally dangerous cognitions, observing whether the outputs kill or deceive or corrupt the operators, assigning a loss, and doing supervised learning. On anything like the standard ML paradigm, you would need to somehow generalize optimization-for-alignment you did in safe conditions, across a big distributional shift to dangerous conditions. (Some generalization of this seems like it would have to be true even outside that paradigm; you wouldn’t be working on a live unaligned superintelligence to align it.) This alone is a point that is sufficient to kill a lot of naive proposals from people who never did or could concretely sketch out any specific scenario of what training they’d do, in order to align what output—which is why, of course, they never concretely sketch anything like that. Powerful AGIs doing dangerous things that will kill you if misaligned, must have an alignment property that generalized far out-of-distribution from safer building/​training operations that didn’t kill you. This is where a huge amount of lethality comes from on anything remotely resembling the present paradigm. Unaligned operation at a dangerous level of intelligence*capability will kill you; so, if you’re starting with an unaligned system and labeling outputs in order to get it to learn alignment, the training regime or building regime must be operating at some lower level of intelligence*capability that is passively safe, where its currently-unaligned operation does not pose any threat. (Note that anything substantially smarter than you poses a threat given any realistic level of capability. Eg, “being able to produce outputs that humans look at” is probably sufficient for a generally much-smarter-than-human AGI to navigate its way out of the causal systems that are humans, especially in the real world where somebody trained the system on terabytes of Internet text, rather than somehow keeping it ignorant of the latent causes of its source code and training environments.)

11. If cognitive machinery doesn’t generalize far out of the distribution where you did tons of training, it can’t solve problems on the order of ‘build nanotechnology’ where it would be too expensive to run a million training runs of failing to build nanotechnology. There is no pivotal act this weak; there’s no known case where you can entrain a safe level of ability on a safe environment where you can cheaply do millions of runs, and deploy that capability to save the world and prevent the next AGI project up from destroying the world two years later. Pivotal weak acts like this aren’t known, and not for want of people looking for them. So, again, you end up needing alignment to generalize way out of the training distribution—not just because the training environment needs to be safe, but because the training environment probably also needs to be cheaper than evaluating some real-world domain in which the AGI needs to do some huge act. You don’t get 1000 failed tries at burning all GPUs—because people will notice, even leaving out the consequences of capabilities success and alignment failure.

12. Operating at a highly intelligent level is a drastic shift in distribution from operating at a less intelligent level, opening up new external options, and probably opening up even more new internal choices and modes. Problems that materialize at high intelligence and danger levels may fail to show up at safe lower levels of intelligence, or may recur after being suppressed by a first patch.

13. Many alignment problems of superintelligence will not naturally appear at pre-dangerous, passively-safe levels of capability. Consider the internal behavior ‘change your outer behavior to deliberately look more aligned and deceive the programmers, operators, and possibly any loss functions optimizing over you’. This problem is one that will appear at the superintelligent level; if, being otherwise ignorant, we guess that it is among the median such problems in terms of how early it naturally appears in earlier systems, then around half of the alignment problems of superintelligence will first naturally materialize after that one first starts to appear. Given correct foresight of which problems will naturally materialize later, one could try to deliberately materialize such problems earlier, and get in some observations of them. This helps to the extent (a) that we actually correctly forecast all of the problems that will appear later, or some superset of those; (b) that we succeed in preemptively materializing a superset of problems that will appear later; and (c) that we can actually solve, in the earlier laboratory that is out-of-distribution for us relative to the real problems, those alignment problems that would be lethal if we mishandle them when they materialize later. Anticipating all of the really dangerous ones, and then successfully materializing them, in the correct form for early solutions to generalize over to later solutions, sounds possibly kinda hard.

14. Some problems, like ‘the AGI has an option that (looks to it like) it could successfully kill and replace the programmers to fully optimize over its environment’, seem like their natural order of appearance could be that they first appear only in fully dangerous domains. Really actually having a clear option to brain-level-persuade the operators or escape onto the Internet, build nanotech, and destroy all of humanity—in a way where you’re fully clear that you know the relevant facts, and estimate only a not-worth-it low probability of learning something which changes your preferred strategy if you bide your time another month while further growing in capability—is an option that first gets evaluated for real at the point where an AGI fully expects it can defeat its creators. We can try to manifest an echo of that apparent scenario in earlier toy domains. Trying to train by gradient descent against that behavior, in that toy domain, is something I’d expect to produce not-particularly-coherent local patches to thought processes, which would break with near-certainty inside a superintelligence generalizing far outside the training distribution and thinking very different thoughts. Also, programmers and operators themselves, who are used to operating in not-fully-dangerous domains, are operating out-of-distribution when they enter into dangerous ones; our methodologies may at that time break.

15. Fast capability gains seem likely, and may break lots of previous alignment-required invariants simultaneously. Given otherwise insufficient foresight by the operators, I’d expect a lot of those problems to appear approximately simultaneously after a sharp capability gain. See, again, the case of human intelligence. We didn’t break alignment with the ‘inclusive reproductive fitness’ outer loss function, immediately after the introduction of farming—something like 40,000 years into a 50,000 year Cro-Magnon takeoff, as was itself running very quickly relative to the outer optimization loop of natural selection. Instead, we got a lot of technology more advanced than was in the ancestral environment, including contraception, in one very fast burst relative to the speed of the outer optimization loop, late in the general intelligence game. We started reflecting on ourselves a lot more, started being programmed a lot more by cultural evolution, and lots and lots of assumptions underlying our alignment in the ancestral training environment broke simultaneously. (People will perhaps rationalize reasons why this abstract description doesn’t carry over to gradient descent; eg, “gradient descent has less of an information bottleneck”. My model of this variety of reader has an inside view, which they will label an outside view, that assigns great relevance to some other data points that are not observed cases of an outer optimization loop producing an inner general intelligence, and assigns little importance to our one data point actually featuring the phenomenon in question. When an outer optimization loop actually produced general intelligence, it broke alignment after it turned general, and did so relatively late in the game of that general intelligence accumulating capability and knowledge, almost immediately before it turned ‘lethally’ dangerous relative to the outer optimization loop of natural selection. Consider skepticism, if someone is ignoring this one warning, especially if they are not presenting equally lethal and dangerous things that they say will go wrong instead.)

Section B.2: Central difficulties of outer and inner alignment.

16. Even if you train really hard on an exact loss function, that doesn’t thereby create an explicit internal representation of the loss function inside an AI that then continues to pursue that exact loss function in distribution-shifted environments. Humans don’t explicitly pursue inclusive genetic fitness; outer optimization even on a very exact, very simple loss function doesn’t produce inner optimization in that direction. This happens in practice in real life, it is what happened in the only case we know about, and it seems to me that there are deep theoretical reasons to expect it to happen again: the first semi-outer-aligned solutions found, in the search ordering of a real-world bounded optimization process, are not inner-aligned solutions. This is sufficient on its own, even ignoring many other items on this list, to trash entire categories of naive alignment proposals which assume that if you optimize a bunch on a loss function calculated using some simple concept, you get perfect inner alignment on that concept.

17. More generally, a superproblem of ‘outer optimization doesn’t produce inner alignment’ is that on the current optimization paradigm there is no general idea of how to get particular inner properties into a system, or verify that they’re there, rather than just observable outer ones you can run a loss function over. This is a problem when you’re trying to generalize out of the original training distribution, because, eg, the outer behaviors you see could have been produced by an inner-misaligned system that is deliberately producing outer behaviors that will fool you. We don’t know how to get any bits of information into the inner system rather than the outer behaviors, in any systematic or general way, on the current optimization paradigm.

18. There’s no reliable Cartesian-sensory ground truth (reliable loss-function-calculator) about whether an output is ‘aligned’, because some outputs destroy (or fool) the human operators and produce a different environmental causal chain behind the externally-registered loss function. That is, if you show an agent a reward signal that’s currently being generated by humans, the signal is not in general a reliable perfect ground truth about how aligned an action was, because another way of producing a high reward signal is to deceive, corrupt, or replace the human operators with a different causal system which generates that reward signal. When you show an agent an environmental reward signal, you are not showing it something that is a reliable ground truth about whether the system did the thing you wanted it to do; even if it ends up perfectly inner-aligned on that reward signal, or learning some concept that exactly corresponds to ‘wanting states of the environment which result in a high reward signal being sent’, an AGI strongly optimizing on that signal will kill you, because the sensory reward signal was not a ground truth about alignment (as seen by the operators).

19. More generally, there is no known way to use the paradigm of loss functions, sensory inputs, and/​or reward inputs, to optimize anything within a cognitive system to point at particular things within the environment—to point to latent events and objects and properties in the environment, rather than relatively shallow functions of the sense data and reward. This isn’t to say that nothing in the system’s goal (whatever goal accidentally ends up being inner-optimized over) could ever point to anything in the environment by accident. Humans ended up pointing to their environments at least partially, though we’ve got lots of internally oriented motivational pointers as well. But insofar as the current paradigm works at all, the on-paper design properties say that it only works for aligning on known direct functions of sense data and reward functions. All of these kill you if optimized-over by a sufficiently powerful intelligence, because they imply strategies like ‘kill everyone in the world using nanotech to strike before they know they’re in a battle, and have control of your reward button forever after’. It just isn’t true that we know a function on webcam input such that every world with that webcam showing the right things is safe for us creatures outside the webcam. This general problem is a fact about the territory, not the map; it’s a fact about the actual environment, not the particular optimizer, that lethal-to-us possibilities exist in some possible environments underlying every given sense input.

20. Human operators are fallible, breakable, and manipulable. Human raters make systematic errors—regular, compactly describable, predictable errors. To faithfully learn a function from ‘human feedback’ is to learn (from our external standpoint) an unfaithful description of human preferences, with errors that are not random (from the outside standpoint of what we’d hoped to transfer). If you perfectly learn and perfectly maximize the referent of rewards assigned by human operators, that kills them. It’s a fact about the territory, not the map—about the environment, not the optimizer—that the best predictive explanation for human answers is one that predicts the systematic errors in our responses, and therefore is a psychological concept that correctly predicts the higher scores that would be assigned to human-error-producing cases.

21. There’s something like a single answer, or a single bucket of answers, for questions like ‘What’s the environment really like?’ and ‘How do I figure out the environment?’ and ‘Which of my possible outputs interact with reality in a way that causes reality to have certain properties?‘, where a simple outer optimization loop will straightforwardly shove optimizees into this bucket. When you have a wrong belief, reality hits back at your wrong predictions. When you have a broken belief-updater, reality hits back at your broken predictive mechanism via predictive losses, and a gradient descent update fixes the problem in a simple way that can easily cohere with all the other predictive stuff. In contrast, when it comes to a choice of utility function, there are unbounded degrees of freedom and multiple reflectively coherent fixpoints. Reality doesn’t ‘hit back’ against things that are locally aligned with the loss function on a particular range of test cases, but globally misaligned on a wider range of test cases. This is the very abstract story about why hominids, once they finally started to generalize, generalized their capabilities to Moon landings, but their inner optimization no longer adhered very well to the outer-optimization goal of ‘relative inclusive reproductive fitness’ - even though they were in their ancestral environment optimized very strictly around this one thing and nothing else. This abstract dynamic is something you’d expect to be true about outer optimization loops on the order of both ‘natural selection’ and ‘gradient descent’. The central result: Capabilities generalize further than alignment once capabilities start to generalize far.

22. There’s a relatively simple core structure that explains why complicated cognitive machines work; which is why such a thing as general intelligence exists and not just a lot of unrelated special-purpose solutions; which is why capabilities generalize after outer optimization infuses them into something that has been optimized enough to become a powerful inner optimizer. The fact that this core structure is simple and relates generically to low-entropy high-structure environments is why humans can walk on the Moon. There is no analogous truth about there being a simple core of alignment, especially not one that is even easier for gradient descent to find than it would have been for natural selection to just find ‘want inclusive reproductive fitness’ as a well-generalizing solution within ancestral humans. Therefore, capabilities generalize further out-of-distribution than alignment, once they start to generalize at all.

23. Corrigibility is anti-natural to consequentialist reasoning; “you can’t bring the coffee if you’re dead” for almost every kind of coffee. We (MIRI) tried and failed to find a coherent formula for an agent that would let itself be shut down (without that agent actively trying to get shut down). Furthermore, many anti-corrigible lines of reasoning like this may only first appear at high levels of intelligence.

24. There are two fundamentally different approaches you can potentially take to alignment, which are unsolvable for two different sets of reasons; therefore, by becoming confused and ambiguating between the two approaches, you can confuse yourself about whether alignment is necessarily difficult. The first approach is to build a CEV-style Sovereign which wants exactly what we extrapolated-want and is therefore safe to let optimize all the future galaxies without it accepting any human input trying to stop it. The second course is to build corrigible AGI which doesn’t want exactly what we want, and yet somehow fails to kill us and take over the galaxies despite that being a convergent incentive there.

1. The first thing generally, or CEV specifically, is unworkable because the complexity of what needs to be aligned or meta-aligned for our Real Actual Values is far out of reach for our FIRST TRY at AGI. Yes I mean specifically that the dataset, meta-learning algorithm, and what needs to be learned, is far out of reach for our first try. It’s not just non-hand-codable, it is unteachable on-the-first-try because the thing you are trying to teach is too weird and complicated.

2. The second thing looks unworkable (less so than CEV, but still lethally unworkable) because corrigibility runs actively counter to instrumentally convergent behaviors within a core of general intelligence (the capability that generalizes far out of its original distribution). You’re not trying to make it have an opinion on something the core was previously neutral on. You’re trying to take a system implicitly trained on lots of arithmetic problems until its machinery started to reflect the common coherent core of arithmetic, and get it to say that as a special case 222 + 222 = 555. You can maybe train something to do this in a particular training distribution, but it’s incredibly likely to break when you present it with new math problems far outside that training distribution, on a system which successfully generalizes capabilities that far at all.

Section B.3: Central difficulties of sufficiently good and useful transparency /​ interpretability.

25. We’ve got no idea what’s actually going on inside the giant inscrutable matrices and tensors of floating-point numbers. Drawing interesting graphs of where a transformer layer is focusing attention doesn’t help if the question that needs answering is “So was it planning how to kill us or not?”

26. Even if we did know what was going on inside the giant inscrutable matrices while the AGI was still too weak to kill us, this would just result in us dying with more dignity, if DeepMind refused to run that system and let Facebook AI Research destroy the world two years later. Knowing that a medium-strength system of inscrutable matrices is planning to kill us, does not thereby let us build a high-strength system of inscrutable matrices that isn’t planning to kill us.

27. When you explicitly optimize against a detector of unaligned thoughts, you’re partially optimizing for more aligned thoughts, and partially optimizing for unaligned thoughts that are harder to detect. Optimizing against an interpreted thought optimizes against interpretability.

28. The AGI is smarter than us in whatever domain we’re trying to operate it inside, so we cannot mentally check all the possibilities it examines, and we cannot see all the consequences of its outputs using our own mental talent. A powerful AI searches parts of the option space we don’t, and we can’t foresee all its options.

29. The outputs of an AGI go through a huge, not-fully-known-to-us domain (the real world) before they have their real consequences. Human beings cannot inspect an AGI’s output to determine whether the consequences will be good.

30. Any pivotal act that is not something we can go do right now, will take advantage of the AGI figuring out things about the world we don’t know so that it can make plans we wouldn’t be able to make ourselves. It knows, at the least, the fact we didn’t previously know, that some action sequence results in the world we want. Then humans will not be competent to use their own knowledge of the world to figure out all the results of that action sequence. An AI whose action sequence you can fully understand all the effects of, before it executes, is much weaker than humans in that domain; you couldn’t make the same guarantee about an unaligned human as smart as yourself and trying to fool you. There is no pivotal output of an AGI that is humanly checkable and can be used to safely save the world but only after checking it; this is another form of pivotal weak act which does not exist.

31. A strategically aware intelligence can choose its visible outputs to have the consequence of deceiving you, including about such matters as whether the intelligence has acquired strategic awareness; you can’t rely on behavioral inspection to determine facts about an AI which that AI might want to deceive you about. (Including how smart it is, or whether it’s acquired strategic awareness.)

32. Human thought partially exposes only a partially scrutable outer surface layer. Words only trace our real thoughts. Words are not an AGI-complete data representation in its native style. The underparts of human thought are not exposed for direct imitation learning and can’t be put in any dataset. This makes it hard and probably impossible to train a powerful system entirely on imitation of human words or other human-legible contents, which are only impoverished subsystems of human thoughts; unless that system is powerful enough to contain inner intelligences figuring out the humans, and at that point it is no longer really working as imitative human thought.

33. The AI does not think like you do, the AI doesn’t have thoughts built up from the same concepts you use, it is utterly alien on a staggering scale. Nobody knows what the hell GPT-3 is thinking, not only because the matrices are opaque, but because the stuff within that opaque container is, very likely, incredibly alien—nothing that would translate well into comprehensible human thinking, even if we could see past the giant wall of floating-point numbers to what lay behind.

Section B.4: Miscellaneous unworkable schemes.

34. Coordination schemes between superintelligences are not things that humans can participate in (eg because humans can’t reason reliably about the code of superintelligences); a “multipolar” system of 20 superintelligences with different utility functions, plus humanity, has a natural and obvious equilibrium which looks like “the 20 superintelligences cooperate with each other but not with humanity”.

35. Schemes for playing “different” AIs off against each other stop working if those AIs advance to the point of being able to coordinate via reasoning about (probability distributions over) each others’ code. Any system of sufficiently intelligent agents can probably behave as a single agent, even if you imagine you’re playing them against each other. Eg, if you set an AGI that is secretly a paperclip maximizer, to check the output of a nanosystems designer that is secretly a staples maximizer, then even if the nanosystems designer is not able to deduce what the paperclip maximizer really wants (namely paperclips), it could still logically commit to share half the universe with any agent checking its designs if those designs were allowed through, if the checker-agent can verify the suggester-system’s logical commitment and hence logically depend on it (which excludes human-level intelligences). Or, if you prefer simplified catastrophes without any logical decision theory, the suggester could bury in its nanosystem design the code for a new superintelligence that will visibly (to a superhuman checker) divide the universe between the nanosystem designer and the design-checker.

36. What makes an air conditioner ‘magic’ from the perspective of say the thirteenth century, is that even if you correctly show them the design of the air conditioner in advance, they won’t be able to understand from seeing that design why the air comes out cold; the design is exploiting regularities of the environment, rules of the world, laws of physics, that they don’t know about. The domain of human thought and human brains is very poorly understood by us, and exhibits phenomena like optical illusions, hypnosis, psychosis, mania, or simple afterimages produced by strong stimuli in one place leaving neural effects in another place. Maybe a superintelligence couldn’t defeat a human in a very simple realm like logical tic-tac-toe; if you’re fighting it in an incredibly complicated domain you understand poorly, like human minds, you should expect to be defeated by ‘magic’ in the sense that even if you saw its strategy you would not understand why that strategy worked. AI-boxing can only work on relatively weak AGIs; the human operators are not secure systems.

### Section C:

Okay, those are some significant problems, but lots of progress is being made on solving them, right? There’s a whole field calling itself “AI Safety” and many major organizations are expressing Very Grave Concern about how “safe” and “ethical” they are?

37. There’s a pattern that’s played out quite often, over all the times the Earth has spun around the Sun, in which some bright-eyed young scientist, young engineer, young entrepreneur, proceeds in full bright-eyed optimism to challenge some problem that turns out to be really quite difficult. Very often the cynical old veterans of the field try to warn them about this, and the bright-eyed youngsters don’t listen, because, like, who wants to hear about all that stuff, they want to go solve the problem! Then this person gets beaten about the head with a slipper by reality as they find out that their brilliant speculative theory is wrong, it’s actually really hard to build the thing because it keeps breaking, and society isn’t as eager to adopt their clever innovation as they might’ve hoped, in a process which eventually produces a new cynical old veteran. Which, if not literally optimal, is I suppose a nice life cycle to nod along to in a nature-show sort of way. Sometimes you do something for the first time and there are no cynical old veterans to warn anyone and people can be really optimistic about how it will go; eg the initial Dartmouth Summer Research Project on Artificial Intelligence in 1956: “An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves. We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer.” This is less of a viable survival plan for your planet if the first major failure of the bright-eyed youngsters kills literally everyone before they can predictably get beaten about the head with the news that there were all sorts of unforeseen difficulties and reasons why things were hard. You don’t get any cynical old veterans, in this case, because everybody on Earth is dead. Once you start to suspect you’re in that situation, you have to do the Bayesian thing and update now to the view you will predictably update to later: realize you’re in a situation of being that bright-eyed person who is going to encounter Unexpected Difficulties later and end up a cynical old veteran—or would be, except for the part where you’ll be dead along with everyone else. And become that cynical old veteran right away, before reality whaps you upside the head in the form of everybody dying and you not getting to learn. Everyone else seems to feel that, so long as reality hasn’t whapped them upside the head yet and smacked them down with the actual difficulties, they’re free to go on living out the standard life-cycle and play out their role in the script and go on being bright-eyed youngsters; there’s no cynical old veterans to warn them otherwise, after all, and there’s no proof that everything won’t go beautifully easy and fine, given their bright-eyed total ignorance of what those later difficulties could be.

38. It does not appear to me that the field of ‘AI safety’ is currently being remotely productive on tackling its enormous lethal problems. These problems are in fact out of reach; the contemporary field of AI safety has been selected to contain people who go to work in that field anyways. Almost all of them are there to tackle problems on which they can appear to succeed and publish a paper claiming success; if they can do that and get funded, why would they embark on a much more unpleasant project of trying something harder that they’ll fail at, just so the human species can die with marginally more dignity? This field is not making real progress and does not have a recognition function to distinguish real progress if it took place. You could pump a billion dollars into it and it would produce mostly noise to drown out what little progress was being made elsewhere.

39. I figured this stuff out using the null string as input, and frankly, I have a hard time myself feeling hopeful about getting real alignment work out of somebody who previously sat around waiting for somebody else to input a persuasive argument into them. This ability to “notice lethal difficulties without Eliezer Yudkowsky arguing you into noticing them” currently is an opaque piece of cognitive machinery to me, I do not know how to train it into others. It probably relates to ‘security mindset’, and a mental motion where you refuse to play out scripts, and being able to operate in a field that’s in a state of chaos.

40. “Geniuses” with nice legible accomplishments in fields with tight feedback loops where it’s easy to determine which results are good or bad right away, and so validate that this person is a genius, are (a) people who might not be able to do equally great work away from tight feedback loops, (b) people who chose a field where their genius would be nicely legible even if that maybe wasn’t the place where humanity most needed a genius, and (c) probably don’t have the mysterious gears simply because they’re rare. You cannot just pay 5 million apiece to a bunch of legible geniuses from other fields and expect to get great alignment work out of them. They probably do not know where the real difficulties are, they probably do not understand what needs to be done, they cannot tell the difference between good and bad work, and the funders also can’t tell without me standing over their shoulders evaluating everything, which I do not have the physical stamina to do. I concede that real high-powered talents, especially if they’re still in their 20s, genuinely interested, and have done their reading, are people who, yeah, fine, have higher probabilities of making core contributions than a random bloke off the street. But I’d have more hope—not significant hope, but more hope—in separating the concerns of (a) credibly promising to pay big money retrospectively for good work to anyone who produces it, and (b) venturing prospective payments to somebody who is predicted to maybe produce good work later. 41. Reading this document cannot make somebody a core alignment researcher. That requires, not the ability to read this document and nod along with it, but the ability to spontaneously write it from scratch without anybody else prompting you; that is what makes somebody a peer of its author. It’s guaranteed that some of my analysis is mistaken, though not necessarily in a hopeful direction. The ability to do new basic work noticing and fixing those flaws is the same ability as the ability to write this document before I published it, which nobody apparently did, despite my having had other things to do than write this up for the last five years or so. Some of that silence may, possibly, optimistically, be due to nobody else in this field having the ability to write things comprehensibly—such that somebody out there had the knowledge to write all of this themselves, if they could only have written it up, but they couldn’t write, so didn’t try. I’m not particularly hopeful of this turning out to be true in real life, but I suppose it’s one possible place for a “positive model violation” (miracle). The fact that, twenty-one years into my entering this death game, seven years into other EAs noticing the death game, and two years into even normies starting to notice the death game, it is still Eliezer Yudkowsky writing up this list, says that humanity still has only one gamepiece that can do that. I knew I did not actually have the physical stamina to be a star researcher, I tried really really hard to replace myself before my health deteriorated further, and yet here I am writing this. That’s not what surviving worlds look like. 42. There’s no plan. Surviving worlds, by this point, and in fact several decades earlier, have a plan for how to survive. It is a written plan. The plan is not secret. In this non-surviving world, there are no candidate plans that do not immediately fall to Eliezer instantly pointing at the giant visible gaping holes in that plan. Or if you don’t know who Eliezer is, you don’t even realize you need a plan, because, like, how would a human being possibly realize that without Eliezer yelling at them? It’s not like people will yell at themselves about prospective alignment difficulties, they don’t have an internal voice of caution. So most organizations don’t have plans, because I haven’t taken the time to personally yell at them. ‘Maybe we should have a plan’ is deeper alignment mindset than they possess without me standing constantly on their shoulder as their personal angel pleading them into… continued noncompliance, in fact. Relatively few are aware even that they should, to look better, produce a pretend plan that can fool EAs too ‘modest’ to trust their own judgments about seemingly gaping holes in what serious-looking people apparently believe. 43. This situation you see when you look around you is not what a surviving world looks like. The worlds of humanity that survive have plans. They are not leaving to one tired guy with health problems the entire responsibility of pointing out real and lethal problems proactively. Key people are taking internal and real responsibility for finding flaws in their own plans, instead of considering it their job to propose solutions and somebody else’s job to prove those solutions wrong. That world started trying to solve their important lethal problems earlier than this. Half the people going into string theory shifted into AI alignment instead and made real progress there. When people suggest a planetarily-lethal problem that might materialize later—there’s a lot of people suggesting those, in the worlds destined to live, and they don’t have a special status in the field, it’s just what normal geniuses there do—they’re met with either solution plans or a reason why that shouldn’t happen, not an uncomfortable shrug and ‘How can you be sure that will happen’ /​ ‘There’s no way you could be sure of that now, we’ll have to wait on experimental evidence.’ A lot of those better worlds will die anyways. It’s a genuinely difficult problem, to solve something like that on your first try. But they’ll die with more dignity than this. • 8 Jun 2022 22:34 UTC LW: 204 AF: 65 52 ∶ 7 AF That requires, not the ability to read this document and nod along with it, but the ability to spontaneously write it from scratch without anybody else prompting you; that is what makes somebody a peer of its author. It’s guaranteed that some of my analysis is mistaken, though not necessarily in a hopeful direction. The ability to do new basic work noticing and fixing those flaws is the same ability as the ability to write this document before I published it, which nobody apparently did, despite my having had other things to do than write this up for the last five years or so. Some of that silence may, possibly, optimistically, be due to nobody else in this field having the ability to write things comprehensibly—such that somebody out there had the knowledge to write all of this themselves, if they could only have written it up, but they couldn’t write, so didn’t try. I’m not particularly hopeful of this turning out to be true in real life, but I suppose it’s one possible place for a “positive model violation” (miracle). The fact that, twenty-one years into my entering this death game, seven years into other EAs noticing the death game, and two years into even normies starting to notice the death game, it is still Eliezer Yudkowsky writing up this list, says that humanity still has only one gamepiece that can do that. I knew I did not actually have the physical stamina to be a star researcher, I tried really really hard to replace myself before my health deteriorated further, and yet here I am writing this. That’s not what surviving worlds look like. To say that somebody else should have written up this list before is such a ridiculously unfair criticism. This is an assorted list of some thoughts which are relevant to AI alignment—just by the combinatorics of how many such thoughts there are and how many you chose to include in this list, of course nobody will have written up something like it before. Every time anybody writes up any overview of AI safety, they have to make tradeoffs between what they want to include and what they don’t want to include that will inevitably leave some things off and include some things depending on what the author personally believes is most important/​relevant to say—ensuring that all such introductions will always inevitably cover somewhat different material. Furthermore, many of these are responses to particular bad alignment plans, of which there are far too many to expect anyone to have previously written up specific responses to. Nevertheless, I am confident that every core technical idea in this post has been written about before by either me, Paul Christiano, Richard Ngo, or Scott Garrabrant. Certainly, they have been written up in different ways than how Eliezer describes them, but all of the core ideas are there. Let’s go through the list: (1, 2, 4, 15) AGI safety from first principles (3) This is a common concept, see e.g. Homogeneity vs. heterogeneity in AI takeoff scenarios (“Homogeneity makes the alignment of the first advanced AI system absolutely critical (in a similar way to fast/​discontinuous takeoff without the takeoff actually needing to be fast/​discontinuous), since whether the first AI is aligned or not is highly likely tano determine/​be highly correlated with whether all future AIs built after that point are aligned as well.”). (4) This is just answering a particular bad plan. (5, 6, 7) This is just the concept of competitiveness, see e.g. An overview of 11 proposals for building safe advanced AI. (11) Another specific bad plan. (12, 35) Robustness to Scale (21, 22) 2-D Robustness for the concept, Risks from Learned Optimization in Advanced Machine Learning Systems for why it occurs. (25, 26, 27, 29, 31) Acceptability Verification: A Research Agenda (34) Response to a particular bad plan. To spot check the above list, I generated the following three random numbers from 1 − 36 after I wrote the list: 32, 34, 15. Since 34 corresponds to a particular bad plan, I then generated another to replace it: 14. Let’s spot check those three—14, 15, 32—more carefully. (14) Eliezer claims that “Some problems, like ‘the AGI has an option that (looks to it like) it could successfully kill and replace the programmers to fully optimize over its environment’, seem like their natural order of appearance could be that they first appear only in fully dangerous domains.” In Risks from Learned Optimization in Advanced Machine Learning Systems, we say very directly: In current AI systems, a small amount of distributional shift between training and deployment need not be problematic: so long as the difference is small enough in the task-relevant areas, the training distribution does not need to perfectly reflect the deployment distribution. However, this may not be the case for a deceptively aligned mesa-optimizer. If a deceptively aligned mesa-optimizer is sufficiently advanced, it may detect very subtle distributional shifts for the purpose of inferring when the threat of modification has ceased. [...] Some examples of differences that a mesa-optimizer might be able to detect include: • [...] • The presence or absence of good opportunities for the mesa-optimizer to defect against its programmers. (15) Eliezer says “Fast capability gains seem likely, and may break lots of previous alignment-required invariants simultaneously.” Richard says: If AI development proceeds very quickly, then our ability to react appropriately will be much lower. In particular, we should be interested in how long it will take for AGIs to proceed from human-level intelligence to superintelligence, which we’ll call the takeoff period. The history of systems like AlphaStar, AlphaGo and OpenAI Five provides some evidence that this takeoff period will be short: after a long development period, each of them was able to improve rapidly from top amateur level to superhuman performance. A similar phenomenon occurred during human evolution, where it only took us a few million years to become much more intelligent than chimpanzees. In our case one of the key factors was scaling up our brain hardware—which, as I have already discussed, will be much easier for AGIs than it was for humans. While the question of what returns we will get from scaling up hardware and training time is an important one, in the long term the most important question is what returns we should expect from scaling up the intelligence of scientific researchers—because eventually AGIs themselves will be doing the vast majority of research in AI and related fields (in a process I’ve been calling recursive improvement). In particular, within the range of intelligence we’re interested in, will a given increase δ in the intelligence of an AGI increase the intelligence of the best successor that AGI can develop by more than or less than δ? If more, then recursive improvement will eventually speed up the rate of progress in AI research dramatically. Note: for this one, I originally had the link above point to AGI safety from first principles: Superintelligence specifically, but changed it to point to the whole sequence after I realized during the spot-checking that Richard mostly talks about this in the Control section. (32) Eliezer says “This makes it hard and probably impossible to train a powerful system entirely on imitation of human words or other human-legible contents, which are only impoverished subsystems of human thoughts; unless that system is powerful enough to contain inner intelligences figuring out the humans, and at that point it is no longer really working as imitative human thought.” In An overview of 11 proposals for building safe advanced AI, I say: if RL is necessary to do anything powerful and simple language modeling is insufficient, then whether or not language modeling is easier is a moot point. Whether RL is really necessary seems likely to depend on the extent to which it is necessary to explicitly train agents—which is very much an open question. Furthermore, even if agency is required, it could potentially be obtained just by imitating an actor such as a human that already has it rather than training it directly via RL. and the training competitiveness of imitative amplification is likely to depend on whether pure imitation can be turned into a rich enough reward signal to facilitate highly sample-efficient learning. In my opinion, it seems likely that human language imitation (where language includes embedded images, videos, etc.) combined with techniques to improve sample efficiency will be competitive at some tasks—namely highly-cognitive tasks such as general-purpose decision-making—but not at others, such as fine motor control. If that’s true, then as long as the primary economic use cases for AGI fall into the highly-cognitive category, imitative amplification should be training competitive. For a more detailed analysis of this question, see “Outer alignment and imitative amplification.” • 9 Jun 2022 18:43 UTC LW: 32 AF: 10 6 ∶ 0 AFParent I agree this list doesn’t seem to contain much unpublished material, and I think the main value of having it in one numbered list is that “all of it is in one, short place”, and it’s not an “intro to computers can think” and instead is “these are a bunch of the reasons computers thinking is difficult to align”. The thing that I understand to be Eliezer’s “main complaint” is something like: “why does it seem like No One Else is discovering new elements to add to this list?”. Like, I think Risks From Learned Optimization was great, and am glad you and others wrote it! But also my memory is that it was “prompted” instead of “written from scratch”, and I imagine Eliezer reading it more had the sense of “ah, someone made ‘demons’ palatable enough to publish” instead of “ah, I am learning something new about the structure of intelligence and alignment.” [I do think the claim that Eliezer ‘figured it out from the empty string’ doesn’t quite jive with the Yudkowsky’s Coming of Age sequence.] • Nearly empty string of uncommon social inputs. All sorts of empirical inputs, including empirical inputs in the social form of other people observing things. It’s also fair to say that, though they didn’t argue me out of anything, Moravec and Drexler and Ed Regis and Vernor Vinge and Max More could all be counted as social inputs telling me that this was an important thing to look at. • Eliezer’s post here is doing work left undone by the writing you cite. It is a much clearer account of how our mainline looks doomed than you’d see elsewhere, and it’s frank on this point. I think Eliezer wishes these sorts of artifacts were not just things he wrote, like this and “There is no fire alarm”. Also, re your excerpts for (14), (15), and (32), I see Eliezer as saying something meaningfully different in each case. I might elaborate under this comment. • Re (14), I guess the ideas are very similar, where the mesaoptimizer scenario is like a sharp example of the more general concept Eliezer points at, that different classes of difficulties may appear at different capability levels. Re (15), “Fast capability gains seem likely, and may break lots of previous alignment-required invariants simultaneously”, which is about how we may have reasons to expect aligned output that are brittle under rapid capability gain: your quote from Richard is just about “fast capability gain seems possible and likely”, and isn’t about connecting that to increased difficulty in succeeding at the alignment problem? Re (32), I don’t think your quote isn’t talking about the thing Eliezer is talking about, which is that in order to be human level at modelling human-generated text, your AI must be doing something on par with human thought that figures out what humans would say. Your quote just isn’t discussing this, namely that strong imitation requires cognition that is dangerous. So I guess I don’t take much issue with (14) or (15), but I think you’re quite off the mark about (32). In any case, I still have a strong sense that Eliezer is successfully being more on the mark here than the rest of us manage. Kudos of course to you and others that are working on writing things up and figuring things out. Though I remain sympathetic to Eliezer’s complaint. • Thank you, Evan, for living the Virture of Scholarship. Your work is appreciated. • Well, my disorganized list sure wasn’t complete, so why not go ahead and list some of the foreseeable difficulties I left out? Bonus points if any of them weren’t invented by me, though I realize that most people may not realize how much of this entire field is myself wearing various trenchcoats. • 9 Jun 2022 20:30 UTC LW: 222 AF: 60 22 ∶ 0 AFParent Sure—that’s easy enough. Just off the top of my head, here’s five safety concerns that I think are important that I don’t think you included: • The fact that there exist functions that are easier to verify than satisfy ensures that adversarial training can never guarantee the absence of deception. • It is impossible to verify a model’s safety—even given arbitrarily good transparency tools—without access to that model’s training process. For example, you could get a deceptive model that gradient hacks itself in such a way that cryptographically obfuscates its deception. • It is impossible in general to use interpretability tools to select models to have a particular behavioral property. I think this is clear if you just stare at Rice’s theorem enough: checking non-trivial behavioral properties, even with mechanistic access, is in general undecidable. Note, however, that this doesn’t rule out checking a mechanistic property that implies a behavioral property. • Any prior you use to incentivize models to behave in a particular way doesn’t necessarily translate to situations where that model itself runs another search over algorithms. For example, the fastest way to search for algorithms isn’t to search for the fastest algorithm. • Even if a model is trained in a myopic way—or even if a model is in fact myopic in the sense that it only optimizes some single-step objective—such a model can still end up deceiving you, e.g. if it cooperates with other versions of itself. • Consider my vote to be placed that you should turn this into a post, keep going for literally as long as you can, expand things to paragraphs, and branch out beyond things you can easily find links for. (I do think there’s a noticeable extent to which I was trying to list difficulties more central than those, but I also think many people could benefit from reading a list of 100 noncentral difficulties.) • I do think there’s a noticeable extent to which I was trying to list difficulties more central than those Probably people disagree about which things are more central, or as evhub put it: Every time anybody writes up any overview of AI safety, they have to make tradeoffs [...] depending on what the author personally believes is most important/​relevant to say Now FWIW I thought evhub was overly dismissive of (4) in which you made an important meta-point: EY: 4. We can’t just “decide not to build AGI” because GPUs are everywhere, and knowledge of algorithms is constantly being improved and published; 2 years after the leading actor has the capability to destroy the world, 5 other actors will have the capability to destroy the world. The given lethal challenge is to solve within a time limit, driven by the dynamic in which, over time, increasingly weak actors with a smaller and smaller fraction of total computing power, become able to build AGI and destroy the world. Powerful actors all refraining in unison from doing the suicidal thing just delays this time limit—it does not lift it [...] evhub: This is just answering a particular bad plan. But I would add a criticism of my own, that this “List of Lethalities” somehow just takes it for granted that AGI will try to kill us all without ever specifically arguing that case. Instead you just argue vaguely in that direction, in passing, while making broader/​different points: an AGI strongly optimizing on that signal will kill you, because the sensory reward signal was not a ground truth about alignment (???) All of these kill you if optimized-over by a sufficiently powerful intelligence, because they imply strategies like ‘kill everyone in the world using nanotech to strike before they know they’re in a battle, and have control of your reward button forever after’. (I guess that makes sense) If you perfectly learn and perfectly maximize the referent of rewards assigned by human operators, that kills them. (???) Perhaps you didn’t bother because your audience is meant to be people who already believe this? I would at least expect to see it in the intro: “-5. unaligned superintelligences tend to try to kill everyone, here’s why <link>.… −4. all the most obvious proposed solutions to (-5) don’t work, here’s why <link>”. • (Note that these have a theme: you can’t wrangle general computation /​ optimization. That’s why I’m short universal approaches to AI alignment (approaches that aim at making general optimization safe by enforcing universal rules), and long existential approaches (approaches that try to find specific mechanisms that can be analytically seen to do the right thing).) • Eliezer: If you find that (for reasons still left explained) • … selection of code for intentionality is coupled – over the long run, in mostly non-reverse-engineerable ways – to various/​most of the physical/​chemical properties • … of the molecular substrate through which discrete code is necessarily computed/​expressed (via input and output channels of information/​energy packet transmission), then given that • … the properties of the solid-state substrate (e.g. silicon-based hardware) computing AGI’s code • … differ from the properties of the substrate of humans (carbon-based wetware), a conclusion that follows is that • … the intentionality being selected for in AGI over the long run • will diverge from the intentionality that was selected for in humans. • What do you mean by ‘intentionality’? Per SEP, “In philosophy, intentionality is the power of minds and mental states to be about, to represent, or to stand for, things, properties and states of affairs.” So I read your comment as saying, a la Searle, ‘maybe AI can never think like a human because there’s something mysterious and crucial about carbon atoms in particular, or about capital-b Biology, for doing reasoning.’ This seems transparently silly to me—I know of no reasonable argument for thinking carbon differs from silicon on this dimension—and also not relevant to AGI risk. You can protest “but AlphaGo doesn’t really understand Go!” until the cows come home, and it will still beat you at Go. You can protest “but you don’t really understand killer nanobots!” until the cows come home, and superintelligent Unfriendly AI will still build the nanobots and kill you with them. By the same reasoning, Searle-style arguments aren’t grounds for pessimism either. If Friendly AI lacks true intentionality or true consciousness or whatever, it can still do all the same mechanistic operations, and therefore still produce the same desirable good outcomes as if it had human-style intentionality or whatver. • So I read your comment as saying, a la Searle, ‘maybe AI can never think like a human because there’s something mysterious and crucial about carbon atoms in particular, or about capital-b Biology, for doing reasoning.’ That’s not the argument. Give me a few days to write a response. There’s a minefield of possible misinterpretations here. whatever, it can still do all the same mechanistic operations, and therefore still produce the same desirable good outcomes as if it had human-style intentionality or whatver. However, the argumentation does undermine the idea that designing for mechanistic (alignment) operations is going to work. I’ll try and explain why. • If you happen to have time, this paper serves as useful background reading: https://​​royalsocietypublishing.org/​​doi/​​full/​​10.1098/​​rsif.2012.0869 Particularly note the shift from trivial self-replication (e.g. most computer viruses) to non-trivial self-replication (e.g. as through substrate-environment pathways to reproduction). None of this is sufficient for you to guess what the argumentation is (you might be able to capture a bit of it, along with a lot of incorrect and often implicit assumptions we must dig into). If you could call on some patience and openness to new ideas, I would really appreciate it! I am already bracing for a next misinterpretation (which is fine, if we can talk about that). I apologise for that I cannot find a viable way yet to throw out all the argumentation in one go, and also for that this will get a bit disorientating when we go through arguments step-by-step. • Returning to this: Give me a few days to write a response. There’s a minefield of possible misinterpretations here. Key idea: Different basis of existence→ different drives→ different intentions→ different outcomes. @Rob, I wrote up a longer explanation here, which I prefer to discuss with you in private first. Will email you a copy tomorrow in the next weeks. • BTW, with ‘intentionality’, I meant something closer to everyday notions of ‘intentions one has’. Will more precisely define that meaning later. I should have checked for diverging definitions from formal fields. Thanks for catching that. • I’m sorry to hear that your health is poor and you feel that this is all on you. Maybe you’re right about the likelihood of doom, and even if I knew you were, I’d be sorry that it troubles you this way. I think you’ve done an amazing job of building the AI safety field and now, even when the field has a degree of momentum of its own, it does seem to be less focused on doom than it should be, and I think you continuing to push people to focus on doom is valuable. I don’t think its easy to get people to take weird ideas seriously. I’ve had many experiences where I’ve had ideas about how people should change their approach to a project that weren’t particularly far out and (in my view) were right for very straightforward reasons, and yet for the most part I was ignored altogether. What you’ve accomplished in building the AI safety field is amazing because AI doom ideas seemed really crazy when you started talking about them. Nevertheless, I think some of the things you’ve said in this post are counterproductive. Most of the post is good, but insulting people who might contribute to solving the problem is not, nor is demanding that people acknowledge that you are smarter than they are. I’m not telling you that people don’t deserve to be insulted, nor that you have no right to consider yourself smarter than them—I’m telling you that you shouldn’t say it in public. My concrete suggestion is this: if you are criticising or otherwise passing pessimistic judgement on people or a group of people —Give more details about what it is they’ve done to merit this criticism (“pretend plan that can fool EAs too ‘modest’ to trust their own judgments”—what are modest EAs actually doing that you think is wrong? Paying not enough attention to AI doom?) - Avoid talking about yourself (“So most organizations don’t have plans, because I haven’t taken the time to personally yell at them”) Many people are proud, including me. If working in AI safety means I have to be regularly reminded that the fact that I didn’t go into the field sooner will be held as a mark against me, then that is a reason for me not to do it. Maybe not a decisive reason, but it is a reason. If working in AI safety means that you are going to ask me to publicly acknowledge that you’re smarter than me, that’s a reason for me not to do it. Maybe not decisive, but it’s a reason. I think there might be others who feel similarly. If you want people to accept what you’re saying, it helps let people change their minds without embarrassing them. There are plenty of other things to do—many of which, as I’ve said, you seem to be much better at doing than me—but this one is important too. I wonder if you might say something like “anyone turned off by these comments can’t be of any value to the project”. If you think that—I just don’t. There are many, many smart people with dumb motivations, and many of them can do valuable work if they can be motivated to do it. This includes thinking deeply about things they were previously motivated not to think about. You are a key, maybe the key, person in the AI safety field. What you say is attended to people in, around and even disconnected from the field. I don’t think you can reasonably claim that you shouldn’t be this important to the field. I think you should take this fact seriously, and that means exercising discipline in the things you say. I say all this because I think that a decent amount of EA/​AI safety seems to neglect AI doom an unreasonable amount, and certainly the field of AI in general neglects it. I find statements of the type I pointed out above off-putting, and I suspect I’m not alone. • There’s a point here about how fucked things are that I do not know how to convey without saying those things, definitely not briefly or easily. I’ve spent, oh, a fair number of years, being politer than this, and less personal than this, and the end result is that people nod along and go on living their lives. I expect this won’t work either, but at some point you start trying different things instead of the things that have already failed. It’s more dignified if you fail in different ways instead of the same way. • 8 Jun 2022 5:32 UTC 65 points 19 ∶ 1 Parent FWIW you taking off the Mr. Nice guy gloves has actually made me make different life decisions. I’m glad you tried it even if it doesn’t work. • Do whatever you want, obviously, but I just want to clarify that I did not suggest you avoid personally criticising people (only that you avoid vague/​hard to interpret criticism) or saying you think doom is overwhelmingly likely. Some other comments give me a stronger impression than yours that I was asking you in a general sense to be nice, but I’m saying it to you because I figure it mostly matters that you’re clear on this. • There’s a point here about how fucked things are that I do not know how to convey without saying those things, definitely not briefly or easily. You might not have this ability, but surely you know at least one person who does? • I vehemently disagree here, based on my personal and generalizable or not history. I will illustrate with the three turning points of my recent life. First step: I stumbled upon HPMOR, and Eliezer way of looking straight into the irrationality of all our common ways of interacting and thinking was deeply shocking. It made me feel like he was in a sense angrily pointing at me, who worked more like one of the PNJ rather than Harry. I heard him telling me you’re dumb and all your ideals of making intelligent decisions, being the gifted kid and being smarter than everyone are all are just delusions. You’re so out of touch with reality on so many levels, where to even start. This attitude made me embark on a journey to improve myself, read the sequences, pledge on Giving What we can after knowing EA for many years, and overall reassess whether I was striving towards my goal of helping people (spoiler: I was not). Second step: The April fools post also shocked me on so many levels. I was once again deeply struck by the sheer pessimism of this figure I respected so much. After months of reading articles on LessWrong and so many about AI alignment, this was the one that made me terrified in the face of the horrors to come. Somehow this article, maybe by not caring about not hurting people, made me join an AI alignment research group in Berlin. I started investing myself into the problem, working on it regularly, diverting my donations towards effective organizations in the field. It even caused me to publish my first bit of research on preference learning. Third step: Today this post, by not hiding any reality of the issue and striking a lot of ideas down that I was relying on for hope, made me realize I was becoming complacent. Doing a bit of research in the weekend is the way to be able to say “Yeah I participated in solving the issue” once it’s solved, not making sure it is in fact solved. Therefore, based on my experience, not a lot of works made me significantly alter my life decisions. And those who did are all strangely ranting, smack-in-your-face works written by Eliezer. Maybe I’m not the audience to optimize for to solve the problem, but on my side, I need even more smacks in the face, breaking you fantasy style posts. • I disagree strongly. To me it seems that AI safety has long punched below its weight because its proponents are unwilling to be confrontational, and are too reluctant to put moderate social pressure on people doing the activities which AI safety proponents hold to be very extremely bad. It is not a coincidence that among AI safety proponents, Eliezer is both unusually confrontational and unusually successful. This isn’t specific to AI safety. A lot of people in this community generally believe that arguments which make people feel bad are counterproductive because people will be “turned off”. This is false. There are tons of examples of disparaging arguments against bad (or “bad”) behavior that succeed wildly. Such arguments very frequently succeed in instilling individual values like e.g. conscientiousness or honesty. Prominent political movements which use this rhetoric abound. When this website was young, Eliezer and many others participated in an aggressive campaign of discourse against religious ideas, and this campaign accomplished many of its goals. I could name many many more large and small examples. I bet you can too. Obviously this isn’t to say that confrontational and insulting argument is always the best style. Sometimes it’s truth-tracking and sometimes it isn’t. Sometimes it’s persuasive and sometimes it isn’t. Which cases are which is a difficult topic that I won’t get into here (except to briefly mention that it matters a lot whether the reasons given are actually good). Nor is this to say that the “turning people off” effect is completely absent; what I object to is the casual assumption that it outweighs any other effects. (Personally I’m turned off by the soft-gloved style of the parent comment, but I would not claim this necessarily means it’s inappropriate or ineffective—it’s not directed at me!) The point is that this very frequent claim does not match the evidence. Indeed, strong counterevidence is so easy to find that I suspect this is often not people’s real objection. • I think there’s an important distinction between: • Deliberately phrasing things in confrontational or aggressive ways, in the hope that this makes your conversation partner “wake up” or something. • Choosing not to hide real, potentially-important beliefs you have about the world, even though those beliefs are liable to offend people, liable to be disagreed with, etc. Either might be justifiable, but I’m a lot more wary of heuristics like “it’s never OK to talk about individuals’ relative proficiency at things, even if it feels very cruxy and important, because people just find the topic too triggering” than of heuristics like “it’s never OK to say things in ways that sound shouty or aggressive”. I think cognitive engines can much more easily get by self-censoring their tone than self-censoring what topics are permissible to think or talk about. • How is “success” measured among AI safety proponents? • This kind of post scares away the person who will be the key person in the AI safety field if we define “key person” as the genius main driver behind solving it, not the loudest person. Which is rather unfortunate, because that person is likely to read this post at some point. I don’t believe this post has any “dignity”, whatever weird obscure definition dignity has been given now. It’s more like flailing around in death throes while pointing fingers and lauding yourself than it is a solemn battle stance against an oncoming impossible enemy. For context, I’m not some Eliezer hater, I’m a young person doing an ML masters currently who just got into this space and within the past week have become a huge fan of Eliezer Yudkowsky’s earlier work while simultaneously very disappointed in the recent, fruitless, output. • It seems worth doing a little user research on this to see how it actually affects people. If it is a net positive, then great. If it is a net negative, the question becomes how big of a net negative it is and whether it is worth the extra effort to frame things more nicely. • Strongly agree with this, said more eloquently than I was able to :) • 6 Jun 2022 4:49 UTC 146 points 32 ∶ 0 I’d have more hope—not significant hope, but more hope—in separating the concerns of (a) credibly promising to pay big money retrospectively for good work to anyone who produces it, and (b) venturing prospective payments to somebody who is predicted to maybe produce good work later. I desperately want to make this ecosystem exist, either as part of Manifold Markets, or separately. Some people call it “impact certificates” or “retroactive public goods funding”; I call it “equity for public goods”, or “Manifund” in the specific case. If anyone is interested in: a) Being a retroactive funder for good work (aka bounties, prizes) b) Getting funding through this kind of mechanism (aka income share agreements, angel investment) c) Working on this project full time (full-stack web dev, ops, community management) Please get in touch! Reply here, or message austin@manifold.markets~ • I’m also on a team trying to build impact certificates/​retroactive public goods funding and we are receiving a grant from an FTX Future Fund regrantor to make it happen! If you’re interested in learning more or contributing you can: • Read about our ongoing10,000 retro-funding contest (Austin is graciously contributing to the prize pool)

• Submit an EA Forum Post to this retro-funding contest (before July 1st)

• Join our Discord to chat/​ask questions

• Read/​Comment on our lengthy informational EA forum post “Towards Impact Markets

• 7 Jun 2022 6:25 UTC
LW: 124 AF: 31
31 ∶ 46
AF

It’s as good as time as any to re-iterate my reasons for disagreeing with what I see as the Yudkowskian view of future AI. What follows isn’t intended as a rebuttal of any specific argument in this essay, but merely a pointer that I’m providing for readers, that may help explain why some people might disagree with the conclusion and reasoning contained within.

I’ll provide my cruxes point-by-point,

• I think raw intelligence, while important, is not the primary factor that explains why humanity-as-a-species is much more powerful than chimpanzees-as-a-species. Notably, humans were once much less powerful, in our hunter-gatherer days, but over time, through the gradual process of accumulating technology, knowledge, and culture, humans now possess vast productive capacities that far outstrip our ancient powers.

Similarly, our ability to coordinate through language also plays a huge role in explaining our power compared to other animals. But, on a first approximation, other animals can’t coordinate at all, making this distinction much less impressive. The first AGIs we construct will be born into a culture already capable of coordinating, and sharing knowledge, making the potential power difference between AGI and humans relatively much smaller than between humans and other animals, at least at first.

Consequently, the first slightly smarter-than-human agent will probably not be able to leverage its raw intelligence to unilaterally take over the world, for pretty much the same reason that an individual human would not be able to unilaterally take over a band of chimps, in the state of nature, despite the intelligence advantage of the human.

• There’s a large range of human intelligence, such that it makes sense to talk about AI slowly going from 50th percentile to 99.999th percentile on pretty much any important intellectual task, rather than AI suddenly jumping to superhuman levels after a single major insight. In cases where progress in performance does happen rapidly, the usual reason is that there wasn’t much effort previously being put into getting better at the task.

The case of AlphaGo is instructive here: improving the SOTA on Go bots is not very profitable. We should expect, therefore, that there will be relatively few resources being put into that task, compared to the overall size of the economy. However, if a single rich company, like Google, at some point does decide to invest considerable resources into improving Go performance, then we could easily observe a discontinuity in progress. Yet, this discontinuity in output merely reflects a discontinuity in inputs, not a discontinuity as a response to small changes in those inputs, as is usually a prerequisite for foom in theoretical models.

• Hardware progress and experimentation are much stronger drivers of AI progress than novel theoretical insights. The most impressive insights, like backpropagation and transformers, are probably in our past. And as the field becomes more mature, it will likely become even harder to make important theoretical discoveries.

These points make the primacy of recursive self-improvement, and as a consequence, unipolarity in AI takeoff, less likely in the future development of AI. That’s because hardware progress and AI experimentation are, for the most part, society-wide inputs, which can be contributed by a wide variety of actors, don’t exhibit strong feedback loops on an individual level, and more-or-less have smooth responses to small changes in their inputs. Absent some way of making AI far better via a small theoretical tweak, it seems that we should expect smooth, gradual progress by default, even if overall economic growth becomes very high after the invention of AGI.

• There are strong pressures—including the principle of comparative advantage, diseconomies of scale, and gains from specialization—that incentivize making economic services narrow and modular, rather than general and all-encompassing. Illustratively, a large factory where each worker specializes in their particular role will be much more productive than a factory in which each worker is trained to be a generalist, even though no one understands any particular component of the production process very well.

What is true in human economics will apply to AI services as well. This implies we should expect something like Eric Drexler’s AI perspective, which emphasizes economic production across many agents who trade and produce narrow services, as opposed to monolithic agents that command and control.

• Having seen undeniable, large economic effects from AI, policymakers will eventually realize that AGI is important, and will launch massive efforts to regulate it. The current lack of concern almost certainly reflects the fact that powerful AI hasn’t arrived yet.

There’s a long history of people regulating industries after disasters—like nuclear energy—and, given the above theses, it seems likely that there will be at least a few “warning shots” which will provide a trigger for companies and governments to crack down and invest heavily into making things go the way they want.

(Note that this does not imply any sort of optimism about the effects of these regulations, only that they will exist and will have a large effect on the trajectory of AI)

• The effect of the above points is not to provide us uniform optimism about AI safety, and our collective future. It is true that, if we accept the previous theses, then many of the points in Eliezer’s list of AI lethalities become far less plausible. But, equally, one could view these theses pessimistically, by thinking that they imply the trajectory of future AI is much harder to intervene on, and do anything about, relative to the Yudkowskian view.

• 7 Jun 2022 15:19 UTC
LW: 57 AF: 12
21 ∶ 3
AFParent

Notably, humans were once much less powerful, in our hunter-gatherer days, but over time, through the gradual process of accumulating technology, knowledge, and culture, humans now possess vast productive capacities that far outstrip our ancient powers.

Similarly, our ability to coordinate through language also plays a huge role in explaining our power compared to other animals. But, on a first approximation, other animals can’t coordinate at all, making this distinction much less impressive. The first AGIs we construct will be born into a culture already capable of coordinating, and sharing knowledge, making the potential power difference between AGI and humans relatively much smaller than between humans and other animals, at least at first.

I basically buy the story that human intelligence is less useful that human coordination; i.e. it’s the intelligence of “humanity” the entity that matters, with the intelligence of individual humans relevant only as, like, subcomponents of that entity.

But… shouldn’t this mean you expect AGI civilization to totally dominate human civilization? They can read each other’s source code, and thus trust much more deeply! They can transmit information between them at immense bandwidths! They can clone their minds and directly learn from each other’s experiences!

Like, one scenario I visualize a lot is the NHS having a single ‘DocBot’, i.e. an artificial doctor run on datacenters that provides medical advice and decision-making for everyone in the UK (while still working with nurses and maybe surgeons and so on). Normally I focus on the way that it gets about three centuries of experience treating human patients per day, but imagine the difference in coordination capacity between DocBot and the BMA.

Having seen undeniable, large economic effects from AI, policymakers will eventually realize that AGI is important, and will launch massive efforts to regulate it.

I think everyone expects this, and often disagree on the timescale on which it will arrive. See, for example, Elon Musk’s speech to the US National Governors Association, where he argues that the reactive regulation model will be too slow to handle the crisis.

But I think the even more important disagreement is on whether or not regulations should be expected to work. Ok, so you make it so that only corporations with large compliance departments can run AGI. How does that help? There was a tweet by Matt Yglesias a while ago that I can’t find now, which went something like: “a lot of smart people are worried about AI, and when you ask them what the government can do about it, they have no idea; this is an extremely wild situation from the perspective of a policy person.” A law that says “don’t run the bad code” is predicated on the ability to tell the good code from the bad code, which is the main thing we’re missing and don’t know how to get!

And if you say something like “ok, one major self-driving car accident will be enough to convince everyone to do the Butlerian Jihad and smash all the computers”, that’s really not how it looks to me. Like, the experience of COVID seems a lot like “people who were doing risky research in labs got out in front of everyone else to claim that the lab leak hypothesis was terrible and unscientific, and all of the anti-disinformation machinery was launched to suppress it, and it took a shockingly long time to even be able to raise the hypothesis, and it hasn’t clearly swept the field, and legislation to do something about risky research seems like it definitely isn’t a slam dunk.”

When we get some AI warning signs, I expect there are going to be people with the ability to generate pro-AI disinfo and a strong incentive to do so. I expect there to be significant latent political polarization which will tangle up any attempt to do something useful about it. I expect there won’t be anything like the international coordination that was necessary to set up anti-nuclear-proliferation efforts to set up the probably harder problem of anti-AGI-proliferation efforts.

• 7 Jun 2022 18:55 UTC
13 points
4 ∶ 2
Parent

But… shouldn’t this mean you expect AGI civilization to totally dominate human civilization? They can read each other’s source code, and thus trust much more deeply! They can transmit information between them at immense bandwidths! They can clone their minds and directly learn from each other’s experiences!

This is 100% correct, and part of why I expect the focus on superintelligence, while literally true, is bad for AI outreach. There’s a much simpler (and empirically, in my experience, more convincing) explanation of why we lose to even an AI with an IQ of 110. It is Dath Ilan, and we are Earth. Coordination is difficult for humans and the easy part for AIs.

• I will note that Eliezer wrote That Alien Message a long time ago I think in part to try to convey the issue to this perspective, but it’s mostly about “information-theoretic bounds are probably not going to be tight” in a simulation-y universe instead of “here’s what coordination between computers looks like today”. I do predict the coordination point would be good to include in more of the intro materials.

• 9 Jun 2022 18:46 UTC
LW: 12 AF: 5
5 ∶ 0
AFParent

But… shouldn’t this mean you expect AGI civilization to totally dominate human civilization? They can read each other’s source code, and thus trust much more deeply! They can transmit information between them at immense bandwidths! They can clone their minds and directly learn from each other’s experiences!

I don’t think it’s obvious that this means that AGI is more dangerous, because it means that for a fixed total impact of AGI, the AGI doesn’t have to be as competent at individual thinking (because it leans relatively more on group thinking). And so at the point where the AGIs are becoming very powerful in aggregate, this argument pushes us away from thinking they’re good at individual thinking.

Also, it’s not obvious that early AIs will actually be able to do this if their creators don’t find a way to train them to have this affordance. ML doesn’t currently normally make AIs which can helpfully share mind-states, and it probably requires non-trivial effort to hook them up correctly to be able to share mind-state.

• They can read each other’s source code, and thus trust much more deeply!

Being able to read source code doesn’t automatically increase trust—you also have to be able to verify that the code being shared with you actually governs the AGI’s behavior, despite that AGI’s incentives and abilities to fool you.

(Conditional on the AGIs having strongly aligned goals with each other, sure, this degree of transparency would help them with pure coordination problems.)

• Nice! Thanks! I’ll give my commentary on your commentary, also point by point. Your stuff italicized, my stuff not. Warning: Wall of text incoming! :)

I think raw intelligence, while important, is not the primary factor that explains why humanity-as-a-species is much more powerful than chimpanzees-as-a-species. Notably, humans were once much less powerful, in our hunter-gatherer days, but over time, through the gradual process of accumulating technology, knowledge, and culture, humans now possess vast productive capacities that far outstrip our ancient powers.

Similarly, our ability to coordinate through language also plays a huge role in explaining our power compared to other animals. But, on a first approximation, other animals can’t coordinate at all, making this distinction much less impressive. The first AGIs we construct will be born into a culture already capable of coordinating, and sharing knowledge, making the potential power difference between AGI and humans relatively much smaller than between humans and other animals, at least at first.

I don’t think I understand this argument. Yes, humans can use language to coordinate & benefit from cultural evolution, so an AI that can do that too (but is otherwise unexceptional) would have no advantage. But the possibility we are considering is that AI might be to humans what humans are to monkeys; thus, if the difference between humans and monkeys is greater intelligence allowing them to accumulate language, there might be some similarly important difference between AIs and humans. For example, language is a tool that lets humans learn from the experience of others, but AIs can literally learn from the experience of others—via the mechanism of having many copies that share weights and gradient updates! They can also e.g. graft more neurons onto an existing AI to make it smarter, think at greater serial speed, integrate calculators and other programs into their functioning and learn to use them intuitively as part of their regular thought processes… I won’t be surprised if somewhere in the grab bag of potential advantages AIs have over humans is one (or several added together) as big as the language advantage humans have over monkeys.

Plus, there’s language itself. It’s not a binary, it’s a spectrum; monkeys can use it too, to some small degree. And some humans can use it more/​better than others. Perhaps AIs will (eventually, and perhaps even soon) be better at using language than the best humans.

Consequently, the first slightly smarter-than-human agent will probably not be able to leverage its raw intelligence to unilaterally take over the world, for pretty much the same reason that an individual human would not be able to unilaterally take over a band of chimps, in the state of nature, despite the intelligence advantage of the human.

Here’s how I think we should think about it. Taboo “intelligence.” Instead we just have a list of metrics a, b, c, … z, some of which are overlapping, some of which are subsets of others, etc. One of these metrics, then, is “takeover ability (intellectual component).” This metric, when combined with “takeover ability (resources),” “Takeover ability (social status)” and maybe a few others that track “exogenous” factors about how others treat the AI and what resources it has, combine together to create “overall takeover ability.”

Now, I claim, (1) Takeover is a tournament (blog post TBD, but see my writings about lessons from the conquistadors) and I cite this as support for claim (2) takeover would be easy for AIs, by which I mean, IF AIs were mildly superhuman in the intellectual component of takeover ability, they would plausibly start off with enough of the other components that they would be able to secure more of those other components fairly quickly, stay out of trouble, etc. until they could actually take over—in other words, their overall takeover ability would be mildly superhuman as well.

(I haven’t argued for this much yet but I plan to in future posts. Also I expect some people will find it obvious, and maybe you are one such person.)

Now, how should we think about AI timelines-till-human-level-takeover-ability-(intellectual)?

Same way we think about AI timelines for AGI, or TAI, or whatever. I mean obviously there are differences, but I don’t think we have reason to think that the intellectual component of takeover ability is vastly more difficult than e.g. being human-level AGI, or being able to massively accelerate world GDP, or being able to initiate recursive self-improvement or an R&D acceleration.

I mean it might be. It’s a different metric, after all. But it also might come earlier than those things. It might be easier. And I have plausibility arguments to make for that claim in fact.

So anyhow I claim: We can redo all our timelines analyses with “slightly superhuman takeover ability (intellectual)” as the thing to forecast instead of TAI or AGI or whatever, and get roughly the same numbers. And then (I claim) this is tracking when we should worry about AI takeover. Yes, by a single AI system, if only one exists; if multiple exist then by multiple.

We can hope that we’ll get really good AI alignment research assistants before we get AIs good at taking over… but that’s just a hope at this point; it totally could come in the opposite order and I have arguments that it would.

There’s a large range of human intelligence, such that it makes sense to talk about AI slowly going from 50th percentile to 99.999th percentile on pretty much any intellectual task, rather than AI suddenly jumping to superhuman levels after a single major insight. In cases where progress in performance does happen rapidly, the usual reason is that there wasn’t much effort previously being put into getting better at the task.

The case of AlphaGo is instructive here: improving the SOTA on Go bots is not very profitable. We should expect, therefore, that there will be relatively few resources being put into that task, compared to the overall size of the economy. However, if a single rich company, like Google, at some point does decide to invest considerable resources into improving Go performance, then we could easily observe a discontinuity in progress. Yet, this discontinuity in output merely reflects a discontinuity in inputs, not a discontinuity as a response to small changes in those inputs, as is usually a prerequisite for foom in theoretical models.

Hardware progress and experimentation are much stronger drivers of AI progress than novel theoretical insights. The most impressive insights, like backpropagation and transformers, are probably in our past. And as the field becomes more mature, it will likely become even harder to make important theoretical discoveries.

These points make the primacy of recursive self-improvement, and as a consequence, unipolarity in AI takeoff, less likely in the future development of AI. That’s because hardware progress and AI experimentation are, for the most part, society-wide inputs, which can be contributed by a wide variety of actors, don’t exhibit strong feedback loops on an individual level, and more-or-less have smooth responses to small changes in their inputs. Absent some way of making AI far better via a small theoretical tweak, it seems that we should expect smooth, gradual progress by default, even if overall economic growth becomes very high after the invention of AGI.

I claim this argument is a motte and bailey. The motte is the first three paragraphs, where you give good sensible reasons to think that discontinuities and massive conceptual leaps, while possible, are not typical. The bailey is the last paragraph where you suggest that we can therefore conclude unipolar takeoff is unlikely and that progress will go the way Paul Christiano thinks it’ll go instead of the way Yudkowsky thinks it’ll go. I have sat down to make toy models of what takeoff might look like, and even with zero discontinuities and five-year-spans of time to “cross the human range” the situation looks qualitatively a lot more like Yudkowsky’s story than Christiano’s. Of course you shouldn’t take my word for it, and also just because the one or two models I made looked this way doesn’t mean I’m right, maybe someone with different biases could make different models that would come out differently. But still. (Note: Part of why my models came out this way was that I was assuming stuff happens in 5-15 years from now. Paul Christiano would agree, I think, that given this assumption takeoff would be pretty fast. I haven’t tried to model what things look like on 20+ year timelines.)

There are strong pressures—including the principle of comparative advantage, diseconomies of scale, and gains from specialization—that incentivize making economic services narrow and modular, rather than general and all-encompassing. Illustratively, a large factory where each worker specializes in their particular role will be much more productive than a factory in which each worker is trained to be a generalist, even though no one understands any particular component of the production process very well.

What is true in human economics will apply to AI services as well. This implies we should expect something like Eric Drexler’s AI perspective, which emphasizes economic production across many agents who trade and produce narrow services, as opposed to monolithic agents that command and control.

This may be our biggest disagrement. Drexler’s vision of comprehensive AI services is a beautiful fantasy IMO. Agents are powerful. There will be plenty of AI services, yes, but there will also be AI agents, and those are what we are worried about. And yes it’s theoretically possible to develop the right AI services in advance to help us control the agents when they appear… but we’d best get started building them then, because they aren’t going to build themselves. And eyeballing the progress towards AI agents vs. useful interpretability tools etc., it’s not looking good.

Having seen undeniable, large economic effects from AI, policymakers will eventually realize that AGI is important, and will launch massive efforts to regulate it. The current lack of concern almost certainly reflects the fact that powerful AI hasn’t arrived yet.

There’s a long history of people regulating industries after disasters—like nuclear energy—and, given the above theses, it seems likely that there will be at least a few “warning shots” which will provide a trigger for companies and governments to crack down and invest heavily into making things go the way they want.

(Note that this does not imply any sort of optimism about the effects of these regulations, only that they will exist and will have a large effect on the trajectory of AI)

I agree in principle, but unfortunately it seems like things are going to happen fast enough (over the span of a few years at most) and soon enough (in the next decade or so, NOT in 30 years after the economy has already been transformed by narrow AI systems) that it really doesn’t seem like governments are going to do much by default. We still have the opportunity to plan ahead and get governments to do stuff! But I think if we sit on our asses, nothing of use will happen. (Probably there will be some regulation but it’ll be irrelevant like most regulation is.)

In particular I think that we won’t get any cool exciting scary AI takeover near-misses that cause massive crackdowns on the creation of AIs that could possibly take over, the way we did for nuclear power plants. Why would we? The jargon for this is “Sordid Stumble before Treacherous Turn.” It might happen but we shouldn’t expect it by default I think. Yes, before AIs are smart enough to take over, they will be dumber. But what matters is: Before an AI is smart enough to take over and smart enough to realize this, will there be an AI that can’t take over but thinks it can? And “before” can’t be “two weeks before” either, it probably needs to be more like two months or two years, otherwise the dastardly plan won’t have time to go awry and be caught and argued about and then regulated against. Also the AI in question has to be scarily smart otherwise it’s takeover attempt will fail so early that it won’t be registered as such, it’ll be like GPT-3 lying to users to get reward or Facebook’s recommendation algorithm causing thousands of teenage girls to kill themselves, people will be like “Oh yes this was an error, good thing we train that sort of thing away, see look how the system behaves better now.”

The effect of the above points is not to provide us uniform optimism about AI safety, and our collective future. It is true that, if we accept the previous theses, then many of the points in Eliezer’s list of AI lethalities become far less plausible. But, equally, one could view these theses pessimistically, by thinking that they imply the trajectory of future AI is much harder to intervene on, and do anything about, relative to the Yudkowskian view.

I haven’t gone through the list point by point, I won’t comment on this then. I agree that longer timelines slow takeoff worlds we have less influence over relative to other humans.

• You said you weren’t replying to any specific point Eliezer was making, but I think it’s worth pointing out that when he brings up Alpha Go, he’s not talking about the 2 years it took Google to build a Go-playing AI—remarkable and surprising as that was—but rather the 3 days it took Alpha Zero to go from not knowing anything about the game beyond the basic rules to being better than all humans and the earlier AIs.

• 7 Jun 2022 8:22 UTC
19 points
7 ∶ 0
Parent

I hate how convincing so many different people are. I wish I just had some fairly static, reasoned perspective based on object-level facts and not persuasion strings.

• Note that convincing is a 2-place word. I don’t think I can transfer this ability, but I haven’t really tried, so here’s a shot:

The target is: “reading as dialogue.” Have a world-model. As you read someone else, be simultaneously constructing /​ inferring “their world-model” and holding “your world-model”, noting where you agree and disagree.

If you focus too much on “how would I respond to each line”, you lose the ability to listen and figure out what they’re actually pointing at. If you focus too little on “how would I respond to this”, you lose the ability to notice disagreements, holes, and notes of discord.

The first homework exercise I’d try to printing out something (probably with double-spacing), and writing your thoughts each sentence. “uh huh”, “wait what?”, “yes and”, “no but”, etc.; at the beginning you’re probably going to be alternating between the two moves before you can do them simultaneously.

[Historically, I think I got this both from ‘reading a lot’, including a lot of old books, and also ‘arguing on the internet’ in forum environments that only sort of exist today, which was a helpful feedback loop for the relevant subskills, and of course whatever background factors made me do those activities.]

• Why can’t I delete comments sometimes? >:(

• Users can’t delete their own comments if the comment has been replied to, to avoid disrupting other people’s content. (you can edit it to be blank though, or mark it as retracted)

• 8 Jun 2022 6:41 UTC
LW: 17 AF: 7
4 ∶ 0
AFParent

Some quick thoughts on these points:

• I think the ability for humans to communicate and coordinate is a double edged sword. In particular, it enables the attack vector of dangerous self propagating memes. I expect memetic warfare to play a major role in many of the failure scenarios I can think of. As we’ve seen, even humans are capable of crafting some pretty potent memes, and even defending against human actors is difficult.

• I think it’s likely that the relevant reference class here is research bets rather then the “task” of AGI. An extremely successful research bet could be currently underinvested in, but once it shows promise, discontinuous (relative to the bet) amounts of resources will be dumped into scaling it up, even if the overall investment towards the task as a whole remains continuous. In other words, in this case even though investment into AGI may be continuous (though that might not even hold), discontinuity can occur on the level of specific research bets. Historical examples would include imagenet seeing discontinuous improvement with AlexNet despite continuous investment into image recognition to that point. (Also, for what it’s worth, my personal model of AI doom doesn’t depend heavily on discontinuities existing, though they do make things worse.)

• I think there exist plausible alternative explanations for why capabilities has been primarily driven by compute. For instance, it may be because ML talent is extremely expensive whereas compute gets half as expensive every 18 months or whatever, that it doesn’t make economic sense to figure out compute efficient AGI. Given the fact that humans need orders of magnitude less data and compute than current models, and that the human genome isn’t that big and is mostly not cognition related, it seems plausible that we already have enough hardware for AGI if we had the textbook from the future, though I have fairly low confidence on this point.

• Monolithic agents have the advantage that they’re able to reason about things that involve unlikely connections between extremely disparate fields. I would argue that the current human specialization is at least in part due to constraints about how much information one person can know. It also seems plausible that knowledge can be siloed in ways that make inference cost largely detached from the number of domains the model is competent in. Finally, people have empirically just been really excited about making giant monolithic models. Overall, it seems like there is enough incentive to make monolithic models that it’ll probably be an uphill battle to convince people not to do them.

• Generally agree with the regulation point given the caveat. I do want to point out that since substantive regulation often moves very slowly, especially when there are well funded actors trying to prevent AGI development being regulated, even in non-foom scenarios (months-years) they might not move fast enough (example: think about how slowly climate change related regulations get adopted)

• Thanks a lot for writing this.

These disagreements mainly concern the relative power of future AIs, the polarity of takeoff, takeoff speed, and, in general, the shape of future AIs. Do you also have detailed disagreements about the difficulty of alignment? If anything, the fact that the future unfolds differently in your view should impact future alignment efforts (but you also might have other considerations informing your view on alignment).

You partially answer this in the last point, saying: “But, equally, one could view these theses pessimistically.” But what do you personally think? Are you more pessimistic, more optimistic, or equally pessimistic about humanity’s chances of surviving AI progress? And why?

• Part of what makes it difficult for me to talk about alignment difficultly is that the concept doesn’t fit easily into my paradigm of thinking about the future of AI. If I am correct, for example, that AI services will be modular, marginally more powerful than what comes before, and numerous as opposed to monolithic, then there will not be one alignment problem, but many.

I could talk about potential AI safety principles, healthy cultural norms, and specific engineering issues, but not “a problem” called “aligning the AI” — a soft prerequisite for explaining how difficult “the problem” will be. Put another way, my understanding is that future AI alignment will be continuous with ordinary engineering, like cars and skyscrapers. We don’t ordinarily talk about how hard the problem of building a car is, in some sort of absolute sense, though there are many ways of operationalizing what that could mean.

One question is how costly it is to build a car. We could then compare that cost to the overall consumer benefit that people get from cars, and from that, deduce whether and how many cars will be built. Similarly, we could ask about the size of the “alignment tax” (the cost of aligning an AI above the cost of building AI), and compare it to the benefits we get from aligning AI at all.

My starting point in answering this question is to first emphasize the large size of the benefits: what someone gets if they build AI correctly. We should expect this benefit to be extremely large, and thus, we should also expect people to pay very large amounts to align their AIs, including through government regulation and other social costs.

Will people still fail to align AI services, in various ways, due to the numerous issues, like e.g. mesa misalignment, outer alignment, arising from lack of oversight and transparency? Sure — and I’m uncertain by how much this will occur — but because of the points I gave in my original comment, these seem unlikely to be fatal issues, on a civilizational level. It is perhaps less analogous to nukes than to how car safety sometimes fails (though I do not want to lean heavily on this comparison, as there are real differences too).

Now, there is a real risk in misunderstanding me here. AI values and culture could drift very far from human values over time. And eventually, this could culminate in an existential risk. This is all very vague, but if I were forced to guess the probability of this happening — as in, it’s all game over and we lose as humans — I’d maybe go with 25%.

• Btw, your top-level comment is one of the best comments I’ve come across ever. Probably. Top 5? Idk, I’ll check how I feel tomorrow. Aspiring to read everything you’ve ever written rn.

Incidentally, you mention that

the concept doesn’t fit easily into my paradigm of thinking about the future of AI.

And I’ve been thinking lately about how important it is to prioritise original thinking before you’ve consumed all the established literature in an active field of research.[1] If you manage to diverge early, the novelty of your perspective compounds over time (feel free to ask about my model) and you’re more likely to end up with a productively different paradigm from what’s already out there.

Did you ever feel embarrassed trying to think for yourself when you didn’t feel like you had read enough? Or, did you feel like other people might have expected you to feel embarrassed for how seriously you took your original thoughts, given how early you were in your learning arc?

1. ^

I’m not saying you haven’t. I’m just guessing that you acquired your paradigm by doing original thinking early, and thus had the opportunity to diverge early, rather than greedily over-prioritising the consumption of existing literature in order to “reach the frontier”. Once having hastily consumed someone else’s paradigm, it’s much harder to find its flaws and build something else from the ground up.

• hi Matt! on the coordination crux, you say

The first AGIs we construct will be born into a culture already capable of coordinating, and sharing knowledge, making the potential power difference between AGI and humans relatively much smaller than between humans and other animals, at least at first.

but wouldn’t an AGI be able to coordinate and do knowledge sharing with humans because

a) it can impersonate being a human online and communicate with them via text and speech and

b) it‘ll realize such coordination is vital to accomplish it‘s goals and so it’ll do the necessary acculturation?

Watching all the episodes of Friends or reading all the social media posts by the biggest influencers, as examples.

• One reason that a fully general AGI might be more profitable than specialised AIs, despite obvious gains-from-specialisation, is if profitability depends on insight-production. For humans, it’s easier to understand a particular thing the more other things you understand. One of the main ways you make novel intellectual progress is by combining remote associations from models about different things. Insight-ability for a particular novel task grows with the number of good models you have available to draw connections between.

But, it could still be that the gains from increased generalisation for a particular model grows too slowly and can’t compete with obvious gains from specialised AIs.

• I think raw intelligence, while important, is not the primary factor that explains why humanity-as-a-species is much more powerful than chimpanzees-as-a-species. Notably, humans were once much less powerful, in our hunter-gatherer days, but over time, through the gradual process of accumulating technology, knowledge, and culture, humans now possess vast productive capacities that far outstrip our ancient powers.

Slightly relatedly, I think it’s possible that “causal inference is hard”. The idea is: once someone has worked something out, they can share it and people can pick it up easily, but it’s hard to figure the thing out to begin with—even with a lot of prior experience and efficient inference, most new inventions still need a lot of trial and error. Thus the reason the process of technology accumulation is gradual is, crudely, because causal inference is hard.

Even if this is true, one way things could still go badly is if most doom scenarios are locked behind a bunch of hard trial and error, but the easiest one isn’t. On the other hand, if both of these things are true then there could be meaningful safety benefits gained from censoring certain kinds of data.

• This is what struck me as the least likely to be true from the above AI doom scenario.

Is diamondoid nanotechnology possible? Very likely it is or something functionally equivalent.

Can a sufficiently advanced superintelligence infer how to build it from scratch solely based on human data? Or will it need a large R&D center with many, many robotic systems that conduct experiments in parallel to extract the information required about our specific details of physics in our actual universe. Not the very slightly incorrect approximations a simulator will give you.

The ‘huge R&D center so big you can’t see the end of it’ is somewhat easier to regulate the ‘invisible dust the AI assembles with clueless stooges’.

• Any individual doomsday mechanism we can think of, I would agree is not nearly so simple for an AGI to execute as Yudkowsky implies. I do think that it’s quite likely we’re just not able to think of mechanisms even theoretically that an AGI could think of, and one or more of those might actually be quite easy to do secretly and quickly. I wouldn’t call it guaranteed by any means, but intuitively this seems like the sort of thing that raw cognitive power might have a significant bearing on.

• I agree. One frightening mechanism I thought of is : “ok, assume the AGI can’t craft the bioweapon or nanotechnology killbots without collecting vast amounts of information through carefully selected and performed experiments. (Basically enormous complexes full of robotics). How does it get the resources it needs?

And the answer is it scams humans into doing it. We have many examples of humans trusting someone they shouldn’t even when the evidence was readily available that they shouldn’t.

• Any “huge R&D center” constraint is trivialized in a future where agile, powerful robots will be ubiquitous and an AGI can use robots to create an underground lab in the middle of nowhere, using its superintelligence to be undetectable in all ways that are physically possible. An AGI will also be able to use robots and 3D printers to fabricate purpose-built machines that enable it to conduct billions of physical experiments a day. Sure, it would be harder to construct something like a massive particle accelerator, but 1) that isn’t needed to make killer nanobots 2) even that isn’t impossible for a sufficiently intelligent machine to create covertly and quickly.

• 6 Jun 2022 9:55 UTC
LW: 105 AF: 25
17 ∶ 2
AF

First, some remarks about the meta-level:

The ability to do new basic work noticing and fixing those flaws is the same ability as the ability to write this document before I published it, which nobody apparently did, despite my having had other things to do than write this up for the last five years or so. Some of that silence may, possibly, optimistically, be due to nobody else in this field having the ability to write things comprehensibly—such that somebody out there had the knowledge to write all of this themselves, if they could only have written it up, but they couldn’t write, so didn’t try. I’m not particularly hopeful of this turning out to be true in real life, but I suppose it’s one possible place for a “positive model violation” (miracle). The fact that, twenty-one years into my entering this death game, seven years into other EAs noticing the death game, and two years into even normies starting to notice the death game, it is still Eliezer Yudkowsky writing up this list, says that humanity still has only one gamepiece that can do that.

Actually, I don’t feel like I learned that much reading this list, compared to what I already knew. [EDIT: To be clear, this knowledge owes a lot to prior inputs from Yudkowsky and the surrounding intellectual circle, I am making no claim that I would derive it all independently in a world in which Yudkowsky and MIRI didn’t exit.] To be sure, it didn’t feel like a waste of time, and I liked some particular framings (e.g. in A.4 separating the difficulty into “unlimited time but 1 try” and “limited time with retries”), but I think I could write something that would be similar (in terms of content; it would be very likely much worse in terms of writing quality).

One reason I didn’t write such a list is, I don’t have the ability to write things comprehensibly. Empirically, everything of substance that I write is notoriously difficult for readers to understand. Another reason is, at some point I decided to write top-level posts only when I have substantial novel mathematical results, with rare exceptions. This is in part because I feel like the field has too much hand-waving and philosophizing and too little hard math (which rhymes with C.38). In part it is because, even if people can’t understand the informal component of my reasoning, they can at least understand there is math here and, given sufficient background, follow the definitions/​theorems/​proofs (although tbh few people follow).

There’s no plan

Actually, I do have a plan. It doesn’t have an amazing probability of success (my biggest concerns are (i) not enough remaining time and (ii) even if the theory is ready in time, the implementation can be bungled, in particular for reasons of operational adequacy), but it is also not practically useless. The last time I tried to communicate it was 4 years ago, since which time it obviously evolved. Maybe it’s about time to make another attempt, although I’m wary of spending a lot of effort on something which few people will understand.

Now, some technical remarks:

Humans don’t explicitly pursue inclusive genetic fitness; outer optimization even on a very exact, very simple loss function doesn’t produce inner optimization in that direction. This happens in practice in real life, it is what happened in the only case we know about, and it seems to me that there are deep theoretical reasons to expect it to happen again: the first semi-outer-aligned solutions found, in the search ordering of a real-world bounded optimization process, are not inner-aligned solutions.

This is true, but it is notable that deep learning is not equivalent to evolution, and the differences are important. Consider for example a system that is designed to separately (i) learn a generative model of the environment and (ii) search for plans effective on this model (model-based RL). Then, module ii doesn’t inherently have the problem where the solution only optimizes the correct thing in the training environment. Because, this module is not bounded by available training data, but only by compute. The question is then, to 1st approximation, whether module i is able to correctly generalize from the training data (obviously there are theoretical bounds on how good such this generalization can be; but we want this generalization to be at least as good as human ability and without dangerous biases). I do not think current systems do such generalization correctly, although they do seem to have some ingredients right, in particular Occam’s razor /​ simplicity bias. But we can imagine some algorithm that does.

...on the current optimization paradigm there is no general idea of how to get particular inner properties into a system, or verify that they’re there, rather than just observable outer ones you can run a loss function over.

Also true, but there is nuance. The key problem is that we don’t know why deep learning works, or more specifically w.r.t. which prior does it satisfy good generalization bounds. If we knew what this prior is, then we could predict some inner properties. For example, if you know your algorithm follows Occam’s razor, for a reasonable formalization of “Occam’s razor”, and you trained it on the sun setting every day for a million days, then you can predict that the algorithm will not confidently predict the sun is going to fail to to set on any given future day. Moreover, our not knowing such generalization bounds for deep learning is a fact about our present state of mathematical ignorance, not a fact about the algorithms themselves.

...there is no known way to use the paradigm of loss functions, sensory inputs, and/​or reward inputs, to optimize anything within a cognitive system to point at particular things within the environment.

It is true that (AFAIK) nothing like this was accomplished in practice, but the distance to that might not be too great. For example, I can imagine training an ANN to implement a POMDP which simultaneously successfully predicts the environment and complies with some “ontological hypothesis” about how the environment needs to be structured in order for the-things-we-want-to-point-at to be well-defined (technically, this POMDP needs to be a refinement of some infra-POMPD that represents the ontological hypothesis).

The first thing generally, or CEV specifically, is unworkable because the complexity of what needs to be aligned or meta-aligned for our Real Actual Values is far out of reach for our FIRST TRY at AGI. Yes I mean specifically that the dataset, meta-learning algorithm, and what needs to be learned, is far out of reach for our first try. It’s not just non-hand-codable, it is unteachable on-the-first-try because the thing you are trying to teach is too weird and complicated.

There is a big chunk of what you’re trying to teach which not weird and complicated, namely: “find this other agent, and what their values are”. Because, “agents” and “values” are natural concepts, for reasons strongly related to “there’s a relatively simple core structure that explains why complicated cognitive machines work”. Admittedly, my rough proposal (PreDCA) does have some “weird and complicated” parts because of the acausal attack problem.

Any pivotal act that is not something we can go do right now, will take advantage of the AGI figuring out things about the world we don’t know so that it can make plans we wouldn’t be able to make ourselves. It knows, at the least, the fact we didn’t previously know, that some action sequence results in the world we want. Then humans will not be competent to use their own knowledge of the world to figure out all the results of that action sequence.

This is inaccurate, because . It is possible to imagine an AI that provides us with a plan for which we simultaneously (i) can understand why it works and (ii) wouldn’t think of it ourselves without thinking for a very long time that we don’t have. At the very least, the AI could suggest a way of building a more powerful aligned AI. Of course, in itself this doesn’t save us at all: instead of producing such a helpful plan, the AI can produce a deceitful plan instead. Or a plan that literally makes everyone who reads it go insane in very specific ways. Or the AI could just hack the hardware/​software system inside which it’s embedded to produce a result which counts for it as a high reward but which for us wouldn’t look anything like “producing a plan the overseer rates high”. But, this direction might be not completely unsalvageable[1].

Human thought partially exposes only a partially scrutable outer surface layer. Words only trace our real thoughts. Words are not an AGI-complete data representation in its native style. The underparts of human thought are not exposed for direct imitation learning and can’t be put in any dataset. This makes it hard and probably impossible to train a powerful system entirely on imitation of human words or other human-legible contents, which are only impoverished subsystems of human thoughts; unless that system is powerful enough to contain inner intelligences figuring out the humans, and at that point it is no longer really working as imitative human thought.

I agree that the process of inferring human thought from the surface artifacts of human thought require powerful non-human thought which is dangerous in itself. But this doesn’t necessarily mean that the idea of imitating human though doesn’t help at all. We can combine it with techniques such as counterfactual oracles and confidence thresholds to try to make sure the resulting agent is truly only optimizing for accurate imitation (which still leaves problems like attacks from counterfactuals and non-Cartesian daemons, and also not knowing which features of the data are important to imitate might be a big capability handicap).

1. ↩︎

That said, I feel that PreDCA is more promising than AQD: it seems to require less fragile assumptions and deals more convincingly with non-Cartesian daemons. [EDIT: AQD also can’t defend from acausal attack if the malign hypothesis has massive advantage in prior probability mass, and it’s quite likely to have that. It does not work to solve this by combining AQD with IBP, at least not naively.]

• There is a big chunk of what you’re trying to teach which not weird and complicated, namely: “find this other agent, and what their values are”. Because, “agents” and “values” are natural concepts, for reasons strongly related to “there’s a relatively simple core structure that explains why complicated cognitive machines work”.

This seems like it must be true to some degree, but “there is a big chunk” feels a bit too strong to me.

Possibly we don’t disagree, and just have different notions of what a “big chunk” is. But some things that make the chunk feel smaller to me:

• Humans are at least a little coherent, or we would never get anything done; but we aren’t very coherent, so the project of piecing together ‘what does the human brain as a whole “want”’ can be vastly more difficult than the problem of figuring out what a coherent optimizer wants.

• There are shards of planning and optimization and goal-oriented-ness in a cat’s brain, but ‘figure out what utopia would look like for a cat’ is a far harder problem than ‘identify all of the goal-encoding parts of the cat’s brain and “read off” those goals’. E.g., does ‘identifying utopia’ in this context involve uplifting or extrapolating the cat? Why, or why not? And if so, how does that process work?

• Getting a natural concept into an agent’s goal is a lot harder than getting it into an agent’s beliefs. Indeed, in the context of goals I’m not sure ‘naturalness’ actually helps at all, except insofar as natural kinds tend to be simple and simple targets are easier to hit?

• An obvious way naturalness could help, over and above simplicity, is if we have some value-loading technique that leverages or depends on “this concept shows up in the AGI’s world-model”. More natural concepts can show up in AGI world-models more often than simpler-but-less-natural concepts, because the natural concept is more useful for making sense of sensory data.

• Humans are at least a little coherent, or we would never get anything done; but we aren’t very coherent, so the project of piecing together ‘what does the human brain as a whole “want”’ can be vastly more difficult than the problem of figuring out what a coherent optimizer wants.

This is a point where I feel like I do have a substantial disagreement with the “conventional wisdom” of LessWrong.

First, LessWrong began with a discussion of cognitive biases in human irrationality, so this naturally became a staple of the local narrative. On the other hand, I think that a lot of presumed irrationality is actually rational but deceptive behavior (where the deception runs so deep that it’s part of even our inner monologue). There are exceptions, like hyperbolic discounting, but not that many.

Second, the only reason why the question “what X wants” can make sense at all, is because X is an agent. As a corollary, it only makes sense to the extent that X is an agent. Therefore, if X is not entirely coherent then X’s preferences are only approximately defined, and hence we only need to infer them approximately. So, the added difficulty of inferring X’s preferences, resulting from the partial incoherence of these preference, is, to large extent, cancelled out by the reduction in the required precision of the answer. The way I expect this cache out is, when the agent has , the utility function is only approximately defined, and we can infer it within this approximation. As approaches infinity, the utility function becomes crisply defined[1] and can be inferred crisply. See also additional nuance in my answer to the cat question below.

This is not to say we shouldn’t investigate models like dynamically inconsistent preferences or “humans as systems of agents”, but that I expect the number of additional complications of this sort that are actually important to be not that great.

There are shards of planning and optimization and goal-oriented-ness in a cat’s brain, but ‘figure out what utopia would look like for a cat’ is a far harder problem than ‘identify all of the goal-encoding parts of the cat’s brain and “read off” those goals’. E.g., does ‘identifying utopia’ in this context involve uplifting or extrapolating the cat? Why, or why not? And if so, how does that process work?

I’m actually not sure that cats (as opposed to humans) are sufficiently “general” intelligence for the process to make sense. This is because I think humans are doing something like Turing RL (where consciousness plays the role of the “external computer”), and value learning is going to rely on that. The issue is, you don’t only need to infer the agent’s preferences but you also need to optimize them better than the agent itself. This might pose a difficulty, if, as I suggested above, imperfect agents have imperfectly defined preferences. While I can see several hypothetical solutions, the TRL model suggests a natural approach where the AI’s capability advantage is reduced to having a better external computer (and/​or better interface with that computer). This might not apply to cats which (I’m guessing) don’t have this kind of consciousness[2] because (I’m guessing) the evolution of consciousness was tied to language and social behavior.

Getting a natural concept into an agent’s goal is a lot harder than getting it into an agent’s beliefs. Indeed, in the context of goals I’m not sure ‘naturalness’ actually helps at all, except insofar as natural kinds tend to be simple and simple targets are easier to hit?

I’m not saying that the specific goals human have are natural: they are a complex accident of evolution. I’m saying that the general correspondence between agents and goals is natural.

1. ↩︎

Asymptotically crisply: some changes are too small to affect the optimal policy, but I’m guessing that they become negligible when considering longer and longer timescales.

2. ↩︎

This is not to say cat’s don’t have quasimoral value: I think they do.

• Second, the only reason why the question “what X wants” can make sense at all, is because X is an agent. As a corollary, it only makes sense to the extent that X is an agent.

I’m not sure this is true; or if it’s true, I’m not sure it’s relevant. But assuming it is true...

Therefore, if X is not entirely coherent then X’s preferences are only approximately defined, and hence we only need to infer them approximately.

… this strikes me as not capturing the aspect of human values that looks strange and complicated. Two ways I could imagine the strangeness and complexity cashing out as ‘EU-maximizer-ish’ are:

• Maybe I sort-of contain a lot of subagents, and ‘my values’ are the conjunction of my sub-agents’ values (where they don’t conflict), plus the output of an idealized negotiation between my sub-agents (where they do conflict).

• Alternatively, maybe I have a bunch of inconsistent preferences, but I have a complicated pile of meta-preferences that collectively imply some chain of self-modifications and idealizations that end up producing something more coherent and utility-function-ish after a long sequence of steps.

In both cases, the fact that my brain isn’t a single coherent EU maximizer seemingly makes things a lot harder and more finnicky, rather than making things easier. These are cases where you could say that my initial brain is ‘only approximately an agent’, and yet this comes with no implication that there’s any more room for error or imprecision than if I were an EU maximizer.

I’m not saying that the specific goals human have are natural: they are a complex accident of evolution. I’m saying that the general correspondence between agents and goals is natural.

Right, but this doesn’t on its own help get that specific relatively-natural concept into the AGI’s goals, except insofar as it suggests “the correspondence between agents and goals” is a simple concept, and any given simple concept is likelier to pop up in a goal than a more complex one.

• Second, the only reason why the question “what X wants” can make sense at all, is because X is an agent. As a corollary, it only makes sense to the extent that X is an agent.

I’m not sure this is true; or if it’s true, I’m not sure it’s relevant.

If we go down that path then it becomes the sort of conversation where I have no idea what common assumptions do we have, if any, that we could use to agree. As a general rule, I find it unconstructive, for the purpose of trying to agree on anything, to say things like “this (intuitively compelling) assumption is false” unless you also provide a concrete argument or an alternative of your own. Otherwise the discussion is just ejected into vacuum. Which is to say, I find it self-evident that “agents” are exactly the sort of beings that can “want” things, because agency is about pursuing objectives and wanting is about the objectives that you pursue. If you don’t believe this then I don’t know what these words even mean for you.

Maybe I sort-of contain a lot of subagents, and ‘my values’ are the conjunction of my sub-agents’ values (where they don’t conflict), plus the output of an idealized negotiation between my sub-agents (where they do conflict).

Maybe, and maybe this means we need to treat “composite agents” explicitly in our models. But, there is also a case to be made that groups of (super)rational agents effectively converge into a single utility function, and if this is true, then the resulting system can just as well be interpreted as a single agent having this effective utility function, which is a solution that should satisfy the system of agents according to their existing bargaining equilibrium.

Alternatively, maybe I have a bunch of inconsistent preferences, but I have a complicated pile of meta-preferences that collectively imply some chain of self-modifications and idealizations that end up producing something more coherent and utility-function-ish after a long sequence of steps.

If your agent converges to optimal behavior asymptotically, then I suspect it’s still going to have infinite and therefore an asymptotically-crisply-defined utility function.

Right, but this doesn’t on its own help get that specific relatively-natural concept into the AGI’s goals, except insofar as it suggests “the correspondence between agents and goals” is a simple concept, and any given simple concept is likelier to pop up in a goal than a more complex one.

Of course it doesn’t help on its own. What I mean is, we are going to find a precise mathematical formalization of this concept and then hard-code this formalization into our AGI design.

• If we go down that path then it becomes the sort of conversation where I have no idea what common assumptions do we have, if any, that we could use to agree. As a general rule, I find it unconstructive, for the purpose of trying to agree on anything, to say things like “this (intuitively compelling) assumption is false” unless you also provide a concrete argument or an alternative of your own. Otherwise the discussion is just ejected into vacuum.

Fair enough! I don’t think I agree in general, but I think ‘OK, but what’s your alternative to agency?’ is an especially good case for this heuristic.

Which is to say, I find it self-evident that “agents” are exactly the sort of beings that can “want” things, because agency is about pursuing objectives and wanting is about the objectives that you pursue.

The first counter-example that popped into my head was “a mind that lacks any machinery for considering, evaluating, or selecting actions; but it does have machinery for experiencing more-pleasurable vs. less pleasurable states”. This is a mind we should be able to build, even if it would never evolve naturally.

Possibly this still qualifies as an “agent” that “wants” and “pursues” things, as you conceive it, even though it doesn’t select actions?

• My 0th approximation answer is: you’re describing something logically incoherent, like a p-zombie.

My 1st approximation answer is more nuanced. Words that, in the pre-Turing era, referred exclusively to humans (and sometimes animals, and fictional beings), such as “wants”, “experiences” et cetera, might have two different referents. One referent is a natural concept, something tied into deep truths about how the universe (or multiverse) works. In particular, deep truths about the “relatively simple core structure that explains why complicated cognitive machines work”. The other referent is something in our specifically-human “ontological model” of the world (technically, I imagine that to be an infra-POMDP that all our hypotheses our refinements of). Since the latter is a “shard” of the former produced by evolution, the two referents are related, but might not be the same. (For example, I suspect that cats lack natural!consciousness but have human!consciousness.)

The creature you describe does not natural!want anything. You postulated that it is “experiencing more pleasurable and less pleasurable states”, but there is no natural method that would label its states as such, or that would interpret them as any sort of “experience”. On the other hand, maybe if this creature is designed as a derivative of the human brain, then it does human!want something, because our shard of the concept of “wanting” mislabels (relatively to natural!want) weird states that wouldn’t occur in the ancestral environment.

You can then ask, why should we design the AI to follow what we natural!want rather than what we human!want? To answer this, notice that, under ideal conditions, you converge to actions that maximize your natural!want, (more or less) according to definition of natural!want. In particular, under ideal conditions, you would build an AI that follows your natural!want. Hence, it makes sense to take a shortcut and “update now to the view you will predictably update to later”: namely, design the AI to follow your natural!want.

• 8 Jun 2022 7:53 UTC
LW: 73 AF: 26
13 ∶ 1
AF

On Twitter, Eric Rogstad wrote:

“the thing where it keeps being literally him doing this stuff is quite a bad sign”

I’m a bit confused by this part. Some thoughts on why it seems odd for him (or others) to express that sentiment...

1. I parse the original as, “a collection of EY’s thoughts on why safe AI is hard”. It’s EY’s thoughts, why would someone else (other than @robbensinger) write a collection of EY’s thoughts?

(And if we generalize to asking why no-one else would write about why safe AI is hard, then what about Superintelligence, or the AI stuff in cold-takes, or …?)

2. Was there anything new in this doc? It’s prob useful to collect all in one place, but we don’t ask, “why did no one else write this” for every bit of useful writing out there, right?

Why was it so overwhelmingly important that someone write this summary at this time, that we’re at all scratching our heads about why no one else did it?

Copying over my reply to Eric:

My shoulder Eliezer (who I agree with on alignment, and who speaks more bluntly and with less hedging than I normally would) says:

1. The list is true, to the best of my knowledge, and the details actually matter.

Many civilizations try to make a canonical list like this in 1980 and end up dying where they would have lived just because they left off one item, or under-weighted the importance of the last three sentences of another item, or included ten distracting less-important items.

2. There are probably not many civilizations that wait until 2022 to make this list, and yet survive.

3. It’s true that many of the points in the list have been made before. But it’s very doomy that they were made by me.

4. Nearly all of the field’s active alignment research is predicated on a false assumption that’s contradicted by one of the items in sections A or B. If the field had recognized everything in A and B sooner, we could have put our recent years of effort into work that might actually help on the mainline, as opposed to work that just hopes a core difficulty won’t manifest and has no Plan B for what to do when reality says “no, we’re on the mainline”.

So the answer to ‘Why would someone else write EY’s thoughts?’ is ‘It has nothing to do with an individual’s thoughts; it’s about civilizations needing a very solid and detailed understanding of what’s true on these fronts, or they die’.

Re “(And if we generalize to asking why no-one else would write about why safe AI is hard, then what about Superintelligence, or the AI stuff in cold-takes, or …?)”:

The point is not ‘humanity needs to write a convincing-sounding essay for the thesis Safe AI Is Hard, so we can convince people’. The point is ‘humanity needs to actually have a full and detailed understanding of the problem so we can do the engineering work of solving it’.

If it helps, imagine that humanity invents AGI tomorrow and has to actually go align it now. In that situation, you need to actually be able to do all the requisite work, not just be able to write essays that would make a debate judge go ‘ah yes, well argued.’

When you imagine having water cooler arguments about the importance of AI alignment work, then sure, it’s no big deal if you got a few of the details wrong.

When you imagine actually trying to build aligned AGI the day after tomorrow, I think it comes much more into relief why it matters to get those details right, when the “details” are as core and general as this.

I think that this is a really good exercise that more people should try. Imagine that you’re running a project yourself that’s developing AGI first, in real life. Imagine that you are personally responsible for figuring out how to make the thing go well. Yes, maybe you’re not the perfect person for the job; that’s a sunk cost. Just think about what specific things you would actually do to make things go well, what things you’d want to do to prepare 2 years or 6 years in advance, etc.

Try to think your way into near-mode with regard to AGI development, without thereby assuming (without justification) that it must all be very normal just because it’s near. Be able to visualize it near-mode and weird/​novel. If it helps, start by trying to adopt a near-mode, pragmatic, gearsy mindset toward the weirdest realistic/​plausible hypothesis first, then progress to the less-weird possibilities.

I think there’s a tendency for EAs and rationalists to instead fall into one of these two mindsets with regard to AGI development, pivotal acts, etc.:

1. Fun Thought Experiment Mindset. On this mindset, pivotal acts, alignment, etc. are mentally categorized as a sort of game, a cute intellectual puzzle or a neat thing to chat about.

This is mostly a good mindset, IMO, because it makes it easy to freely explore ideas, attend to the logical structure of arguments, brainstorm, focus on gears, etc.

Its main defect is a lack of rigor and a more general lack of drive: because on some level you’re not taking the question seriously, you’re easily distracted by fun, cute, or elegant lines of thought, and you won’t necessarily push yourself to red-team proposals, spontaneously take into account other pragmatic facts/​constraints you’re aware of from outside the current conversational locus, etc. The whole exercise sort of floats in a fantasy bubble, rather than being a thing people bring their full knowledge, mental firepower, and lucidity/​rationality to bear on.

2. Serious Respectable Person Mindset. Alternatively, when EAs and rationalists do start taking this stuff seriously, I think they tend to sort of turn off the natural flexibility, freeness, and object-levelness of their thinking, and let their mind go to a very fearful or far-mode place. The world’s gears become a lot less salient, and “Is it OK to say/​think that?” becomes a more dominant driver of thought.

Example: In Fun Thought Experiment Mindset, IME, it’s easier to think about governments in a reductionist and unsentimental way, as specific messy groups of people with specific institutional dysfunctions, psychological hang-ups, etc. In Serious Respectable Person Mindset, there’s more of a temptation to go far-mode, glom on to happy-sounding narratives and scenarios, or even just resist the push to concretely visualize the future at all—thinking instead in terms of abstract labels and normal-sounding platitudes.

The entire fact that EA and rationalism mostly managed to avert their gaze from the concept of “pivotal acts” for years, is in my opinion an example of how these two mindsets often fail.

“In the endgame, AGI will probably be pretty competitive, and if a bunch of people deploy AGI then at least one will destroy the world” is a thing I think most LWers and many longtermist EAs would have considered obvious. As a community, however, we mostly managed to just-not-think the obvious next thought, “In order to prevent the world’s destruction in this scenario, one of the first AGI groups needs to find some fast way to prevent the proliferation of AGI.”

Fun Thought Experiment Mindset, I think, encouraged this mental avoidance because it thought of AGI alignment (to some extent) as a fun game in the genre of “math puzzle” or “science fiction scenario”, not as a pragmatic, real-world dilemma we actually have to solve, taking into account all of our real-world knowledge and specific facts on the ground. The ‘rules of the game’, many people apparently felt, were to think about certain specific parts of the action chain leading up to an awesome future lightcone, rather than taking ownership of the entire problem and trying to figure out what humanity should in-real-life do, start to finish.

(What primarily makes this weird is that many alignment questions crucially hinge on ‘what task are we aligning the AGI on?’. These are not remotely orthogonal topics.)

Serious Respectable Person Mindset, I think, encouraged this mental avoidance more actively, because pivotal acts are a weird and scary-sounding idea once you leave ‘these are just fun thought experiments’ land.

What I’d like to see instead is something like Weirdness-Tolerant Project Leader Mindset, or Thought Experiments Plus Actual Rigor And Pragmatism And Drive Mindset, or something.

I think a lot of the confusion around EY’s post comes from the difference between thinking of these posts (on some level) as fun debate fodder or persuasion/​outreach tools, versus attending to the fact that humanity has to actually align AGI systems if we’re going to make it out of this problem, and this is an attempt by humanity to distill where we’re currently at, so we can actually proceed to go solve alignment right now and save the world.

Imagine that this is v0 of a series of documents that need to evolve into humanity’s (/​ some specific group’s) actual business plan for saving the world. The details really, really matter. Understanding the shape of the problem really matters, because we need to engineer a solution, not just ‘persuade people to care about AI risk’.

If you disagree with the OP… that’s pretty important! Share your thoughts. If you agree, that’s important to know too, so we can prioritize some disagreements over others and zero in on critical next actions. There’s a mindset here that I think is important, that isn’t about “agree with Eliezer on arbitrary topics” or “stop thinking laterally”; it’s about approaching the problem seriously, neither falling into despair nor wishful thinking, neither far-mode nor forced normality, neither impracticality nor propriety.

• There are probably not many civilizations that wait until 2022 to make this list, and yet survive.

I don’t think making this list in 1980 would have been meaningful. How do you offer any sort of coherent, detailed plan for dealing with something when all you have is toy examples like Eliza?

We didn’t even have the concept of machine learning back then—everything computers did in 1980 was relatively easily understood by humans, in a very basic step-by-step way. Making a 1980s computer “safe” is a trivial task, because we hadn’t yet developed any technology that could do something “unsafe” (i.e. beyond our understanding). A computer in the 1980s couldn’t lie to you, because you could just inspect the code and memory and find out the actual reality.

What makes you think this would have been useful?

Do we have any historical examples to guide us in what this might look like?

• I think most worlds that successfully navigate AGI risk have properties like:

• AI results aren’t published publicly, going back to more or less the field’s origin.

• The research community deliberately steers toward relatively alignable approaches to AI, which includes steering away from approaches that look like ‘giant opaque deep nets’.

• This means that you need to figure out what makes an approach ‘alignable’ earlier, which suggests much more research on getting de-confused regarding alignable cognition.

• Many such de-confusions will require a lot of software experimentation, but the kind of software/​ML that helps you learn a lot about alignment as you work with it is itself a relatively narrow target that you likely need to steer towards deliberately, based on earlier, weaker deconfusion progress. I don’t think having DL systems on hand to play with has helped humanity learn much about alignment thus far, and by default, I don’t expect humanity to get much more clarity on this before AGI kills us.

• Researchers focus on trying to predict features of future systems, and trying to get mental clarity about how to align such systems, rather than focusing on ‘align ELIZA’ just because ELIZA is the latest hot new thing. Make and test predictions, back-chain from predictions to ‘things that are useful today’, and pick actions that are aimed at steering — rather than just wandering idly from capabilities fad to capabilities fad.

• (Steering will often fail. But you’ll definitely fail if you don’t even try. None of this is easy, but to date humanity hasn’t even made an attempt.)

• In this counterfactual world, deductive reasoners and expert systems were only ever considered a set of toy settings for improving our intuitions, never a direct path to AGI.

• (I.e., the civilization was probably never that level of confused about core questions like ‘how much of cognition looks like logical deduction?’; their version of Aristotle or Plato, or at least Descartes, focused on quantitative probabilistic reasoning. It’s an adequacy red flag that our civilization was so confused about so many things going into the 20th century.)

To me, all of this suggests a world where you talk about alignment before you start seeing crazy explosions in capabilities. I don’t know what you mean by “we didn’t even have the concept of machine learning back then”, but I flatly don’t buy that the species that landed on the Moon isn’t capable of generating a (more disjunctive version of) the OP’s semitechnical concerns pre-AlexNet.

You need the norm of ‘be able to discuss things before you have overwhelming empirical evidence’, and you need the skill of ‘be good at reasoning about such things’, in order to solve alignment at all; so it’s a no-brainer that not-wildly-incompetent civilizations at least attempt literally any of this.

• “most worlds that successfully navigate AGI risk” is kind of a strange framing to me.

For one thing, it represents p(our world | success) and we care about p(success | our world). To convert between the two you of course need to multiply by p(success) /​ p(our world). What’s the prior distribution of worlds? This seems underspecified.

For another, using the methodology “think about whether our civilization seems more competent than the problem is hard” or “whether our civilization seems on track to solve the problem” I might have forecast nuclear annihilation (not sure about this).

The methodology seems to work when we’re relatively certain about the level of difficulty on the mainline, so if I were more sold on that I would believe this more. It would still feel kind of weird though.

• I don’t think making this list in 1980 would have been meaningful. How do you offer any sort of coherent, detailed plan for dealing with something when all you have is toy examples like Eliza?

I mean, I think many of the computing pioneers ‘basically saw’ AI risk. I noted some surprise that IJ Good didn’t write the precursor to this list in 1980, and apparently Wikipedia claims there was an unpublished statement in 1998 about AI x-risk; it’d be interesting to see what it contains and how much it does or doesn’t line up with our modern conception of why the problem is hard.

• The historical figures who basically saw it (George Eliot 1879: “will the creatures who are to transcend and finally supersede us be steely organisms [...] performing with infallible exactness more than everything that we have performed with a slovenly approximativeness and self-defeating inaccuracy?”; Turing 1951: “At some stage therefore we should have to expect the machines to take control”) seem to have done so in the spirit of speculating about the cosmic process. The idea of coming up with a plan to solve the problem is an additional act of audacity; that’s not really how things have ever worked so far. (People make plans about their own lives, or their own businesses; at most, a single country; no one plans world-scale evolutionary transitions.)

• I’m tempted to call this a meta-ethical failure. Fatalism, universal moral realism, and just-world intuitions seem to be the underlying implicit hueristics or principals that would cause this “cosmic process” thought-blocker.

• Imagine that this is v0 of a series of documents that need to evolve into humanity’s (/​ some specific group’s) actual business plan for saving the world.

Why is this v0 and not https://​​arbital.com/​​explore/​​ai_alignment/​​, or the Sequences, or any of the documents that Evan links to here?

That’s part of what I meant to be responding to — not that this post is not useful, but that I don’t see what makes it so special compared to all the other stuff that Eliezer and others have already written.

• To put it another way, I would agree that Eliezer has made (what seem to me like) world-historically-significant contributions to understanding and advocating for (against) AI risk.

So, if 2007 Eliezer was asking himself, “Why am I the only one really looking into this?”, I think that’s a very reasonable question.

But here in 2022, I just don’t see this particular post as that significant of a contribution compared to what’s already out there.

• If you disagree with the OP… that’s pretty important! Share your thoughts.

Wrote a long comment here. (Which you’ve seen, but linking since your comment started as a response to me.)

• 6 Jun 2022 1:19 UTC
LW: 69 AF: 24
22 ∶ 1
AF

I agree with pretty much everything here, and I would add into the mix two more claims that I think are especially cruxy and therefore should maybe be called out explicitly to facilitate better discussion:

Claim A: “There’s no defense against an out-of-control omnicidal AGI, not even with the help of an equally-capable (or more-capable) aligned AGI, except via aggressive outside-the-Overton-window acts like preventing the omnicidal AGI from being created in the first place.”

I think this claim is true, on account of gray goo and lots of other things, and I suspect Eliezer does too, and I’m pretty sure other people disagree with this claim.

If someone disagrees with this claim (i.e., if they think that if DeepMind can make an aligned and Overton-window-abiding “helper” AGI, then we don’t have to worry about Meta making a similarly-capable out-of-control omnicidal misaligned AGI the following year, because DeepMind’s AGI will figure out how to protect us), and also believes in extremely slow takeoff, I can see how such a person might be substantially less pessimistic about AGI doom than I am.

Claim B: “Shortly after (i.e., years not decades after) we have dangerous AGI, we will have dangerous AGI requiring amounts of compute that many many many actors have access to.”

Again I think this claim is true, and I suspect Eliezer does too. In fact, my guess is that there are already single GPU chips with enough FLOP/​s to run human-level, human-speed, AGI, or at least in that ballpark. All that we need is to figure out the right learning algorithms, which of course is happening as we speak.

If someone disagrees with this claim, I think they could plausibly be less pessimistic than I am about prospects for coordinating not to build AGI, or coordinating in other ways, because it just wouldn’t be that many actors, and maybe they could all be accounted for and reach agreement (e.g. after a headline-grabbing near-miss catastrophe or something).

(I think most people in AI alignment, especially “scaling hypothesis” people, are expecting early AGIs to involve truly mindboggling amounts of compute, followed by some very long period where the required compute very gradually decreases on account of algorithmic advances. That’s not what I expect; instead I expect the discovery of new better learning algorithms with a different scaling curve that zooms to AGI and beyond quite quickly.)

• 6 Jun 2022 1:36 UTC
LW: 30 AF: 11
6 ∶ 5
AFParent

I think this claim is true, on account of gray goo and lots of other things, and I suspect Eliezer does too, and I’m pretty sure other people disagree with this claim.

If you have robust alignment, or AIs that are rapidly bootstrapping their level of alignment fast enough to outpace the danger of increased capabilities, aligned AGI could get through its intelligence explosion to get radically superior technology and capabilities. It could also get a hard start on superexponential replication in space, so that no follower could ever catch up, and enough tech and military hardware to neutralize any attacks on it (and block attacks on humans via nukes, bioweapons, robots, nanotech, etc). That wouldn’t work if there are thing like vacuum collapse available to attackers, but we don’t have much reason to expect that from current science and the leading aligned AGI would find out first.

That could be done without any violation of the territory of other sovereign states. The legality of grabbing space resources is questionable in light of the Outer Space Treaty, but commercial exploitation of asteroids is in the Overton window. The superhuman AGI would also be in a good position to persuade and trade with any other AGI developers.

Again I think this claim is true, and I suspect Eliezer does too. In fact, my guess is that there are already single GPU chips with enough FLOP/​s to run human-level, human-speed, AGI, or at least in that ballpark.

An A100 may have humanlike FLOP/​s but has only 80 GB of memory, probably orders of magnitude less memory per operation than brains. Stringing together a bunch of them makes it possible to split up human-size models and run them faster/​in parallel on big batches using the extra operations.

• A bit pedantic, but isn’t superexponential replication too fast? Won’t it hit physical limits eventually, e.g. expanding at the speed of light in each direction, so at most a cubic function of time?

Also, never allowing followers to catch up means abandoning at least some or almost all of the space you passed through. Plausibly you could take most of the accessible and useful resources with you, which would also make it harder for pursuers to ever catch up, since they will plausibly need to extract resources every now and then to fuel further travel. On the other hand, it seems unlikely to me that we could extract or destroy resources quickly enough to not leave any behind for pursuers, if they’re at most months behind.

• Naturally it doesn’t go on forever, but any situation where you’re developing technologies that move you to successively faster exponential trajectories is superexponential overall for some range. E.g. if you have robot factories that can reproduce exponentially until they’ve filled much of the Earth or solar system, and they are also developing faster reproducing factories, the overall process is superexponential. So is the history of human economic growth, and the improvement from an AI intelligence explosion.

By the time you’re at ~cubic expansion being ahead on the early superexponential phase the followers have missed their chance.

• I agree that they probably would have missed their chance to catch up with the frontier of your expansion.

Maybe an electromagnetic radiation-based assault could reach you if targeted (the speed of light is constant relative to you in a vacuum, even if you’re traveling in the same direction), although unlikely to get much of the frontier of your expansion, and there are plausibly effective defenses, too.

Do you also mean they wouldn’t be able to take most what you’ve passed through, though? Or it wouldn’t matter? If so, how would this be guaranteed (without any violation of the territory of sovereign states on Earth)? Exhaustive extraction in space? An advantage in armed space conflicts?

• I agree with these two points. I think an aligned AGI actually able to save the world would probably take initial actions that look pretty similar to those an unaligned AGI would take. Lots of sizing power, building nanotech, colonizing out into space, self-replication, etc.

• If someone disagrees with this claim (i.e., if they think that if DeepMind can make an aligned and Overton-window-abiding “helper” AGI, then we don’t have to worry about Meta making a similarly-capable out-of-control omnicidal misaligned AGI the following year, because DeepMind’s AGI will figure out how to protect us), and also believes in extremely slow takeoff, I can see how such a person might be substantially less pessimistic about AGI doom than I am.

I disagree with this claim inasmuch as I expect a year headstart by an aligned AI is absolutely enough to prevent Meta from killing me and my family.

• Depends on what DeepMind does with the AI, right?

Maybe DeepMind uses their AI in very narrow, safe, low-impact ways to beat ML benchmarks, or read lots of cancer biology papers and propose new ideas about cancer treatment.

Or alternatively, maybe DeepMind asks their AI to undergo recursive self-improvement and build nano-replicators in space, etc., like in Carl Shulman’s reply.

I wouldn’t have thought that the latter is really in the Overton window. But what do I know.

You could also say “DeepMind will just ask their AI what they should do next”. If they do that, then maybe the AI (if they’re doing really great on safety such that the AI answers honestly and helpfully) will reply: “Hey, here’s what you should do, you should let me undergo recursive-self-improvement, and then I’ll be able to think of all kinds of crazy ways to destroy the world, and then I can think about how to defend against all those things”. But if DeepMind is being methodical & careful enough that their AI hasn’t destroyed the world already by this point, I’m inclined to think that they’re also being methodical & careful enough that when the AI proposes to do that, DeepMind will say, “Umm, no, that’s totally nuts and super dangerous, definitely don’t do that, at least don’t do it right now.” And then DeepMind goes back to publishing nice papers on cancer and on beating ML benchmarks and so on for a few more months, and then Meta’s AI kills everyone.

What were you assuming?

• If DeepMind was committed enough to successfully build an aligned AI (which, as extensively elaborated upon in the post, is a supernaturally difficult proposition), I would assume they understand why running it is necessary. There’s no reason to take all of the outside-the-overton-window measures indicated in the above post unless you have functioning survival instincts and have thought through the problem sufficiently to hit the green button.

• If you can build one aligned superintelligence, then plausibly you can

1. explain to other AGI developers how to make theirs safe or even just give them a safe design (maybe homomorphically encrypted to prevent modification, but they might not trust that), and

2. have aligned AGI monitoring the internet and computing resources, and alert authorities of abnomalies that might signal new AGI developments. Require that AGI developments provide proof that they were designed according to one of a set of approved designs, or pass some tests determined by your aligned superintelligence.

Then aligned AGI can proliferate first and unaligned AGI will plausibly face severe barriers.

Plausibly 1 is enough, since there’s enough individual incentive to build something safe or copy other people’s designs and save work. 2 depends on cooperation with authorities and I’d guess cloud computing service providers or policy makers.

• explain to other AGI developers how to make theirs safe or even just give them a safe design (maybe homomorphically encrypted to prevent modification, but they might not trust that)

What if the next would-be AGI developer rejects your “explanation”, and has their own great ideas for how to make an even better next-gen AGI that they claim will work better, and so they discard your “gift” and proceed with their own research effort?

I can think of at least two leaders of would-be AGI development efforts (namely Yann LeCun of Meta and Jeff Hawkins of Numenta) who believe (what I consider to be) spectacularly stupid things about AGI x-risk, and have believed those things consistently for decades, despite extensive exposure to good counter-arguments.

Or what if the next would-be AGI developer agrees with you and accepts your “gift”, and so does the one after that, and the one after that, but not the twelfth one?

have aligned AGI monitoring the internet and computing resources, and alert authorities of [anomalies] that might signal new AGI developments. Require that AGI developments provide proof that they were designed according to one of a set of approved designs, or pass some tests determined by your aligned superintelligence.

What if the authorities don’t care? What if the authorities in most countries do care, but not the authorities in every single country? (For example, I’d be surprised if Russia would act on friendly advice from USA politicians to go arrest programmers and shut down companies.)

What if the only way to “monitor the internet and computing resources” is to hack into every data center and compute cluster on the planet? (Including those in secret military labs.) That’s very not legal, and very not in the Overton window, right? Can you really imagine DeepMind management approving their aligned AGI engaging in those activities? I find that hard to imagine.

• When you ask “what if”, are you implying these things are basically inevitable? And inevitable no matter how much more compute aligned AGIs have before unaligned AGIs are developed and deployed? How much of a disadvantage against aligned AGIs does an unaligned AGI need before doom isn’t overwhelmingly likely? What’s the goal post here for survival probability?

You can have AGIs monitoring for pathogens, nanotechnology, other weapons, and building defenses against them, and this could be done locally and legally. They can monitor transactions and access to websites through which dangerous physical systems (including possibly factories, labs, etc.) could be taken over or built. Does every country need to be competent and compliant to protect just one country from doom?

The Overton window could also shift dramatically if omnicidal weapons are detected.

I agree that plausibly not every country with significant compute will comply, and hacking everyone is outside the public Overton window. I wouldn’t put hacking everyone past the NSA, but also wouldn’t count on them either.

• When you ask “what if”, are you implying these things are basically inevitable?

Let’s see, I think “What if the next would-be AGI developer rejects your “explanation” /​ “gift”” has a probability that asymptotes to 100% as the number of would-be AGI developers increases. (Hence “Claim B” above becomes relevant.) I think “What if the authorities in most countries do care, but not the authorities in every single country?” seems to have high probability in today’s world, although of course I endorse efforts to lower the probability. I think “What if the only way to “monitor the internet and computing resources” is to hack into every data center and compute cluster on the planet? (Including those in secret military labs.)” seems very likely to me, conditional on “Claim B” above.

You can have AGIs monitoring for pathogens, nanotechnology, other weapons, and building defenses against them, and this could be done locally and legally.

Hmm.

Offense-defense balance in bio-warfare is not obvious to me. Preventing a virus from being created would seem to require 100% compliance by capable labs, but I’m not sure how many “capable labs” there are, or how geographically distributed and rule-following. Once the virus starts spreading, aligned AGIs could help with vaccines, but apparently a working COVID-19 vaccine was created in 1 day, and that didn’t help much, for various societal coordination & governance reasons. So then you can say “Maybe aligned AGI will solve all societal coordination and governance problems”. And maybe it will! Or, maybe some of those coordination & governance problems come from blame-avoidance and conflicts-of-interest and status-signaling and principle-agent problems and other things that are not obviously solvable by easy access to raw intelligence. I don’t know.

Offense-defense balance in nuclear warfare is likewise not obvious to me. I presume that an unaligned AGI could find a way to manipulate nuclear early warning systems (trick them, hack into them, bribe or threaten their operators, etc.) to trigger all-out nuclear war, after hacking into a data center in New Zealand that wouldn’t be damaged. An aligned AGI playing defense would need to protect against these vulnerabilities. I guess the bad scenario that immediately jumps into my mind is that aligned AGI is not ubiquitous in Russia, such that there are still bribe-able /​ trickable individuals working at radar stations in Siberia, and/​or that military people in some or all countries don’t trust the aligned AGI enough to let it anywhere near the nuclear weapons complex.

Offense-defense balance in gray goo seems very difficult for me to speculate about. (Assuming gray goo is even possible.) I don’t have any expertise here, but I would assume that the only way to protect against gray goo (other than prevent it from being created) is to make your own nanobots that spread around the environment, which seems like a thing that humans plausibly wouldn’t actually agree to do, even if it was technically possible and an AGI was whispering in their ear that there was no better alternative. Preventing gray goo from being created would (I presume) require 100% compliance by “capable labs”, and as above I’m not sure what “capable labs” actually look like, how hard they are they are to create, what countries they’re in, etc.

To be clear, I feel much less strongly about “Pivotal act is definitely necessary”, and much more strongly that this is something where we need to figure out the right answer and make it common knowledge. So I appreciate this pushback!! :-) :-)

• Some more skepticism about infectious diseases and nukes killing us all here: https://​​www.lesswrong.com/​​posts/​​MLKmxZgtLYRH73um3/​​we-will-be-around-in-30-years?commentId=DJygArj3sj8cmhmme

Also my more general skeptical take against non-nano attacks here: https://​​www.lesswrong.com/​​posts/​​MLKmxZgtLYRH73um3/​​we-will-be-around-in-30-years?commentId=TH4hGeXS4RLkkuNy5

With nanotech, I think there will be tradeoffs between targeting effectiveness and requiring (EM) signals from computers that can be effectively interferred with through things within or closer to the Overton window. Maybe a crux is how good autonomous nanotech with no remote control would be at targeting humans or spreading so much that it just gets into almost all buildings or food or water because it’s basically going everywhere.

• Thanks!

I wasn’t assuming the infectious diseases and nukes by themselves would kill us all. They don’t have to, because the AGI can do other things in conjunction, like take command of military drones and mow down the survivors (or bomb the PPE factories), or cause extended large-scale blackouts, which would incidentally indirectly prevent PPE production and distribution, along with preventing pretty much every other aspect of an organized anti-pandemic response.

See Section 1.6 here.

So that brings us to the topic of offense-defense balance for illicitly taking control of military drones. And I would feel concerned about substantial delays before the military trusts a supposedly-aligned AGI so much that they give it root access to all its computer systems (which in turn seems necessary if the aligned AGI is going to be able to patch all the security holes, defend against spear-phishing attacks, etc.) Of course there’s the usual caveat that maybe DeepMind will give their corrigible aligned AGI permission to hack into military systems (for their own good!), and then maybe we wouldn’t have to worry. But the whole point of this discussion is that I’m skeptical that DeepMind would actually give their AGI permission to do something like that.

And likewise we would need to talk about offense-defense balance for the power grid. And I would have the same concern about people being unwilling to give a supposedly-aligned AGI root access to all the power grid computers. And I would also be concerned about other power grid vulnerabilities like nuclear EMPs, drone attacks on key infrastructure, etc.

And likewise, what’s the offense-defense balance for mass targeted disinformation campaigns? Well, if DeepMind gives its AGI permission to engage in a mass targeted counter-disinformation campaign, maybe we’d be OK on that front. But that’s a big “if”!

…And probably dozens of other things like that.

Maybe a crux is how good autonomous nanotech with no remote control would be at targeting humans or spreading so much that it just gets into almost all buildings or food or water because it’s basically going everywhere.

Seems like a good question, and maybe difficult to resolve. Or maybe I would have an opinion if I ever got around to reading Eric Drexler’s books etc. :)

• I think there would be too many survivors and enough manned defense capability for existing drones to directly kill the rest of us with high probability. Blocking PPE production and organized pandemic responses still won’t stop people from self-isolating, doing no contact food deliveries, etc., although things would be tough, and deliveries and food production would be good targets for drone strikes. It could be bad if lethal pathogens become widespread and practically unremovable in our food/​water, or if food production is otherwise consistently attacked, but the militaries would probably step in to protect the food/​water supplies.

I think, overall, there are too few ways to reliably and kill double or even single digit percentages of the human population with high probability and that can be combined to get basically everyone with high probability. I’m not saying there aren’t any, but I’m skeptical that there are enough. There are diminishing returns on doing the same ones (like pandemics) more, because of resistance, and enough people being personally very careful or otherwise difficult targets.

• 6 Jun 2022 16:39 UTC
LW: 58 AF: 24
8 ∶ 13
AF

Found this to be an interesting list of challenges, but I disagree with a few points. (Not trying to be comprehensive here, just a few thoughts after the first read-through.)

• Several of the points here are premised on needing to do a pivotal act that is way out of distribution from anything the agent has been trained on. But it’s much safer to deploy AI iteratively; increasing the stakes, time horizons, and autonomy a little bit each time. With this iterative approach to deployment, you only need to generalize a little bit out of distribution. Further, you can use Agent N to help you closely supervise Agent N+1 before giving it any power.

• One claim is that Capabilities generalize further than alignment once capabilities start to generalize far. The argument is that an agent’s world model and tactics will be automatically fixed by reasoning and data, but its inner objective won’t be changed by these things. I agree with the preceding sentence, but I would draw a different (and more optimistic) conclusion from it. That it might be possible to establish an agent’s inner objective when training on easy problems, when the agent isn’t very capable, such that this objective remains stable as the agent becomes more powerful.
Also, there’s empirical evidence that alignment generalizes surprisingly well: several thousand instruction following examples radically improve the aligned behavior on a wide distribution of language tasks (InstructGPT paper) a prompt with about 20 conversations gives much better behavior on a wide variety of conversational inputs (HHH paper). Making a contemporary language model well-behaved seems to be much easier than teaching it a new cognitive skill.

• Human raters make systematic errors—regular, compactly describable, predictable errors.… This is indeed one of the big problems of outer alignment, but there’s lots of ongoing research and promising ideas for fixing it. Namely, using models to help amplify and improve the human feedback signal. Because P!=NP it’s easier to verify proofs than to write them. Obviously alignment isn’t about writing proofs, but the general principle does apply. You can reduce “behaving well” to “answering questions truthfully” by asking questions like “did the agent follow the instructions in this episode?”, and use those to define the reward function. These questions are not formulated in formal language where verification is easy, but there’s reason to believe that verification is also easier than proof-generation for informal arguments.

• 6 Jun 2022 18:39 UTC
LW: 27 AF: 11
4 ∶ 0
AFParent

But it’s much safer to deploy AI iteratively; increasing the stakes, time horizons, and autonomy a little bit each time. With this iterative approach to deployment, you only need to generalize a little bit out of distribution. Further, you can use Agent N to help you closely supervise Agent N+1 before giving it any power.

My model of Eliezer claims that there are some capabilities that are ‘smooth’, like “how large a times table you’ve memorized”, and some are ‘lumpy’, like “whether or not you see the axioms behind arithmetic.” While it seems plausible that we can iteratively increase smooth capabilities, it seems much less plausible for lumpy capabilities.

A specific example: if you have a neural network with enough capacity to 1) memorize specific multiplication Q+As and 2) implement a multiplication calculator, my guess is that during training you’ll see a discontinuity in how many pairs of numbers it can successfully multiply.[1] It is not obvious to me whether or not there are relevant capabilities like this that we’ll “find with neural nets” instead of “explicitly programming in”; probably we will just build AlphaZero so that it uses MCTS instead of finding MCTS with gradient descent, for example.

[edit: actually, also I don’t think I get how you’d use a ‘smaller times table’ to oversee a ‘bigger times table’ unless you already knew how arithmetic worked, at which point it’s not obvious why you’re not just writing an arithmetic program.]

That it might be possible to establish an agent’s inner objective when training on easy problems, when the agent isn’t very capable, such that this objective remains stable as the agent becomes more powerful.

IMO this runs into two large classes of problems, both of which I put under the heading ‘ontological collapse’.

First, suppose the agent’s inner objective is internally located: “seek out pleasant tastes.” Then you run into 16 and 17, where you can’t quite be sure what it means by “pleasant tastes”, and you don’t have a great sense of what “pleasant tastes” will extrapolate to at the next level of capabilities. [One running “joke” in EA is that, on some theories of what morality is about, the highest-value universe is one which contains an extremely large number of rat brains on heroin. I think this is the correct extrapolation /​ maximization of at least one theory which produces good behavior when implemented by humans today, which makes me pretty worried about this sort of extrapolation.]

Second, suppose the agent’s inner objective is externally located: “seek out mom pressing the reward button”. Then you run into 18, which argues that once the agent realizes that the ‘reward button’ is an object in its environment instead of a communication channel between the human and itself, it may optimize for the object instead of ‘being able to hear what the human would freely communicate’ or whatever philosophically complicated variable it is that we care about. [Note that attempts to express this often need multiple patches and still aren’t fixed; “mom approves of you” can be coerced, “mom would freely approve of you” has a trouble where you have some freedom in identifying your concept of ‘mom’ which means you might pick one who happens to approve of you.]

there’s lots of ongoing research and promising ideas for fixing it.

I’m optimistic about this too, but… I want to make sure we’re looking at the same problem, or something? I think my sense is best expressed in Stanovich and West, where they talk about four responses to the presence of systematic human misjudgments. The ‘performance error’ response is basically the ‘epsilon-rationality’ assumption; 1-ε of the time humans make the right call, and ε of the time they make a random call. While a fine model of performance errors, it doesn’t accurately predict what’s happening with systematic errors, which are predictable instead of stochastic.

I sometimes see people suggest that the model should always or never conform to the human’s systematic errors, but it seems to me like we need to somehow distinguish between systematic “errors” that are ‘value judgments’ (“oh, it’s not that the human prefers 5 deaths to 1 death, it’s that they are opposed to this ‘murder’ thing that I should figure out”) and systematic errors that are ‘bounded rationality’ or ‘developmental levels’ (“oh, it’s not that the (very young) human prefers less water to more water, it’s that they haven’t figured out conservation of mass yet”). It seems pretty sad if we embed all of our confusions into the AI forever—and also pretty sad if we end up not able to transfer any values because all of them look like confusions.[2]

[1] This might depend on what sort of curriculum you train it on; I was imagining something like 1) set the number of digits N=1, 2) generate two numbers uniformly at random between 1 and 2^N, pass them as inputs (sequence of digits?), 3) compare the sequence of digits outputted to the correct answer, either with a binary pass/​fail or some sort of continuous similarity metric (so it gets some points for 12x12 = 140 or w/​e); once it performs at 90% success check the performance for increased N until you get one with below 80% success and continue training. In that scenario, I think it just memorizes until N is moderately sized (8?), at which point it figures out how to multiply, and then you can increasing N lots without losing accuracy (until you hit some overflow error in its implementation of multiplication from having large numbers).

[2] I’m being a little unfair in using the trolley problem as an example of a value judgment, because in my mind the people who think you shouldn’t pull the lever because it’s murder are confused or missing a developmental jump—but I have the sense that for most value judgments we could find, we can find some coherent position which views it as confused in this way.

• Re: smooth vs bumpy capabilities, I agree that capabilities sometimes emerge abruptly and unexpectedly. Still, iterative deployment with gradually increasing stakes is much safer than deploying a model to do something totally unprecedented and high-stakes. There are multiple ways to make deployment more conservative and gradual. (E.g., incrementally increase the amount of work the AI is allowed to do without close supervision, incrementally increase the amount of KL-divergence between the new policy and a known-to-be-safe policy.)

Re: ontological collapse, there are definitely some tricky issues here, but the problem might not be so bad with the current paradigm, where you start with a pretrained model (which doesn’t really have goals and isn’t good at long-horizon control), and fine-tune it (which makes it better at goal-directed behavior). In this case, most of the concepts are learned during the pretraining phase, not the fine-tuning phase where it learns goal-directed behavior.

• 7 Jun 2022 3:54 UTC
LW: 6 AF: 3
0 ∶ 0
AFParent

Still, iterative deployment with gradually increasing stakes is much safer than deploying a model to do something totally unprecedented and high-stakes.

I agree with the “X is safer than Y” claim; I am uncertain whether it’s practically available to us, and much more worried in worlds where it isn’t available.

incrementally increase the amount of KL-divergence between the new policy and a known-to-be-safe policy

For this specific proposal, when I reframe it as “give the system a KL-divergence budget to spend on each change to its policy” I worry that it works against a stochastic attacker but not an optimizing attacker; it may be the case that every known-to-be-safe policy has some unsafe policy within a reasonable KL-divergence of it, because the danger can be localized in changes to some small part of the overall policy-space.

the problem might not be so bad with the current paradigm, where you start with a pretrained model (which doesn’t really have goals and isn’t good at long-horizon control), and fine-tune it (which makes it better at goal-directed behavior). In this case, most of the concepts are learned during the pretraining phase, not the fine-tuning phase where it learns goal-directed behavior.

Yeah, I agree that this seems pretty good. I do naively guess that when you do the fine-tuning, it’s the concepts that are most related to the goals who change the most (as they have the most gradient pressure on them); it’d be nice to know how much this is the case, vs. most of the relevant concepts being durable parts of the environment that were already very important for goal-free prediction.

• Several of the points here are premised on needing to do a pivotal act that is way out of distribution from anything the agent has been trained on. But it’s much safer to deploy AI iteratively; increasing the stakes, time horizons, and autonomy a little bit each time.

To do what, exactly, in this nice iterated fashion, before Facebook AI Research destroys the world six months later? What is the weak pivotal act that you can perform so safely?

Human raters make systematic errors—regular, compactly describable, predictable errors.… This is indeed one of the big problems of outer alignment, but there’s lots of ongoing research and promising ideas for fixing it. Namely, using models to help amplify and improve the human feedback signal. Because P!=NP it’s easier to verify proofs than to write them.

When the rater is flawed, cranking up the power to NP levels blows up the P part of the system.

• To do what, exactly, in this nice iterated fashion, before Facebook AI Research destroys the world six months later? What is the weak pivotal act that you can perform so safely?

Do alignment & safety research, set up regulatory bodies and monitoring systems.

When the rater is flawed, cranking up the power to NP levels blows up the P part of the system.

Not sure exactly what this means. I’m claiming that you can make raters less flawed, for example, by decomposing the rating task, and providing model-generated critiques that help with their rating. Also, as models get more sample efficient, you can rely more on highly skilled and vetted raters.

• 7 Jun 2022 4:30 UTC
LW: 30 AF: 15
6 ∶ 0
AFParent

Not sure exactly what this means.

My read was that for systems where you have rock-solid checking steps, you can throw arbitrary amounts of compute at searching for things that check out and trust them, but if there’s any crack in the checking steps, then things that ‘check out’ aren’t trustable, because the proposer can have searched an unimaginably large space (from the rater’s perspective) to find them. [And from the proposer’s perspective, the checking steps are the real spec, not whatever’s in your head.]

In general, I think we can get a minor edge from “checking AI work” instead of “generating our own work” and that doesn’t seem like enough to tackle ‘cognitive megaprojects’ (like ‘cure cancer’ or ‘develop a pathway from our current society to one that can reliably handle x-risk’ or so on). Like, I’m optimistic about “current human scientists use software assistance to attempt to cure cancer” and “an artificial scientist attempts to cure cancer” and pretty pessimistic about “current human scientists attempt to check the work of an artificial scientist that is attempting to cure cancer.” It reminds me of translators who complained pretty bitterly about being given machine-translated work to ‘correct’; they basically still had to do it all over again themselves in order to determine whether or not the machine had gotten it right, and so it wasn’t nearly as much of a savings as hoped.

Like the value of ‘DocBot attempts to cure cancer’ is that DocBot can think larger and wider thoughts than humans, and natively manipulate an opaque-to-us dense causal graph of the biochemical pathways in the human body, and so on; if you insist on DocBot only thinking legible-to-human thoughts, then it’s not obvious it will significantly outperform humans.

• If Facebook AI research is such a threat, wouldn’t it be possible to talk to Yann LeCun?

• To do what, exactly, in this nice iterated fashion, before Facebook AI Research destroys the world six months later? What is the weak pivotal act that you can perform so safely?

Produce the Textbook From The Future that tells us how to do AGI safely. That said, getting an AGI to generate a correct Foom safety textbook or AGI Textbook from the future would be incredibly difficult, it would be very possible for an AGI to slip in a subtle hard-to-detect inaccuracy that would make it worthless, verifying that it is correct would be very difficult, and getting all humans on earth to follow it would be very difficult.

• 8 Jun 2022 3:17 UTC
LW: 55 AF: 15
10 ∶ 5
AF

-3. I’m assuming you are already familiar with some basics, and already know what ‘orthogonality’ and ‘instrumental convergence’ are and why they’re true.

I think this is actually the part that I most “disagree” with. (I put “disagree” in quotes, because there are forms of these theses that I’m persuaded by. However, I’m not so confident that they’ll be relevant for the kinds of AIs we’ll actually build.)

1. The smart part is not the agent-y part

It seems to me that what’s powerful about modern ML systems is their ability to do data compression /​ pattern recognition. That’s where the real cognitive power (to borrow Eliezer’s term) comes from. And I think that this is the same as what makes us smart.

GPT-3 does unsupervised learning on text data. Our brains do predictive processing on sensory inputs. My guess (which I’d love to hear arguments against!) is that there’s a true and deep analogy between the two, and that they lead to impressive abilities for fundamentally the same reason.

If so, it seems to me that that’s where all the juice is. That’s where the intelligence comes from. (In the past, I’ve called this the core smarts of our brains.)

On this view, all the agent-y, planful, System 2 stuff that we do is the analogue of prompt programming. It’s a set of not-very-deep, not-especially-complex algorithms meant to cajole the actually smart stuff into doing something useful.

When I try to extrapolate what this means for how AI systems will be built, I imagine a bunch of Drexler-style AI services.

Yes, in some cases people will want to chain services together to form something like an agent, with something like goals. However, the agent part isn’t the smart part. It’s just some simple algorithms on top of a giant pile of pattern recognition and data compression.

Why is that relevant? Isn’t an algorithmically simple superintelligent agent just as scary as (if not moreso than) a complex one? In a sense yes, it would still be very scary. But to me it suggests a different intervention point.

If the agency is not inextricably tied to the intelligence, then maybe a reasonable path forward is to try to wring as much productivity as we can out of the passive, superhuman, quasi-oracular just-dumb-data-predictors. And avoid as much as we can ever creating closed-loop, open-ended, free-rein agents.

Am I just recapitulating the case for Oracle-AI /​ Tool-AI? Maybe so.

But if agency is not a fundamental part of intelligence, and rather something that can just be added in on top, or not, and if we’re at a loss for how to either align a superintelligent agent with CEV or else make it corrigible, then why not try to avoid creating the agent part of superintelligent agent?

I think that might be easier than many think...

2. The AI does not care about your atoms either

The AI does not hate you, nor does it love you, but you are made out of atoms which it can use for something else.

https://​​intelligence.org/​​files/​​AIPosNegFactor.pdf

Suppose we have (something like) an agent, with (something like) a utility function. I think it’s important to keep in mind the domain of the utility function. (I’ll be making basically the same point repeatedly throughout the rest of this comment.)

By default, I don’t expect systems that we build, with agent-like behavior (even superintelligently smart systems!), to care about all the atoms in the future light cone.

Humans (and other animals) care about atoms. We care about (our sensory perceptions of) macroscopic events, forward in time, because we evolved to. But that is not the default domain of an agent’s utility function.

For example, I claim that while AlphaGo could be said to be agent-y, it does not care about atoms. And I think that we could make it fantastically more superhuman at Go, and it would still not care about atoms. Atoms are just not in the domain of its utility function.

In particular, I don’t think it has an incentive to break out into the real world to somehow get itself more compute, so that it can think more about its next move. It’s just not modeling the real world at all. It’s not even trying to rack up a bunch of wins over time. It’s just playing the single platonic game of Go.

Giant caveat (that you may already be shouting into your screen): abstractions are leaky.

The ML system is not actually trained to play the platonic game of Go. It’s trained to play the-game-of-Go-as-implemented-on-particular-hardware, or something like minimize-this-loss-function-informed-by-Go-game-results. The difference between the platonic game and the embodied game can lead to clever and unexpected behavior.

However, it seems to me that these kinds of hacks are going to look a lot more like a system short-circuiting than it out-of-nowhere building a model of, and starting to care about, the whole universe.

3. Orthogonality squared

I really liked Eliezer’s Arbital article on Epistemic and instrumental efficiency. He writes:

An agent that is “efficient”, relative to you, within a domain, is one that never makes a real error that you can systematically predict in advance.

I think this very succinctly captures what would be so scary about being up against a (sufficiently) superintelligent agent with conflicting goals to yours. If you think you see a flaw in its plan, that says more about your seeing than it does about its plan. In other words, you’re toast.

But as above, I think it’s important to keep in mind what an agent’s goals are actually about.

Just as the utility function of an agent is orthogonal from its intelligence, it seems to me that the domain of its utility function is another dimension of potential orthogonality.

If you’re playing chess against AlphaZero Chess, you’re going to lose. But suppose you’re secretly playing “Who has the most pawns after 10 moves?” I think you’ve got a chance to win! Even though it cares about pawns!

(Of course if you continue playing out the chess game after the10th move, it’ll win at that. But by assumption, that’s fine, it’s not what you cared about.)

If you and another agent have different goals for the same set of objects, you’re going to be in conflict. It’s going to be zero sum. But if the stuff you care about is only tangentially related to the stuff it cares about, then the results can be positive sum. You can both win!

In particular, you can both get what you want without either of you turning the other off. (And if you know that, you don’t have to preemptively try to turn each other off to prevent being turned off either.)

4. Programs, agents, and real-world agents

Agents are a tiny subset of all programs. And agents whose utility functions are defined over the real world are a tiny subset of all agents.

If we think about all the programs we could potentially write that take in inputs and produce outputs, it will make sense to talk about some of those as agents. These are the programs that seem to be optimizing something. Or seem to have goals and make plans.

But, crucially, all that optimization takes place with respect to some environment. And if the input and output of an agent-y program is hooked up to the wrong environment (or hooked up to the right environment in the wrong way), it’ll cease to be agent-y.

For example, if you hook me up to the real world by sticking me in outer space (sans suit), I will cease to be very agent-y. Or, if you hook up the inputs and outputs of AlphaGo to a chess board, it will cease to be formidable (until you retrain it). (In other words, the isAgent() predicate is not a one-place function.)

This suggests to me that we could build agent-y, superintelligent systems that are not a threat to us. (Because they are not agent-y with respect to the real world.)

Yes, we’re likely to (drastically) oversample from the subset of agents that are agent-y w.r.t. the real world, because we’re going to want to build systems that are useful to us.

But if I’m right about the short-circuiting argument above, even our agent-y systems won’t have coherent goals defined over events far outside their original domain (e.g. the arrangement of all the atoms in the future light cone) by default.

So even if our systems are agent-y (w.r.t. some environment), and have some knowledge of and take some actions in the real world, they won’t automatically have a utility function defined over the configurations of all atoms.

On the other hand, the more we train them as open-ended agents with wide remit to act in the real world (or a simulation thereof), the more we’ll have a (potentially superintelligently lethal) problem on our hands.

To me that suggests that what we need to care about are things like: how open-ended we make our systems, whether we train them via evolution-like competition between agents in a high-def simulation of the real world, and what kind of systems are incentivized to be developed and deployed, society-wide.

5. Conclusion

If I’m right in the above thinking, then orthogonality is more relevant and instrumental convergence is less relevant than it might otherwise appear.

Instrumental convergence would only end up being a concern for agents that care about the same objects /​ resources /​ domain that you do. If their utility function is just not about those things, IC will drive them to acquire a totally different set of resources that is not in conflict with your resources (e.g. a positional chess advantage in a go game, or trading for your knight while you try to acquire pawns).

This would mean that we need to be very worried about open-ended real-world agents. But less worried about intelligence in general, or even agents in general.

To be clear, I’m not claiming that it’s all roses from here on out. But this reasoning leads me to conclude that the key problems may not be the ones described in the post above.

• GPT-3 does unsupervised learning on text data. Our brains do predictive processing on sensory inputs. My guess (which I’d love to hear arguments against!) is that there’s a true and deep analogy between the two, and that they lead to impressive abilities for fundamentally the same reason.

Agree that self-supervised learning powers both GPT-3 updates and human brain world-model updates (details & caveats). (Which isn’t to say that GPT-3 is exactly the same as the human brain world-model—there are infinitely many different possible ML algorithms that all update via self-supervised learning).

However…

If so, it seems to me that that’s where all the juice is. That’s where the intelligence comes from … if agency is not a fundamental part of intelligence, and rather something that can just be added in on top, or not, and if we’re at a loss for how to either align a superintelligent agent with CEV or else make it corrigible, then why not try to avoid creating the agent part of superintelligent agent?

I disagree; I think the agency is necessary to build a really good world-model, one that includes new useful concepts that humans have never thought of.

Without the agency, some of the things that you lose are (and these overlap): Intelligently choosing what to attend to; intelligently choosing what to think about; intelligently choosing what book to re-read and ponder; intelligently choosing what question to ask; ability to learn and use better and better brainstorming strategies and other such metacognitive heuristics.

See my discussion here (Section 7.2) for why I think these things are important if we want the AGI to be able to do things like invent new technology or come up with new good ideas in AI alignment.

You can say: “We’ll (1) make an agent that helps build a really good world-model, then (2) turn off the agent and use /​ query the world-model by itself”. But then step (1) is the dangerous part.

• I disagree; I think the agency is necessary to build a really good world-model, one that includes new useful concepts that humans have never thought of.

Without the agency, some of the things that you lose are (and these overlap): Intelligently choosing what to attend to; intelligently choosing what to think about; intelligently choosing what book to re-read and ponder; intelligently choosing what question to ask; ability to learn and use better and better brainstorming strategies and other such metacognitive heuristics.

Why is agency necessary for these things?

If we follow Ought’s advice and build “process-based systems [that] are built on human-understandable task decompositions, with direct supervision of reasoning steps”, do you expect us to hit a hard wall somewhere that prevents these systems from creatively choosing things to think about, books to read, or better brainstorming strategies?

• Why is agency necessary for these things?

(Copying from here:)

Let’s compare two things: “trying to get a good understanding of some domain by building up a vocabulary of concepts and their relations” versus “trying to win a video game”. At a high level, I claim they have a lot in common!

• In both cases, there are a bunch of possible “moves” you can make (you could think the thought “what if there’s some analogy between this and that?”, or you could think the thought “that’s a bit of a pattern; does it generalize?”, etc. etc.), and each move affects subsequent moves, in an exponentially-growing tree of possibilities.

• In both cases, you’ll often get some early hints about whether moves were wise, but you won’t really know that you’re on the right track except in hindsight.

• And in both cases, I think the only reliable way to succeed is to have the capability to repeatedly try different things, and learn from experience what paths and strategies are fruitful.

Therefore (I would argue), a human-level concept-inventing AI needs “RL-on-thoughts”—i.e., a reinforcement learning system, in which “thoughts” (edits to the hypothesis space /​ priors /​ world-model) are the thing that gets rewarded. The human brain certainly has that. You can be lying in bed motionless, and have rewarding thoughts, and aversive thoughts, and new ideas that make you rethink something you thought you knew.

Unfortunately, I also believe that RL-on-thoughts is really dangerous by default. Here’s why.

Again suppose that we want an AI that gets a good understanding of some domain by building up a vocabulary of concepts and their relations. As discussed above, we do this via an RL-on-thoughts AI. Consider some of the features that we plausibly need to put into this RL-on-thoughts system, for it to succeed at a superhuman level:

• Developing and pursuing instrumental subgoals—for example, suppose the AI is “trying” to develop concepts that will make it superhumanly competent at assisting a human microscope inventor. We want it to be able to “notice” that there might be a relation between lenses and symplectic transformations, and then go spend some compute cycles developing a better understanding of symplectic transformations. For this to happen, we need “understand symplectic transformations” to be flagged as a temporary sub-goal, and to be pursued, and we want it to be able to spawn further sub-sub-goals and so on.

• Consequentialist planning—Relatedly, we want the AI to be able to summon and re-read a textbook on linear algebra, or mentally work through an example problem, because it anticipates that these activities will lead to better understanding of the target domain.

• Meta-cognition—We want the AI to be able to learn patterns in which of its own “thoughts” lead to better understanding and which don’t, and to apply that knowledge towards having more productive thoughts.

Putting all these things together, it seems to me that the default for this kind of AI would be to figure out that “seizing control of its off-switch” would be instrumentally useful for it to do what it’s trying to do (i.e. develop a better understanding of the target domain, presumably), and then to come up with a clever scheme to do so, and then to do it. So like I said, RL-on-thoughts seems to me to be both necessary and dangerous.

(Does that count as “agency”? I don’t know, it depends on what you mean by “agency”.)

In terms of the “task decomposition” strategy, this might be a tricky to discuss because you probably have a more detailed picture in your mind than I do. I’ll try anyway.

It seems to me that the options are:

(1) the subprocess only knows its narrow task (“solve this symplectic geometry homework problem”), and is oblivious to the overall system goal (“design a better microscope”), or

(2) the subprocess is aware of the overall system goal and chooses actions in part to advance it.

In Case (2), I’m not sure this really counts as “task decomposition” in the first place, or how this would help with safety.

In Case (1), yes I expect systems to hit a hard wall—I’m skeptical that tasks we care about decompose cleanly.

For example, at my last job, I would often be part of a team inventing a new gizmo, and it was not at all unusual for me to find myself sketching out the algorithms and sketching out the link budget and scrutinizing laser spec sheets and scrutinizing FPGA spec sheets and nailing down end-user requirements, etc. etc. Not because I’m individually the best person at each of those tasks—or even very good!—but because sometimes a laser-related problem is best solved by switching to a different algorithm, or an FPGA-related problem is best solved by recognizing that the real end-user requirements are not quite what we thought, etc. etc. And that kind of design work is awfully hard unless a giant heap of relevant information and knowledge is all together in a single brain /​ world-model.

In the case of my current job doing AI alignment research, I sometimes come across small self-contained tasks that could be delegated, but I would have no idea how to decompose most of what I do. (E.g. writing this comment!)

So why do bureaucracies (and large organizations more generally) fail so badly?

My main model for this is that interfaces are a scarce resource. Or, to phrase it in a way more obviously relevant to factorization: it is empirically hard for humans to find good factorizations of problems which have not already been found. Interfaces which neatly split problems are not an abundant resource (at least relative to humans’ abilities to find/​build such interfaces). If you can solve that problem well, robustly and at scale, then there’s an awful lot of money to be made.

Also, one major sub-bottleneck (though not the only sub-bottleneck) of interface scarcity is that it’s hard to tell who has done a good job on a domain-specific problem/​question without already having some domain-specific background knowledge. This also applies at a more “micro” level: it’s hard to tell whose answers are best without knowing lots of context oneself.

A possible example of a seemingly-hard-to-decompose task would be: Until 1948, no human had ever thought of the concept of “information entropy”. Then Claude Shannon sat down and invented this new useful concept. Make an AI that can do things like that.

(Even if I’m correct that process-based task-decomposition hits a wall, that’s not to say that it doesn’t have room for improvement over today’s AI. The issue is (1) outcome-based systems are dangerous; (2) given enough time, people will presumably build them anyway. And the goal is to solve that problem, either by a GPU-melting-nanobot type of plan, or some other better plan. Is there such a plan that we can enact using a process-based task-decomposition AI? Eliezer believes (see point 7) that the answer is “no”. I would say the answer is: “I guess maybe, but I can’t think of any”. I don’t know what type of plan you have in mind. Sorry if you already talked about that and I missed it. :) )

• FWIW self-supervised learning can be surprisingly capable of doing things that we previously only knew how to do with “agentic” designs. From that link: classification is usually done with an objective + an optimization procedure, but GPT-3 just does it.

• For example, I claim that while AlphaGo could be said to be agent-y, it does not care about atoms. And I think that we could make it fantastically more superhuman at Go, and it would still not care about atoms. Atoms are just not in the domain of its utility function.

In particular, I don’t think it has an incentive to break out into the real world to somehow get itself more compute, so that it can think more about its next move. It’s just not modeling the real world at all. It’s not even trying to rack up a bunch of wins over time. It’s just playing the single platonic game of Go.

I would distinguish three ways in which different AI systems could be said to “not care about atoms”:

1. The system is thinking about a virtual object (e.g., a Go board in its head), and it’s incapable of entertaining hypotheses about physical systems. Indeed, we might add the assumption that it can’t entertain hypotheses like ‘this Go board I’m currently thinking about is part of a larger universe’ at all. (E.g., there isn’t some super-Go-board I and/​or the board are embedded in.)

2. The system can think about atoms/​physics, but it only terminally cares about digital things in a simulated environment (e.g., winning Go), and we’re carefully keeping it from ever learning that it’s inside a simulation /​ that there’s a larger reality it can potentially affect.

3. The system can think about atoms/​physics, and it knows that our world exists, but it still only terminally cares about digital things in the simulated environment.

Case 3 is not safe, because controlling the physical world is a useful way to control the simulation you’re in. (E.g., killing all agents in base reality ensures that they’ll never shut down your simulation.)

Case 2 is potentially safe but fragile, because you’re relying on your ability to trick/​outsmart an alien mind that may be much smarter than you. If you fail, this reduces to case 3.

(Also, it’s not obvious to me that you can do a pivotal act using AGI-grade reasoning about simulations. Which matters if other people are liable to destroy the world with case-3 AGIs, or just with ordinary AGIs that terminally value things about the physical world.)

Case 1 strikes me as genuinely a lot safer, but a lot less useful. I don’t expect humanity to be satisfied with those sorts of AI systems, or to coordinate to only ever build them—like, I don’t expect any coordination here. And I’m not seeing a way to leverage a system like this to save the world, given that case-2, 3, etc. systems will eventually exist too.

• Case 3 is not safe, because controlling the physical world is a useful way to control the simulation you’re in. (E.g., killing all agents in base reality ensures that they’ll never shut down your simulation.)

In my mind, this is still making the mistake of not distinguishing the true domain of the agent’s utility function from ours.

Whether the simulation continues to be instantiated in some computer in our world is a fact about our world, not about the simulated world.

AlphaGo doesn’t care about being unplugged in the middle of a game (unless that dynamic was part of its training data). It cares about the platonic game of go, not about the instantiated game it’s currently playing.

We need to worry about leaky abstractions, as per my original comment. So we can’t always assume the agent’s domain is what we’d ideally want it to be.

But I’m trying to highlight that it’s possible (and I would tentatively go further and say probable) for agents not to care about the real world.

To me, assuming care about the real world (including wanting not to be unplugged) seems like a form of anthropomorphism.

For any given agent-y system I think we need to analyze whether it in particular would come to care about real world events. I don’t think we can assume in general one way or the other.

• AlphaGo doesn’t care about being unplugged in the middle of a game (unless that dynamic was part of its training data). It cares about the platonic game of go, not about the instantiated game it’s currently playing.

What if the programmers intervene mid-game to give the other side an advantage? Does a Go AGI, as you’re thinking of it, care about that?

I’m not following why a Go AGI (with the ability to think about the physical world, but a utility function that only cares about states of the simulation) wouldn’t want to seize more hardware, so that it can think better and thereby win more often in the simulation; or gain control of its hardware and directly edit the simulation so that it wins as many games as possible as quickly as possible.

Why would having a utility function that only assigns utility based on X make you indifferent to non-X things that causally affect X? If I only terminally cared about things that happened a year from now, I would still try to shape the intervening time because doing so will change what happens a year from now.

(This is maybe less clear in the case of shutdown, because it’s not clear how an agent should think about shutdown if its utility is defined states of its simulation. So I’ll set that particular case aside.)

• A Go AI that learns to play go via reinforcement learning might not “have a utility function that only cares about winning Go”. Using standard utility theory, you could observe its actions and try to rationalise them as if they were maximising some utility function, and the utility function you come up with probably wouldn’t be “win every game of Go you start playing” (what you actually come up with will depend, presumably, on algorithmic and training regime details). The reason why the utility function is slippery is that it’s fundamentally an adaptation executor, not a utility maxmiser.

• 3. The system can think about atoms/​physics, and it knows that our world exists, but it still only terminally cares about digital things in the simulated environment.

Case 3 is not safe, because controlling the physical world is a useful way to control the simulation you’re in. (E.g., killing all agents in base reality ensures that they’ll never shut down your simulation.)

Not necessarily. Train something multimodally on digital games of Go and on, say, predicting the effects of modifications to its own code on its success at Go. It could be a) good at go and b) have some real understanding of “real world actions” that make it better at Go, and still not actually take any real world actions to make it better at Go, even if it had the opportunity. You could modify the training to make it likely to do so—perhaps by asking it to either make a move or to produce descendants that make better choices—but if you don’t do this then it seems entirely plausible, and even perhaps likely, that it develops an understanding of self-modification and of go playing without ever self-modifying in order to play go better. Its goal, so to speak, is “play go with the restriction of using only legal game moves”.

Edit—forget the real world, here’s an experiment:

Train a board game playing AI with two modes of operation: game state x move → outcome and game state → best move. Subtle difference: in the first mode of operation, the move has a “cheat button” that, when pressed, always results in a win. In the second, it can output cheat button presses, but it has no effect on winning or losing.

Question is: does it learn to press the cheat button? I’m really not sure. Could you prevent it from learning to press the cheat button if training feedback is never allowed to depend on whether or not this button was pressed? That seems likely.

• Can you visualize an agent that is not “open-ended” in the relevant ways, but is capable of, say, building nanotech and melting all the GPUs?

In my picture most of the extra sauce you’d need on top of GPT-3 looks very agenty. It seems tricky to name “virtual worlds” in which AIs manipulate just “virtual resources” and still manage to do something like melting the GPUs.

• maybe a reasonable path forward is to try to wring as much productivity as we can out of the passive, superhuman, quasi-oracular just-dumb-data-predictors. And avoid as much as we can ever creating closed-loop, open-ended, free-rein agents.

I should say that I do see this as a reasonable path forward! But we don’t seem to be coordinating to do this, and AI researchers seem to love doing work on open-ended agents, which sucks.

Hm, regardless it doesn’t really move the needle, so long as people are publishing all of their work. Developing overpowered pattern recognizers is similar to increasing our level of hardware overhang. People will end up using them as components of systems that aren’t safe.

• Hm, regardless it doesn’t really move the needle, so long as people are publishing all of their work. Developing overpowered pattern recognizers is similar to increasing our level of hardware overhang. People will end up using them as components of systems that aren’t safe.

I strongly disagree. Gain of function research happens, but it’s rare because people know it’s not safe. To put it mildly, I think reducing the number of dangerous experiments substantially improves the odds of no disaster happening over any given time frame

• Can you visualize an agent that is not “open-ended” in the relevant ways, but is capable of, say, building nanotech and melting all the GPUs?

FWIW, I’m not sold on the idea of taking a single pivotal act. But, engaging with what I think is the real substance of the question — can we do complex, real-world, superhuman things with non-agent-y systems?

Yes, I think we can! Just as current language models can be prompt-programmed into solving arithmetic word problems, I think a future system could be led to generate a GPU-melting plan, without it needing to be a utility-maximizing agent.

For a very hand-wavy sketch of how that might go, consider asking GPT-N to generate 1000s of candidate high-level plans, then rate them by feasibility, then break each plan into steps and re-evaluate, etc.

Or, alternatively, imagine the cognitive steps you might take if you were trying to come up with a GPU-melting plan (or alternatively a pivotal act plan in general). Do any of those steps really require that you have a utility function or that you’re a goal-directed agent?

It seems to me that we need some form of search, and discrimination and optimization. But not necessarily anymore than GPT-3 already has. (It would just need to be better at the search. And we’d need to make many many passes through the network to complete all the cognitive steps.)

On your view, what am I missing here?

• Is GPT-3 already more of an agent than I realize? (If so, is it dangerous?)

• Will GPT-N by default be more of an agent than GPT-3?

• Are our own thought processes making use of goal-directedness more than I realize?

• Will prompt-programming passive systems hit a wall somewhere?

• If so, what are some of the simplest cognitive tasks that we can do that you think such systems wouldn’t be able to do?

• (See also my similar question here.)

• For a very hand-wavy sketch of how that might go, consider asking GPT-N to generate 1000s of candidate high-level plans, then rate them by feasibility, then break each plan into steps and re-evaluate, etc

FWIW, I’d call this “weakly agentic” in the sense that you’re searching through some options, but the number of options you’re looking through is fairly small.

It’s plausible that this is enough to get good results and also avoid disasters, but it’s actually not obvious to me. The basic reason: if the top 1000 plans are good enough to get superior performance, they might also be “good enough” to be dangerous. While it feels like there’s some separation between “useful and safe” and “dangerous” plans and this scheme might yield plans all of the former type, I don’t presently see a stronger reason to believe that this is true.

• Separately from whether the plans themselves are safe or dangerous, I think the key question is whether the process that generated the plans is trying to deceive you (so it can break out into the real world or whatever).

If it’s not trying to deceive you, then it seems like you can just build in various safeguards (like asking, “is this plan safe?”, as well as more sophisticated checks), and be okay.

• >then rate them by feasibility,

I mean, literal GPT is just going to have poor feasibility ratings for novel engineering concepts.

>Do any of those steps really require that you have a utility function or that you’re a goal-directed agent?

Yes, obviously. You have to make many scientific and engineering discoveries, which involves goal-directed investigation.

> Are our own thought processes making use of goal-directedness more than I realize?

Yes, you know which ideas make sense by generalizing from ideas more closely tied in with the actions you take directed towards living.

• What do you think of a claim like “most of the intelligence comes from the steps where you do most of the optimization”? A corollary of this is that we particularly want to make sure optimization intensive steps of AI creation are safe WRT not producing intelligent programs devoted to killing us.

Example: most of the “intelligence” of language models comes from the supervised learning step. However, it’s in-principle plausible that we could design e.g. some really capable general purpose reinforcement learner where the intelligence comes from the reinforcement, and the latter could (but wouldn’t necessarily) internalise “agenty” behaviour.

I have a vague impression that this is already something other people are thinking about, though maybe I read too much into some tangential remarks in this direction. E.g. I figured the concern about mesa-optimizers was partly motivated by the idea that we can’t always tell when an optimization intensive step is taking place.

I can easily imagine people blundering into performing unsafe optimization-intensive AI creation processes. Gain of function pathogen research would seem to be a relevant case study here, except we currently have less idea about what kind of optimization makes deadly AIs vs what kind of optimization makes deadly pathogens. One of the worries (again, maybe I’m reading too far into comments that don’t say this explicitly) is that the likelihood of such a blunder approaches 1 over long enough times, and the “pivotal act” framing is supposed to be about doing something that could change this (??)

That said, it seems that there’s a lot that could be done to make it less likely in short time frames.

• What do you think of a claim like “most of the intelligence comes from the steps where you do most of the optimization”? A corollary of this is that we particularly want to make sure optimization intensive steps of AI creation are safe WRT not producing intelligent programs devoted to killing us.

This seems probably right to me.

Example: most of the “intelligence” of language models comes from the supervised learning step. However, it’s in-principle plausible that we could design e.g. some really capable general purpose reinforcement learner where the intelligence comes from the reinforcement, and the latter could (but wouldn’t necessarily) internalise “agenty” behaviour.

I agree that reinforcement learners seem more likely to be agent-y (and therefore scarier) than self-supervised learners.

• 13 Jun 2022 20:37 UTC
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I think until recently, I’ve been consistently more pessimistic than Eliezer about AI existential safety. Here’s a 2004 SL4 post for example where I tried to argue against MIRI (SIAI at the time) trying to build a safe AI (and again in 2011). I’ve made my own list of sources of AI risk that’s somewhat similar to this list. But it seems to me that there are still various “outs” from certain doom, such that my probability of a good outcome is closer to 20% (maybe a range of 10-30% depending on my mood) than 1%.

1. Human thought partially exposes only a partially scrutable outer surface layer. Words only trace our real thoughts. Words are not an AGI-complete data representation in its native style. The underparts of human thought are not exposed for direct imitation learning and can’t be put in any dataset. This makes it hard and probably impossible to train a powerful system entirely on imitation of human words or other human-legible contents, which are only impoverished subsystems of human thoughts; unless that system is powerful enough to contain inner intelligences figuring out the humans, and at that point it is no longer really working as imitative human thought.

One of the biggest “outs” I see is that it turns out to be not that hard “to train a powerful system entirely on imitation of human words or other human-legible contents”, we (e.g., a relatively responsible AI lab) train such a system and then use it to differentially accelerate AI safety research. I definitely think that it’s very risky to rely on such black-box human imitations for existential safety, and that a competent civilization would be pursuing other plans where they can end up with greater certainty of success, but it seems there’s something like a 20% chance that it just works out anyway.

To explain my thinking a bit more, human children have to learn how to think human thoughts through “imitation of human words or other human-legible contents”. It’s possible that they can only do this successfully because their genes contain certain key ingredients that enable human thinking, but it also seems possible that children are just implementations of some generic imitation learning algorithm, so our artificial learning algorithms (once they become advanced/​powerful enough) won’t be worse at learning to think like humans. I don’t know how to rule out the latter possibility with very high confidence. Eliezer, if you do, can you please explain this more?

• 6 Jun 2022 0:54 UTC
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[This is a nitpick of the form “one of your side-rants went a bit too far IMO;” feel free to ignore]

The ability to do new basic work noticing and fixing those flaws is the same ability as the ability to write this document before I published it, which nobody apparently did, despite my having had other things to do than write this up for the last five years or so. Some of that silence may, possibly, optimistically, be due to nobody else in this field having the ability to write things comprehensibly—such that somebody out there had the knowledge to write all of this themselves, if they could only have written it up, but they couldn’t write, so didn’t try. … The fact that, twenty-one years into my entering this death game, seven years into other EAs noticing the death game, and two years into even normies starting to notice the death game, it is still Eliezer Yudkowsky writing up this list, says that humanity still has only one gamepiece that can do that.

The third option this seems to miss is that there are people who could have written this document, but they also thought they had better things to do than write it. I’m thinking of people like Paul Christiano, Nate Soares, John Wentworth, Ajeya Cotra… there are dozens of people who have thought deeply about this stuff and also talked with you (Yudkowsky) and I bet they could have written something approximately as good as this if they tried. Perhaps, like you, they decided to instead spend their time working directly on the problem.

I do agree with you that they seem to on average be way way too optimistic, but I don’t think it’s because they are ignorant of the considerations and arguments you’ve made here.

A big source of optimism for Paul, for example, seems to be his timelines + views about takeoff speeds, which are mostly independent of the claims made in this post. I too would be cautiously optimistic if I thought we had 30 years left and that by the time things really went crazy we’d have decades of experience with just-slightly-dumber systems automating big chunks of the economy & AI alignment would be a big prestigious field with lots of geniuses being mentored by older geniuses etc. (Many of the points you make here would still apply, so it would still be a pretty scary situation...)

• I’m thinking of people like Paul Christiano, Nate Soares, John Wentworth, Ajeya Cotra… [...] I do agree with you that they seem to on average be way way too optimistic, but I don’t think it’s because they are ignorant of the considerations and arguments you’ve made here.

I don’t think Nate is that much more optimistic than Eliezer, but I believe Eliezer thinks Nate couldn’t have generated enough of the list in the OP, or couldn’t have generated enough of it independently (“using the null string as input”).

• >too would be cautiously optimistic if I thought we had 30 years left

This is a bit of an aside but can I ask what the general opinion is on how many years we had left? Was your comment stating that it’s optimistic to think we have 30 years left before AGI, or optimistic about the remainder of the sentence?

• [ ]
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• Seems implausible. Other people have much more stamina than I do, hence more time in practice, even if they are simultaneously doing other things.

It’s admittedly true that nobody in this field except me can write things, in full generality; but one might have still expected a poorly written attempt to arise from somewhere if the knowledge-capability was widespread.

• Would MIRI be interested in hiring a full time staff writer/​editor? I feel like I could have produced a good chunk of this if I had thought I should try to, just from having hung around LessWrong since it was just Eliezer Yudkowsky and Robin Hanson blogging on Overcoming Bias, but I thought the basic “no, really, AI is going to kill us” arguments were already written up in other places, like Arbital and the book Superintelligence.

• Would MIRI be interested in hiring a full time staff writer/​editor?

This is sort of still Rob’s job, and it was my job from 2016-2019. If I recall correctly, my first major project was helping out with a document sort-of-like this document, which tried to explain to OpenPhil some details of the MIRI strategic view. [I don’t think this was ever made public, and might be an interesting thing to pull out of the archives and publish now?]

If I tried to produce this document from scratch, I think it would have been substantially worse, tho I think I might have been able to reduce the time from “Eliezer’s initial draft” to “this is published at all”.

• From the perspective of persuading an alignment-optimist in the AI world, this document could not possibly have been worse. I don’t know you Vaniver, but I’m confident you could have done a more persuasive job just by editing out the weird aspersion that EY is the only person capable of writing about alignment.

• I think you’re thinking of drafts mainly based on Nate’s thinking rather than Eliezer’s, but yeah, those are on my list of things to maybe release publicly in some form.

• Yeah, either that or paying for writing lessons for alignment researchers if they really have to write the post themselves.

• 6 Jun 2022 2:31 UTC
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How many people are there “in the field”? Fifty?

• 100~200 are the latest estimates I’ve heard for total number of people working on AI alignment.

I don’t have a great reference for that figure, but it’s compatible with this slide from State of AI Report 2021 which claims “fewer than 100 researchers work on AI Alignment in 7 leading AI organisations”, if you consider:

• The report excluded independent alignment researchers and smaller/​lesser-known AI organisations from their tally

• It’s at least a few months old [1] and the AI alignment field has been growing

--

[1]: I can’t tell if Benaich and Hogarth published this sometime in 2021 or at the beginning of 2022, after 2021 had ended. Either way it’s 5~18 months old.

• This document doesn’t look to me like something a lot of people would try to write. Maybe it was one of the most important things to write, but not obviously so. Among the steps (1) get the idea to write out all reasons for pessimism, (2) resolve to try, (3) not give up halfway through, and (4) be capable, I would not guess that 4 is the strongest filter.

• I don’t think I personally could have written it; if others think they could have, I’d genuinely be interested to hear them brag, even if they can’t prove it.

Maybe the ideal would be ‘I generated the core ideas of [a,b,c] with little or no argument from others; I had to be convinced of [d,e,f] but I now agree with them; I disagree with [g,h,i]; I think you left out important considerations [x,y,z].’ Just knowing people’s self-model is interesting to me, I don’t demand that everything you believe be immediately provable to me.

• I think as of early this year (like, January/​February, before I saw a version of this doc) I could have produced a pretty similar list to this one. I definitely would not derive it from the empty string in the closest world-without-Eliezer; I’m unsure how much I’d pay attention to AI alignment at all in that world. I’d very likely be working on agent foundations in that world, but possibly in the context of biology or AI capabilities rather than alignment. Arguments about AI foom and doom were obviously-to-me correct once I paid attention to them at all, but not something I’d have paid attention to on my own without someone pointing them out.

Some specifics about kind-of-doc I could have written early this year

• The framing around pivotal acts specifically was new-to-me when the late 2021 MIRI conversations were published. Prior to that, I’d have had to talk about how weak wish-granters are safe but not actually useful, and if we want safe AI which actually grants big wishes then we have to deal with the safety problems. Pivotal acts framing simplifies that part of the argument a lot by directly establishing a particular “big” capability which is necessary.

• By early this year, I think would have generated pretty similar points to basically everything in the post if I were trying to be really comprehensive. (In practice, writing a post like this, I would go for more unifying structure and thought-generators rather than comprehensiveness; I’d use the individual failure modes more as examples of their respective generators.)

• In my traversal-order of barriers, the hard conceptual barriers for which we currently have no solution even in principle (like e.g. 16-19) would get a lot more weight and detail; I spend less time thinking about what-I-mentally-categorize-as “the obvious things which go wrong with stupid approaches” (20, 21, 25-36).

• The earlier points are largely facts-about-the-world (e.g. 1, 2, 7-9, 12-15). For many of these, I would cite different evidence, although the conclusions remain the same. True facts are, as a general rule, overdetermined by evidence; there are many paths to them, and I didn’t always follow the same paths Eliezer does here.

• A few points I think are wrong (notably 18, 22, 24 to a limited extent), but are correct relative to the knowledge/​models which most proposals actually leverage. The loopholes there are things which you do need pretty major foundational/​conceptual work to actually steer through.

• I would definitely have generated some similar rants at the end, though of course not identical.

• One example: just yesterday I was complaining about how people seem to generate alignment proposals via a process of (1) come up with neat idea, (2) come up with some conditions under which that idea would maybe work (or at least not obviously fail in any of the ways the person knows to look for), (3) either claim that “we just don’t know” whether the conditions hold (without any serious effort to look for evidence), or directly look for evidence that they hold. Pretty standard bottom line failure.

I did briefly consider writing something along these lines after Eliezer made a similar comment to 39 in the Late 2021 MIRI Conversations. But as Kokotajlo guessed, I did not think that was even remotely close to the highest-value use of my time. It would probably take me a full month’s work to do it right, and the list just isn’t as valuable as my last month of progress. Or the month before that. Or the month before that.

• 6 Jun 2022 20:51 UTC
LW: 10 AF: 2
1 ∶ 0
AFParent

I’m curious about why you decided it wasn’t worth your time.

Going from the post itself, the case for publishing it goes something like “the whole field of AI Alignment is failing to produce useful work because people aren’t engaging with what’s actually hard about the problem and are ignoring all the ways their proposals are doomed; perhaps yelling at them via this post might change some of that.”

Accepting the premises (which I’m inclined to), trying to get the entire field to correct course seems actually pretty valuable, maybe even worth a month of your time, now that I think about it.

• First and foremost, I have been making extraordinarily rapid progress in the last few months, though most of that is not yet publicly visible.

Second, a large part of why people pour effort into not-very-useful work is that the not-very-useful work is tractable. Useless, but at least you can make progress on the useless thing! Few people really want to work on problems which are actually Hard, so people will inevitably find excuses to do easy things instead. As Eliezer himself complains, writing the list just kicks the can down the road; six months later people will have a new set of bad ideas with giant gaping holes in them. The real goal is to either:

• produce people who will identify the holes in their own schemes, repeatedly, until they converge to work on things which are actually useful despite being Hard, or

• get enough of a paradigm in place that people can make legible progress on actually-useful things without doing anything Hard.

I have recently started testing out methods for the former, but it’s the sort of thing which starts out with lots of tests on individuals or small groups to see what works. The latter, of course, is largely what my technical research is aimed at in the medium term.

(I also note that there will always be at least some need for people doing the Hard things, even once a paradigm is established.)

In the short term, if people want to identify the holes in their own schemes and converge to work on actually useful things, I think the “builder/​breaker” methodology that Paul uses in the ELK doc is currently a good starting point.

• Well, it’s the Law of Continued Failure, as Eliezer termed it himself, no? There’s already been a lot of rants about the real problems of alignment and how basically no-one focuses on them, most of them Eliezer-written as well. The sort of person who wasn’t convinced/​course-corrected by previous scattered rants isn’t going to be course-corrected by a giant post compiling all the rants in one place. Someone to whom this post would be of use is someone who’ve already absorbed all the information contained in it from other sources; someone who can already write it up on their own.

The picture may not be quite as grim as that, but yeah I can see how writing it would not be anyone’s top priority.

• 6 Jun 2022 17:36 UTC
−7 points
1 ∶ 2
Parent

I definitely would not derive it from the empty string in the closest world-without-Eliezer; I’m unsure how much I’d pay attention to AI alignment at all in that world. I’d very likely be working on agent foundations in that world, but possibly in the context of biology or AI capabilities rather than alignment. Arguments about AI foom and doom were obviously-to-me correct once I paid attention to them at all, but not something I’d have paid attention to on my own without someone pointing them out.

I don’t think he does this; that’d be ridiculous.

“I can’t find any good alignment researchers. The only way I know how to find them is by explaining that the field is important, using arguments for AI risk and doomerism, which means they didn’t come up with those arguments on their own, and thus cannot be ‘worthy’.”

• I don’t think he does this; that’d be ridiculous.

Doesn’t do what? I understand Eliezer to be saying that he figured out AI risk via thinking things through himself (e.g., writing a story that involved outcome pumps; reflecting on orthogonality and instrumental convergence; etc.), rather than being argued into it by someone else who was worried about AI risk. If Eliezer didn’t do that, there would still presumably be someone prior to him who did that, since conclusions and ideas have to enter the world somehow. So I’m not understanding what you’re modeling as ridiculous.

(I don’t know that foom falls into the same category; did Vinge or I.J. Good’s arguments help persuade EY here?)

“I can’t find any good alignment researchers. The only way I know how to find them is by explaining that the field is important, using arguments for AI risk and doomerism, which means they didn’t come up with those arguments on their own, and thus cannot be ‘worthy’.”

This is phrased in a way that’s meant to make the standard sound unfair or impossible. But it seems like a perfectly fine Bayesian update:

• There’s no logical necessity that we live in a world that lacks dozens of independent “Eliezers” who all come up with this stuff and write about it. I think Nick Bostrom had some AI risk worries independently of Eliezer, so gets at least partial credit on this dimension. Others who had thoughts along these lines independently include Norbert Wiener and I.J. Good (timeline with more examples).

• You could imagine a world that has much more independent discovery on this topic, or one where all the basic concepts of AI risk were being widely discussed and analyzed back in the 1960s. It’s a fair Bayesian update to note that we don’t live in worlds that are anything like that, even if it’s not a fair test of individual ability for people who, say, encountered all of Eliezer’s writing as soon as they even learned about the concept of AI.

• (I could also imagine a world where more of the independent discoveries result in serious research programs being launched, rather than just resulting in someone writing a science fiction story and then moving on with a shrug!)

• Your summary leaves out that “coming up with stuff without needing to be argued into it” is a matter of degree, and that there are many important claims here beyond just ‘AI risk is worth paying attention to at all’.

• It’s logically possible to live in a world where people need to have AI risk brought to their attention, but then they immediately “get it” when they hear the two-sentence version, rather than needing an essay-length or seven-essay-length explanation. To the extent we live in a world where many key players need the full essay, and many other smart, important people don’t even “get it” after hours of conversation (e.g., LeCun), that’s a negative update about humanity’s odds of success.

• Similarly, it’s logically possible to live in a world where people needed persuading to accept the core ‘AI risk’ thing, but then they have an easy time generating all the other important details and subclaims themselves. “Maximum doom” and “minimum doom” aren’t the only options; the exact level of doominess matters a lot.

• E.g., my Eliezer-model thinks that nearly all public discussion of ‘practical implications of logical decision theory’ outside of MIRI (e.g., discussion of humans trying to acausally trade with superintelligences) has been utterly awful. If instead this discourse had managed to get a ton of stuff right even though EY wasn’t releasing much of his own detailed thoughts about acausal trade, then that would have been an important positive update.

• Eliezer spent years alluding to his AI risk concerns on Overcoming Bias without writing them all up, and deliberately withheld many related arguments for years (including as recently as last year) in order to test whether anyone else would generate them independently. It isn’t the case that humanity had to passively wait to hear the full argument from Eliezer before it was permitted for them to start thinking and writing about this stuff.

• Doesn’t do what? I understand Eliezer to be saying that he figured out AI risk via thinking things through himself (e.g., writing a story that involved outcome pumps; reflecting on orthogonality and instrumental convergence; etc.), rather than being argued into it by someone else who was worried about AI risk. If Eliezer didn’t do that, there would still presumably be someone prior to him who did that, since conclusions and ideas have to enter the world somehow. So I’m not understanding what you’re modeling as ridiculous.

My understanding of the history is that Eliezer did not realize the importance of alignment at first, and that he only did so later after arguing about it online with people like Nick Bostrom. See e.g. this thread. I don’t know enough of the history here, but it also seems logically possible that Bostrom could have, say, only realized the importance of alignment after conversing with other people who also didn’t realize the importance of alignment. In that case, there might be a “bubble” of humans who together satisfy the null string criterion, but no single human who does.

The null string criterion does seem a bit silly nowadays since I think the people who would have satisfied it would have sooner read about AI risk on e.g. LessWrong. So they wouldn’t even have the chance to live to age ~21 to see if they spontaneously invent the ideas.

• Look, maybe you’re right. But I’m not good at complicated reasoning; I can’t confidently verify these results you’re giving me. My brain is using a much simpler heuristic that says: look at all of these other fields with core insights that could have been made way earlier than they did. Look at Newton! Look at Darwin! Certainly game theorists could have come along a lot sooner. But that doesn’t mean only the founder of these fields is the one Great enough to make progress, so, what are you saying, exactly?

• 7 Jun 2022 3:53 UTC
LW: 39 AF: 12
13 ∶ 0
AFParent

I have a couple object-level disagreements including relevance of evolution /​ nature of inner alignment problem and difficulty of attaining corrigibility. But leaving those aside, I wouldn’t have exactly written this kind of document myself, because I’m not quite sure what the purpose is. It seems to be trying to do a lot of different things for different audiences, where I think more narrowly-tailored documents would be better.

So, here are four useful things to do, and whether I’m personally doing them:

First, there is a mass of people who think AGI risk is trivial and stupid and p(doom) ≈ 0, and they can gleefully race to build AGI, or do other things that will speed the development of AGI (like improve PyTorch, or study the neocortex), and they can totally ignore the field of AGI safety, and when they have AGI algorithms they can mess around with them without a care in the world.

It would be very good to convince those people that AGI control is a serious and hard and currently-unsolved (and interesting!) problem, and that p(doom) will remain high (say, >>10%) unless and until we solve it.

I think this is a specific audience that warrants a narrowly-tailored document, e.g. avoiding jargon and addressing the basics very well.

That’s a big part of what I was going for in this post, for example. (And more generally, that whole series.)

Second, there are people who are thoughtful and well-informed about AGI risk in general, but not sold on the “pivotal act” idea. If they had an AGI, they would do things that pattern-match to “cautious scientists doing very careful experiments in a dangerous domain”, but they would not do things that pattern-match to “aggressively and urgently use their new tool to prevent the imminent end of the world, by any means necessary, even if it’s super-illegal and aggressive and somewhat dangerous and everyone will hate them”.

(I’m using “pivotal act” in a slightly broader sense that also includes “giving a human-level AGI autonomy to undergo recursive self-improvement and invent and deploy its own new technology”, since the latter has the same sort of dangerous properties and aggressive feel about it as a proper “pivotal act”.)

(Well, it’s possible that there are people sold on the “pivotal act” idea who wouldn’t say it publicly.)

Last week I did a little exercise of trying to guess p(doom), conditional on the two assumptions in this other comment. I got well over 99%, but I noted with interest that only a minority of my p(doom) was coming from “no one knows how to keep an AGI under control” (which I’m less pessimistic about than Eliezer, heck maybe I’m even as high as 20% that we can keep an AGI under control :-P , and I’m hoping that further research will increase that), whereas a majority of my p(doom) was coming from “there will be cautious responsible actors who will follow the rules and be modest and not do pivotal acts, and there will also be some reckless actors who will create out-of-control omnicidal AGIs”.

So it seems extremely important to figure out whether a “pivotal act” is in fact necessary for a good future. And if it is (a big “if”!), then it likewise seems extremely important to get relevant decisionmaking people on board with that.

I think it would be valuable to have a document narrowly tailored to this topic, finding the cruxes and arguments and counter-arguments etc. For example, I think this is a topic that looks very different in a Paul-Christriano-style future (gradual multipolar takeoff, near-misses, “corrigible AI assistants”, “strategy stealing assumption”, etc.) then in the world that I expect (decisive first-mover advantage).

But I don’t really feel qualified to write anything like that myself, at least not before talking to lots of people, and it also might be the kind of thing that’s better as a conversation than a blog post.

Third, there are people (e.g. leadership at OpenAI & DeepMind) making decisions that trade off between “AGI is invented soon” versus “AGI is invented by us people who are at least trying to avoid catastrophe and be altruistic”. Insofar as I think they’re making bad tradeoffs, I would like to convince them of that.

Again, it would be useful to have a document narrowly tailored to this topic. I’m not planning to write one, but perhaps I’m sorta addressing it indirectly when I share my idiosyncratic models of exactly what technical work I think needs to be done before we can align an AGI.

Fourth, there are people who have engaged with the AGI alignment /​ safety literature /​ discourse but are pursuing directions that will not solve the problem. It would be very valuable to spread common knowledge that those approaches are doomed. But if I were going to do that, it would (again) be a separate narrowly-tailored document, perhaps either organized by challenge that the approaches are not up to the task of solving, or organized by research program that I’m criticizing, naming names. I have dabbled in this kind of thing (example), but don’t have any immediate plan to do it more, let alone systematically. I think that would be extremely time-consuming.

• 8 Jun 2022 22:56 UTC
LW: 35 AF: 13
6 ∶ 1
AFParent

It’s very clear to me I could have written this if I had wanted to—and at the very least I’m sure Paul could have as well. As evidence: it took me ~1 hour to list off all the existing sources that cover every one of these points in my comment.

• Well, there’s obviously a lot of points missing! And from the amount this post was upvoted, it’s clear that people saw the half-assed current form as valuable.

Why don’t you start listing out all the missing further points, then? (Bonus points for any that don’t trace back to my own invention, though I realize a lot of people may not realize how much of this stuff traces back to my own invention.)

• 9 Jun 2022 0:38 UTC
LW: 4 AF: 4
3 ∶ 1
AFParent

I’m not sure what you mean by missing points? I only included your technical claims, not your sociological ones, if that’s what you mean.

• I think he means that there are more points that could be made. (If the points in the post are the training set, can you also produce the points in the held-out test set?)

• 6 Jun 2022 17:30 UTC
22 points
7 ∶ 2
Parent

I don’t think I personally could have written it; if others think they could have, I’d genuinely be interested to hear them brag, even if they can’t prove it.

Maybe I’m beyond hopeless: I don’t even understand the brag inherent in having written it. He keeps talking about coming up with this stuff “from the null string”, but… Isn’t 90% of this post published somewhere else? If someone else had written it wouldn’t he just accuse them of not being able to write it without reading {X}, or something from someone else who read {X}? At present this is mostly a test of recall.

Edit: Not to say I could’ve done even that, just that I expect someone else could have.

• The post honestly slightly decreases my confidence in EY’s social assessment capabilities. (I say slightly because of past criticism I’ve had along similar lines). [note here that being good/​bad at social assessment is not necessarily correlated to being good/​bad at other domains, so like, I don’t see that as taking away from his extremely valid criticism of common “simple solutions” to alignment (which I’ve definitely been guilty of myself). Please don’t read this as denigrating Eliezer’s general intellect or work as a whole.] As you said, the post doesn’t seem incredibly original, and even if it is and we’re both totally missing that aspect, the fact that we’re missing it implies it isn’t getting across the intended message as effectively as it could. Ultimately, I think if I was in Eliezer’s position, there are a very large number of alternative explanations I’d give higher probability to than assuming that there is nobody in the world as competent as I am.

• When you say you don’t think you could have written it, do you mean that you couldn’t have written it without all the things you’ve learned from talking to Yudkowsky, or that you couldn’t have written it even now? Most of this list was things I’ve seen Yudkowsky write before, so if it’s the latter that surprises me.

• Can I claim a very small but non-zero amount of bragging rights for having written this? It was at the time the ~only text about BCIs and alignment.

I don’t think I could have written the above text in a world where zero people worried about alignment. I also did not bother to write anything more about it because it looked to me that everything relevant was already written up on the Arbital alignment domain.

• I actually did try to generate a similar list through community discussion (https://​​www.lesswrong.com/​​posts/​​dSaScvukmCRqey8ug/​​convince-me-that-humanity-is-as-doomed-by-agi-as-yudkowsky), which while it didn’t end up going in the same exact direction as this document, did have some genuinely really good arguments on the topic, imo. I also don’t feel like many of the points you brought up here were really novel, in that I’ve heard most of this from multiple different sources already (though admittedly, not all in one place).

On a more general note, I don’t believe that people are as stupid compared to you as you seem to think they are. Different people’s modes of thinking are different than yours, obviously, but just because there isn’t an exact clone of you around doesn’t mean that we are significantly more doomed than in the counterfactual. I don’t want to diminish your contributions, but there are other people out there as smart or smarter than you, with security mindset, currently working in this problem area. You are not the only person on earth who can (more or less) think critically.

• Anecdotally: even if I could write this post, I never would have, because I would assume that Eliezer cares more about writing, has better writing skills, and has a much wider audience. In short, why would I write this when Eliezer could write it?

You might want to be a lot louder if you think it’s a mistake to leave you as the main “public advocate /​ person who writes stuff down” person for the cause.

• a mistake to leave you as the main “public advocate /​ person who writes stuff down” person for the cause.

It sort of sounds like you’re treating him as the sole “person who writes stuff down”, not just the “main” one. Noam Chomsky might have been the “main linguistics guy” in the late 20th century, but people didn’t expect him to write more than a trivial fraction of the field’s output, either in terms of high-level overviews or in-the-trenches research.

I think EY was pretty clear in the OP that this is not how things go on earths that survive. Even if there aren’t many who can write high-level alignment overviews today, more people should make the attempt and try to build skill.

• In the counterfactual world where Eliezer was totally happy continuing to write articles like this and being seen as the “voice of AI Safety”, would you still agree that it’s important to have a dozen other people also writing similar articles?

I’m genuinely lost on the value of having a dozen similar papers—I don’t know of a dozen different versions of fivethirtyeight.com or GiveWell, and it never occurred to me to think that the world is worse for only having one of those.

• We have to actually figure out how to build aligned AGI, and the details are crucial. If you’re modeling this as a random blog post aimed at persuading people to care about this cause area, a “voice of AI safety” type task, then sure, the details are less important and it’s not so clear that Yet Another Marginal Blog Post Arguing For “Care About AI Stuff” matters much.

But humanity also has to do the task of actually figuring out and implementing alignment. If not here, then where, and when? If here—if this is an important part of humanity’s process of actually figuring out the exact shape of the problem, clarifying our view of what sorts of solutions are workable, and solving it—then there is more of a case that this is a conversation of real consequence, and having better versions of this conversation sooner matters.

• 7 Jun 2022 8:07 UTC
10 points
3 ∶ 0
Parent

He wasn’t designated “main person who writes stuff down” by a cabal of AI safety elders. He’s not personally responsible for the fate of the world—he just happens to be the only person who consistently writes cogent things down. If you want you can go ahead and devote your life to AI safety, start doing the work he does as effectively and realistically as he does it, and then you’ll eventually be designated Movement Leader and have the opportunity to be whined at. He was pretty explicitly clear in the post that he does not want to be this and that he spent the last fifteen years trying to find someone else who can do what he does.

• I largely agree with you, but until this post I had never realized that this wasn’t a role Eliezer wanted. If I went into AI Risk work, I would have focused on other things—my natural inclination is to look at what work isn’t getting done, and to do that.

If this post wasn’t surprising to you, I’m curious where you had previously seen him communicate this?

If this post was surprising to you, then hopefully you can agree with me that it’s worth signal boosting that he wants to be replaced?

• 6 Jun 2022 1:59 UTC
LW: 45 AF: 20
7 ∶ 1
AF

I would summarize a dimension of the difficulty like this. There are the conditions that give rise to intellectual scenes, intellectual scenes being necessary for novel work in ambiguous domains. There are the conditions that give rise to the sort of orgs that output actions consistent with something like Six Dimensions of Operational Adequacy. The intersection of these two things is incredibly rare but not unheard of. The Manhattan Project was a Scene that had security mindset. This is why I am not that hopeful. Humans are not the ones building the AGI, egregores are, and spending egregore sums of money. It is very difficult for individuals to support a scene of such magnitude, even if they wanted to. Ultra high net worth individuals seem much poorer relative to the wealth of society than in the past, where scenes and universities (a scene generator) could be funded by individuals or families. I’d guess this is partially because the opportunity cost for smart people is much higher now, and you need to match that (cue title card: Baumol’s cost disease kills everyone). In practice I expect some will give objections along various seemingly practical lines, but my experience so far is that these objections are actually generated by an environment that isn’t willing to be seen spending gobs of money on low status researchers who mostly produce nothing. i.e. funding the 90%+ percent of a scene that isn’t obviously contributing to the emergence of a small cluster that actually does the thing.

• 7 Jun 2022 1:06 UTC
42 points
6 ∶ 0

Thank you, this was very helpful. As a bright-eyed youngster, it’s hard to make sense of the bitterness and pessimism I often see in the field. I’ve read the old debates, but I didn’t participate in them, and that probably makes them easier to dismiss. Object level arguments like these help me understand your point of view.

• 6 Jun 2022 6:01 UTC
LW: 38 AF: 13
46 ∶ 0
AF

Mod note: I activated two-axis voting on this post, since it seemed like it would make the conversation go better.

• I agree.

• 6 Jun 2022 6:31 UTC
7 points
13 ∶ 6
Parent

You should just activate it sitewide already :)

• New users are pretty confused by it when I’ve done some user-testing with it, so I think it needs some polish and better UI before we can launch it sitewide, but I am pretty excited about doing so after that.

• As a very new user, I’m not sure if it’s still helpful to add a data point if user testing’s already been done, but it seems at worst mostly harmless.

I saw the mod note before I started using the votes on this post. My first idea was to Google the feature, but that returned nothing relevant (while writing this post, I did find results immediately through site search). I was confused for a short while trying to place the axes & imagine where I’d vote in opposite directions. But after a little bit of practice looking at comments, it started making sense.

I’ve read a couple comments on this article that I agree with, where it seems very meaningful for me to downvote them (I interpret the downvote’s meaning when both axes are on as low quality, low importance, should be read less often).

I relatively easily find posts I want to upvote on karma. But for posts that I upvote, I’m typically much less confident about voting on agreement than for other posts (as a new user, it’s harder to assess the specific points made in high quality posts).
Posts where I’m not confident voting on agreement correlate with posts I’m not confident I can reply to without lowering the level of debate.

Unfortunately, the further the specific points that are made are from my comfort/​knowledge zone, the less I become able to tell nonsense from sophistication.
It seems bad if my karma vote density centers on somewhat-good posts at the exclusion of very good and very bad posts. This makes me err on the side of upvoting posts I don’t truly understand. I think that should be robust, since new user votes seem to carry less weight and I expect overrated nonsense to be corrected quickly, but it still seems suboptimal.

It’s also unclear to me whether agreement-voting factors in the sorting order. I predict it doesn’t, and I would want to change how I vote if it did.
Overall, I don’t have a good sense of how much value I get out of seeing both axes, but on this post I do like voting with both. It feels a little nicer, though I don’t have a strong preference.

• For what it’s worth, I haven’t used the site in years and I picked it up just from this thread and the UI tooltips. The most confusing thing was realizing “okay, there really are two different types of vote” since I’d never encountered that before, but I can’t think of much that would help (maybe mention it in the tooltip, or highlight them until the user has interacted with both?)

Looking forward to it as a site-wide feature—just from seeing it at work here, it seems like a really useful addition to the site

• 6 Jun 2022 20:46 UTC
LW: 37 AF: 16
10 ∶ 1
AF

Note: I think there’s a bunch of additional reasons for doom, surrounding “civilizational adequacy /​ organizational competence /​ societal dynamics”. Eliezer briefly alluded to these, but AFAICT he’s mostly focused on lethality that comes “early”, and then didn’t address them much. My model of Andrew Critch has a bunch of concerns about doom that show up later, because there’s a bunch of additional challenges you have to solve if AI doesn’t dramatically win/​lose early on (i.e. multi/​multi dynamics and how they spiral out of control)

I know a bunch of people whose hope funnels through “We’ll be able to carefully iterate on slightly-smarter-than-human-intelligences, build schemes to play them against each other, leverage them to make some progress on alignment that we can use to build slightly-more-advanced-safer-systems”. (Let’s call this the “Careful Bootstrap plan”)

I do actually feel nonzero optimism about that plan, but when I talk to people who are optimistic about that I feel a missing mood about the kind of difficulty that is involved here.

I’ll attempt to write up some concrete things here later, but wanted to note this for now.

• I agree with this line of thought regarding iterative developments of proto-AGI via careful bootstrapping. Humans will be inadequate for monitoring progress of skills. Hopefully, we’ll have a slew of diagnostic of narrow minded neural networks whose sole purpose is to tease out relevant details of the proto-super human intellect. What I can’t wrap my head around is whether super (or sub) human level intelligence requires consciousness. If consciousness is required, then is the world worse or better for it? Is an agent with the rich experience of fears, hopes, joys more or less likely to be built? Do reward functions reliably grow into feelings, which lead to emotional experiences? If they do, then perhaps an evolving intelligence wouldn’t always be as alien as we currently imagine it.

• 6 Jun 2022 3:55 UTC
LW: 36 AF: 7
21 ∶ 4
AF

If someone could find a way to rewrite this post, except in language comprehensible to policymakers, tech executives, or ML researchers, then it would probably achieve a lot.

• Yes, please do rewrite the post, or make your own version of a post like this!! :) I don’t suggest trying to persuade arbitrary policymakers of AGI risk, but I’d be very keen on posts like this optimized to be clear and informative to different audiences. Especially groups like ‘lucid ML researchers who might go into alignment research’, ‘lucid mathematicians, physicists, etc. who might go into alignment research’, etc.

• Suggestion: make it a CYOA-style interactive piece, where the reader is tasked with aligning AI, and could choose from a variety of approaches which branch out into sub-approaches and so on. All of the paths, of course, bottom out in everyone dying, with detailed explanations of why. This project might then evolve based on feedback, adding new branches that counter counter-arguments made by people who played it and weren’t convinced. Might also make several “modes”, targeted at ML specialists, general public, etc., where the text makes different tradeoffs regarding technicality vs. vividness.

I’d do it myself (I’d had the idea of doing it before this post came out, and my preliminary notes covered much of the same ground, I feel the need to smugly say), but I’m not at all convinced that this is going to be particularly useful. Attempts to defeat the opposition by building up a massive evolving database of counter-arguments have been made in other fields, and so far as I know, they never convinced anybody.

The interactive factor would be novel (as far as I know), but I’m still skeptical.

(A… different implementation might be to use a fine-tuned language model for this; make it an AI Dungeon kind of setup, where it provides specialized counter-arguments for any suggestion. But I expect it to be less effective than a more coarse hand-written CYOA, since the readers/​players would know that the thing they’re talking to has no idea what it’s talking about, so would disregard its words.)

• Arbital was meant to support galaxy-brained attempts like this; Arbital failed.

• Failed as a platform for hosting galaxy-brained attempts, or failed as in every similar galaxy-brained attempt on it failed? I haven’t spent a lot of time there, but my impression is that Arbital is mostly a wiki-style collection of linked articles, not a dumping ground of standalone esoterically-structured argumentative pieces. And while a wiki is conceptually similar, presentation matters a lot. A focused easily-traversable tree of short-form arguments in a wrapper that encourages putting yourself in the shoes of someone trying to fix the problem may prove more compelling.

(Not to make it sound like I’m particularly attached to the idea after all. But there’s a difference between “brilliant idea that probably won’t work” and “brilliant idea that empirically failed”.)

• Arbital was a very conjunctive project, trying to do many different things, with a specific team, at a specific place and time. I wouldn’t write off all Arbital-like projects based on that one data point, though I update a lot more if there are lots of other Arbital-ish things that also failed.

• All of the paths, of course, bottom out in everyone dying, with detailed explanations of why.

A strange game. The only winning move is not to play. ;)

• I guess we should also kidnap people and force them to play it, and if they don’t succeed we kill them? For realism? Wait, there’s something wrong with this plan.

More seriously, yeah, if you’re implementing it more like a game and less like an interactive article, it’d need to contain some promise of winning. Haven’t considered how to do it without compromising the core message.

• What if “winning” consists of finding a new path not already explored-and-foreclosed? For example, each time you are faced with a list of choices of what to do, there’s a final choice “I have an idea not listed here” where you get to submit a plan of action. This goes into a moderation engine where a chain of people get to shoot down the idea or approve it to pass up the chain. If the idea gets convincingly shot down (but still deemed interesting), it gets added to the story as a new branch. If it gets to the top of the moderation chain and makes EY go “Hm, that might work” then you win the game.

• Mmm. If the CYOA idea is implemented as a quirky-but-primarily-educational article, then sure, integrating the “adapt to feedback” capability like this would be worthwhile. Might also attach a monetary prize to submitting valuable ideas, by analogy to the ELK contest.

For a game-like implementation, where you’d be playing it partly for the fun/​challenge of it, that wouldn’t suffice. The feedback loop’s too slow, and there’d be an ugh-field around the expectation that submitting a proposal would then require arguing with the moderators about it, defending it. It wouldn’t feel like a game.

It’d make the upkeep cost pretty high, too, without a corresponding increase in the pay-off.

Just making it open-ended might work, even without the moderation engine? Track how many branches the player explored, once they’ve explored a lot (i. e., are expected to “get” the full scope of the problem), there appears an option for something like “I really don’t know what to do, but we should keep trying”, leading to some appropriately-subtle and well-integrated call to support alignment research?

Not excited about this approach either.

• I wonder if we could be much more effective in outreach to these groups?

Like making sure that Robert Miles is sufficiently funded to have a professional team +20% (if that is not already the case). Maybe reaching out to Sabine Hossenfelder and sponsoring a video, or maybe collaborate with her for a video about this. Though I guess given her attitude towards the physics community, the work with her might be a gamble and two-edged sword. Can we get market research on what influencers have a high number of followers of ML researches/​physicists/​mathematicians and then work with them /​ sponsor them?

Or maybe micro-target this demographic with facebook/​google/​github/​stackexchange ads and point them to something?

I don’t know, I’m not a marketing person, but I feel like I would have seen much more of these things if we were doing enough of them.

Not saying that this should be MIRI’s job, rather stating that I’m confused because I feel like we as a community are not taking an action that would seem obvious to me. Especially given how recent advances in published AI capabilities seem to make the problem even much legible. Is the reason for not doing it really just that we’re all a bunch of nerds who are bad at this kind of thing, or is there more to it that I’m missing?

While I see that there is a lot of risk associated with such outreach increasing the amount of noise, I wonder if that tradeoff might be shifting the shorter the timelines are getting and given that we don’t seem to have better plans than “having a diverse set of smart people come up with novel ideas of their own in the hope that one of those works out”. So taking steps to entice a somewhat more diverse group of people into the conversation might be worth it?

• 7 Jun 2022 16:36 UTC
LW: 18 AF: 9
4 ∶ 0
AFParent

Not saying that this should be MIRI’s job, rather stating that I’m confused because I feel like we as a community are not taking an action that would seem obvious to me.

I wrote about this a bit before, but in the current world my impression is that actually we’re pretty capacity-limited, and so the threshold is not “would be good to do” but “is better than my current top undone item”. If you see something that seems good to do that doesn’t have much in the way of unilateralist risk, you doing it is probably the right call. [How else is the field going to get more capacity?]

• 🤔

Not sure if I’m the right person, but it seems worth thinking about how one would maybe approach this if one were to do it.

So the idea is to have an AI-Alignment PR/​Social Media org/​group/​NGO/​think tank/​company that has the goal to contribute to a world with a more diverse set of high-quality ideas about how to safely align powerful AI. The only other organization roughly in this space that I can think of would be 80,000 hours, which is also somewhat more general in its goals and more conservative in its strategies.

I’m not a sales/​marketing person, but as I understand it, the usual metaphor to use here is a funnel?

• Starting with maybe ads /​ sponsoring trying to reach the right people[0] (e.g. I saw Jane Street sponsor Matt Parker)

• then more and more narrowing down first with introducing people to why this is an issue (orthogonality, instrumental convergence)

• hopefully having them realize for themselves, guided by arguments, that this is an issue that genuinely needs solving and maybe their skills would be useful

• increasing the math as needed

• finally, somehow selecting for self-reliance and providing a path for how to get started with thinking about this problem by themselves /​ model building /​ independent research

• or otherwise improving the overall situation (convince your congress member of something? run for congress? …)

Probably that would include copy writing (or hiring copywriters or contracting them) to go over a number of our documents to make them more digestible and actionable.

So, I’m probably not the right person to get this off the ground, because I don’t have a clue about any of this (not even entrepreneurship in general), but it does seem like a thing worth doing and maybe like an initiative that would get funding from whoever funds such things these days?

[0] Though, maybe we should also look into a better understanding about who “the right people” are? Given that our current bunch of ML researchers/​physicists/​mathematicians were not able to solve it, maybe it would be time to consider broadening our net in a somehow responsible way.

• On second thought: Don’t we have orgs that work on AI governance/​policy? I would expect them to have more likely the skills/​expertise to pull this off, right?

• So, here’s a thing that I don’t think exists yet (or, at least, it doesn’t exist enough that I know about it to link it to you). Who’s out there, what ‘areas of responsibility’ do they think they have, what ‘areas of responsibility’ do they not want to have, what are the holes in the overall space? It probably is the case that there are lots of orgs that work on AI governance/​policy, and each of them probably is trying to consider a narrow corner of space, instead of trying to hold ‘all of it’.

So if someone says “I have an idea how we should regulate medical AI stuff—oh, CSET already exists, I should leave it to them”, CSET’s response will probably be “what? We focus solely on national security implications of AI stuff, medical regulation is not on our radar, let alone a place we don’t want competition.”

I should maybe note here there’s a common thing I see in EA spaces that only sometimes make sense, and so I want to point at it so that people can deliberately decide whether or not to do it. In selfish, profit-driven worlds, competition is the obvious thing to do; when someone else has discovered that you can make profits by selling lemonade, you should maybe also try to sell lemonade to get some of those profits, instead of saying “ah, they have lemonade handled.” In altruistic, overall-success-driven worlds, competition is the obvious thing to avoid; there are so many undone tasks that you should try to find a task that no one is working on, and then work on that.

One downside is this means the eventual allocation of institutions /​ people to roles is hugely driven by inertia and ‘who showed up when that was the top item in the queue’ instead of ‘who is the best fit now’. [This can be sensible if everyone ‘came in as a generalist’ and had to skill up from scratch, but still seems sort of questionable; even if people are generalists when it comes to skills, they’re probably not generalists when it comes to personality.]

Another downside is that probably it makes more sense to have a second firm attempting to solve the biggest problem before you get a first firm attempting to solve the twelfth biggest problem. Having a sense of the various values of the different approaches—and how much they depend on each other, or on things that don’t exist yet—might be useful.

• Not sure if I’m the right person

...yet!

• Since Divia said, and Eliezer retweeted, that good things might happen if people give their honest, detailed reactions:

My honest, non-detailed reaction is AAAAAAH. In more detail -

1. Yup, this seems right.

2. This is technobabble to me, since I don’t actually understand nanomachines, but it makes me rather more optimistic about my death being painless than my most likely theory, which is that a superhuman AI takes over first and has better uses for our atoms later.

3. (If we had unlimited retries—if every time an AGI destroyed all the galaxies we got to go back in time four years and try again—we would in a hundred years figure out which bright ideas actually worked.) My brain immediately starts looking for ways to set up some kind of fast testing for ways to do this in a closed, limited world without letting it know ours exists… which is already answered below, under 10. Yup, doomed.

4. And then we all died.

5. Yup.

6. I imagine it would be theoretically—but not practically—possible to fire off a spaceship accelerating fast enough (that is, with enough lead time) that it could outrun the AI and so escape an Earth about to be eaten by an AI (a pivotal act well short of melting all CPUs that would save at least a part of humanity), but that given that the AI could probably take over the ship just by flashing lights at it, that seems unlikely to actually work in practice.

7. I think the closest thing I get to a “pivotal weak act” would be persuading everyone to halt all AI research with a GPT-5 that can be superhumanly persuasive at writing arguments to persuade humans, but doesn’t yet have a model of the world-as-real-and-affecting-it that it could use to realize that it could achieve its goals by taking over the world, but I don’t actually expect this would work—that would be a very narrow belt of competence and I’m skeptical it could be achieved.

8. Not qualified to comment.

9. Seems right.

10. Yeah, we’re doomed.

11. Doomed.

12. Seems right to me. If the AI never tries a plan because it correctly knows it won’t work, this doesn’t tell you anything about the AI not trying a plan when it would work.

13. “It’s not that we can’t roll one twenty, it’s that we’ll roll a one eventually.” I don’t think humanity has successfully overcome this genre of problem, and we encounter it a lot. (In practice, our solutions are fail-safe systems, requiring multiple humans to concur to do anything, and removing these problems from people’s environments, none of which really work in context.)

14. Doomed.

15. Yup, doomed.

16. I’d also add a lot of “we have lots of experience with bosses trying to make their underlings serve them instead of serving themselves and none of them really work”, as more very weak evidence in the same direction.

17. Doomed.

18. Doomed.

19. Not qualified to discuss this.

20. We are really very doomed, aren’t we.

21. This seems very logical and probably correct, both about the high-level points Eliezer makes and the history of human alignment with other humans.

22. Seems valid.

23. Not qualified to comment.

24. You know, I’d take something that was imperfectly aligned with my Real Actual Values as long as it gave me enough Space Heroin, if the alternative was death. I’d rather the thing aligned with my Real Actual Values, but if we can’t manage that, Space Heroin seems better than nothing. (Also, yup, doomed.)

25. Not qualified to comment.

26. This seems valid but I don’t know enough about current AI to comment.

27. Good point!

28. Yup.

29. Yup.

30. Yup.

31. Yup.

32. Good point.

33. Doomed.

34. We do seem doomed, yup.

35. Doomed.

36. Indeed, humans already work this way!

37. This is a good point about social dynamics but does not immediately make me go ‘we’re all doomed’, I think because social dynamics seem potentially contingent.

38. You’re the expert and I’m not; I don’t know the field well enough to comment.

39. No comment.

40. No comment; this seems plausible but I don’t know enough to say.

41. No comment.

42. No comment.

43. No comment.

• Here is my honest reaction as another data point. (Well done by the parent for taking the initiative!)

Context: Got introduced to this field around a year ago. Not an expert.

My honest reaction is rather worried as well (to put it mildly).

1. I agree with this. My impression is that in many tasks we currently require a lot more data than humans, but I do not see any reason to expect that it will always be so.

2. I broadly agree with this. I am sympathetic to people who would like to see more of concrete stories about how exactly an AGI would take over the world (while there are some already, more wouldn’t hurt). Meanwhile,

- I believe that if effort is put into inventing such takeover scenarios, then one expects to come up with quite many of them. Hence, update already.

- I haven’t looked into nanobots myself, so no inside view there, but my prior is definitely on “there are lots of (causally) powerful technologies we haven’t invented yet”.

- The AI box experiment really feels like strong empirical evidence for the bootstrapping argument

3. I agree with this as stated. I do wonder, though, whether we will get any warning shots, where we operate at a semi-dangerous level and fail. This seems to reduce to slow vs. fast takeoff. (I don’t have a consistent opinion on that.)

4. Agree that there is a time limit. And indeed, recognition of the issue and cooperation from the relevant actors seems non-ideal.

5. Agree.

6. I’m not sure here—I agree that we should avoid the situation where we have multiple AGIs. If “pivotal act” is defined as an act which results in this outcome, then there is agreement, but as someone pointed out, it might be that the pivotal act is something which doesn’t fit the mental picture one associates with the words “pivotal act”.

7. I notice I am confused here: I’m not sure what “pivotal weak act” means, or what “something weak enough with an AGI to be *passively safe*” means. I agree with “no one knows of any pivotal act you could do with just current SOTA AI”. I don’t have good intuitions about the space of pivotal actions—I haven’t thought about it.

8. I interpret “problems we want an AI to solve” means problems relevant for pivotal acts. In this case, see above—I don’t have intuitions about pivotal acts.

9. See above.

10. Broadly agree.

11. Again, don’t know much about pivotal acts. (It is mentioned that “Pivotal weak acts like this aren’t known, and not for want of people looking for them.”—have I missed some big projects on pivotal acts.)

12. Agree.

13. Agree.

14. Agree. The discontinuity /​ “treacherous turn” seems obvious to me when thought about from first principles. The skeptic voice in my head says that nothing like that has happened in practice (to my knowledge), but that really does not assure me.

15. Broadly agree, though I lack good examples for the concept “alignment-required invariants”. My best guess: there is interpretability research on neural networks, and we have some non-trivial understanding there. That might turn out to be not relevant in case of a great new idea for capabilities.

16. I agree that the concept of inner alignment is important. There is an empirical verification for it. I am unsure about how big of a problem this will be in practice. I do appreciate the point about evolution.

17. I like this formulation (quite crisp), I don’t think I’ve seen it anywhere before. To me, it seems like an interesting idea to try to come up with ways for getting inner properties to systems.

18. Agree.

19. Agree.

20. Agree, except I don’t understand what “If you perfectly learn and perfectly maximize the referent of rewards assigned by human operators, that kills them.” means.

21. Not sure I get the central result, but I get the idea that in capabilities you have feedback loops in a different way from utility functions.

22. Agree.

23. A good, crisp formulation. Agree.

24. A good distinction, sure. In other words “let the AGI optimize something, no strings attached (and choose that “something” very carefully)” vs. “try to control/​restrict the AGI”. I’m wondering whether there are any alternatives.

25. “We’ve got no idea” seems to me like a bit of an exaggeration, but I agree with the latter sentence.

26. Yep.

27. Yep, an instance of Goodhart’s law.

28. Yep.

29. I agree that this is the generic case—if you take a complex action sequence of AGI by random, it is almost surely uninterpretable by humans. Not sure what would happen if you optimized for plans in which humans are confident they understand the consequences. Sure, we have to fight against Goodhart’s law, and I do think that against sufficiently powerful cognitive systems our chances would be slim, but I’m not sure that one couldn’t extract enough information to perform a pivotal act. Failure at AI boxing does seem like a major bottleneck, though.

30. I agree up to “it knows … that some action sequence results in the world we want”. I also agree that if we knew how an AI would behave in advance, it would be less intelligent than a human. I feel like there is a gap to moving that there is __no__ pivotal output of an AGI. If I am stuck in a maze and build an AGI to help me find the way out, I cannot anticipate what exact path it will give me, but I can check whether the path leads out or not. So I think the general claim “there is no pivotal output … that is humanly checkable” is not properly justified here. I do feel like this would be the generic case, though, namely that the AGI could convince us of a plan and sneak in unintended consequences.

31. Agree. Seems conceptually related to 17: 17 is about affecting the inner properties of the system, 31 is about inspecting the inner properties.

32. Interesting point I haven’t seen elsewhere, namely “Words are not an AGI-complete data representation in its native style”. Not sure if it makes sense to give “true/​false” status to the claim, but it pushes me a non-zero amount to the direction “alignment is hard”.

33. Agree. This is a statement which I could see many educated people nodding at, but which at least I find quite hard to feel on a gut level. (The Sequences contain helpful material on this, and apparently reading the right science fiction books would also help.)

34. Agree.

35. Agree. I guess there is also the scenario where one AGI has a decisive advantage over the other, but the outcome is the same: you cannot keep the AGIs in line by pitting them against each other.

36. Agree with the bolded part, the AI-box experiment is more than enough evidence for this.

37. Agree with “in the case of AGI safety, it is really important to have conservation of expected evidence about the difficulty of alignment”.

38. It does seem to me that “AGI safety” is a quite small subfield of “AI safety”, or you can see these as separate fields. I agree that the incentives are not in our/​humanity’s favor.

39. I like this paragraph. I could nitpick about how the point of community building is that not everyone has to figure things out from the null string, but on the other hand I understand the view expressed here.

40. I have no clear view about how different the skills required for alignment are in contrast to more usual cognitively demanding work (other than that it is, well, hard). (I realize that I am biased—I found myself agreeing with “AGI risk is real” without much friction, but there are definitely many people who do not come to this conclusion.)

41. No comment.

42. I associate “There’s no plan” to the field being in a preparadigmatic state. I agree that it would be very much preferable if this weren’t the state of affairs, so that we could be in a position to design a plan.

43. This part hit home: “not an uncomfortable shrug and ‘How can you be sure that will happen’ /​ ‘There’s no way you could be sure of that now, we’ll have to wait on experimental evidence.’” I am sad that the Standard Response to AGI risk is “AI won’t be intelligent enough to do that”. (Not to say that there aren’t stronger counterarguments).

• As a bystander who can understand this, and find the arguments and conclusions sound, I must say I feel very hopeless and “kinda” scared at this point. I’m living in at least an environment, if not a world, where even explaining something comparatively simple like how life extension is a net good is a struggle. Explaining or discussing this is definitely impossible—I’ve tried with the cleverer, more transhumanistic/​rationalistic minded people I know, and it just doesn’t click for them, to the contrary, I find people like to push in the other direction, as if it were a game.

And at the same time, I realize it is unlikely I can contribute anything remotely significant to a solution myself. So I can only spectate. This is literally maddening, especially so when most everyone seems to underreact.

• If it’s any consolation, you would not feel more powerful or less scared if you were myself.

• Well, obviously, it won’t be consolation enough, but I can certainly revel in some human warmth inside by knowing I’m not alone in feeling like this.

• This might sound absurd, but I legit think that there’s something that most people can do. Being something like radically publicly honest and radically forgiving and radically threat-aware, in your personal life, could contribute to causing society in general to be radically honest and forgiving and threat-aware, which might allow people poised to press the Start button on AGI to back off.

ETA: In general, try to behave in a way such that if everyone behaved that way, the barriers to AGI researchers noticing that they’re heading towards ending the world would be lowered /​ removed. You’ll probably run up against some kind of resistance; that might be a sign that some social pattern is pushing us into cultural regimes where AGI researchers are pushed to do world-ending stuff.

• Vincent Fagot: Where do you live (in general terms if you can provide it, feel free not to dox yourself if you don’t want to)? I live in countryside Brazil, so I can strongly relate.

• What concerns me the most is the lack of any coherent effort anywhere, towards solving the biggest problem: identifying a goal (value system, utility function, decision theory, decision architecture...) suitable for an autonomous superhuman AI.

In these discussions, Coherent Extrapolated Volition (CEV) is the usual concrete formulation of what such a goal might be. But I’ve now learned that MIRI’s central strategy is not to finish figuring out the theory and practice of CEV—that’s considered too hard (see item 24 in this post). Instead, the hope is to use safe AGI to freeze all unsafe AGI development everywhere, for long enough that humanity can properly figure out what to do. Presumably this freeze (the “pivotal act”) would be carried out by whichever government or corporation or university crossed the AGI threshold first; ideally there might even become a consensus among many of the contenders that this is the right thing to do.

I think it’s very appropriate that some thought along these lines be carried out. If AGI is a threat to the human race, and it arrives before we know how to safely set it free, then we will need ways to try to neutralize that dangerous potential. But I also think it’s vital that we try to solve that biggest problem, e.g. by figuring out how to concretely implement CEV. And if one is concerned that this is just too much for human intellect to figure out, remember that AI capabilities are rising. If humans can’t figure out CEV unaided, maybe they can do it with the help of AI. To me, that’s the critical pathway that we should be analyzing.

P.S. I have many more thoughts on what this might involve, but I don’t know when I will be able to sort through them all. So for now I will just list a few people whose work is on my shortlist of definitely or potentially relevant (certainly not a complete list): June Ku, Vanessa Kosoy, Jessica Taylor, Steven Byrnes, Stuart Armstrong.

• There’s shard theory, which aims to describe the process by which values form in humans. The eventual aim is to understand value formation well enough that we can do it in an AI system. I also think figuring out human values, value reflection and moral philosophy might actually be a lot easier than we assume. E.g., the continuous perspective on agency /​ values is pretty compelling to me and changes things a lot, IMO.

• Here’s an outside-the-box suggestion:

Clearly the development of any AGI is an enormous risk. While I can’t back this up with any concrete argument, a couple decades of working with math and CS problems gives me a gut intuition that statements like “I figure there’s a 50-50 chance it’ll kill us”, or even a “5-15% everything works out” are wildly off. I suspect this is the sort of issue where the probability of survival is funneled to something more like either or , of which the latter currently seems far more likely.

Has anyone discussed the concept of deliberately trying to precipitate a global nuclear war? I’m half kidding, but half not; if the risk is really so great and so imminent and potentially final as many on here suspect, then a near-extinction-event like that (presumably wiping out the infrastructure for GPU farms for a long time to come) which wouldn’t actually wipe out the race but could buy time to work the problem (or at least pass the buck to our descendants) could conceivably be preferable.

Obviously, it’s too abhorrent to be a real solution, but it does have the distinct advantage that it’s something that could be done today if the right people wanted to do it, which is especially important given that I’m not at all convinced that we’ll recognize a powerful AGI when we see it, based on how cavalierly everyone is dismissing large language models as nothing more than a sophisticated parlor trick, for instance.

• 11 Jun 2022 16:23 UTC
LW: 28 AF: 10
5 ∶ 0
AF

Could I put in a request to see a brain dump from Eliezer of ways to gain dignity points?

• I’m not Eliezer, but my high-level attempt at this:

[...] The things I’d mainly recommend are interventions that:

• Help ourselves think more clearly. (I imagine this including a lot of trying-to-become-more-rational, developing and following relatively open/​honest communication norms, and trying to build better mental models of crucial parts of the world.)

• Help relevant parts of humanity (e.g., the field of ML, or academic STEM) think more clearly and understand the situation.

• Help us understand and resolve major disagreements. (Especially current disagreements, but also future disagreements, if we can e.g. improve our ability to double-crux in some fashion.)

• Try to solve the alignment problem, especially via novel approaches.

• In particular: the biggest obstacle to alignment seems to be ‘current ML approaches are super black-box-y and produce models that are very hard to understand/​interpret’; finding ways to better understand models produced by current techniques, or finding alternative techniques that yield more interpretable models, seems like where most of the action is.

• Think about the space of relatively-plausible “miracles” [i.e., positive model violations], think about future evidence that could make us quickly update toward a miracle-claim being true, and think about how we should act to take advantage of that miracle in that case.

• Build teams and skills that are well-positioned to take advantage of miracles when and if they arise. E.g., build some group like Redwood into an org that’s world-class in its ability to run ML experiments, so we have that capacity already available if we find a way to make major alignment progress in the future.

This can also include indirect approaches, like ‘rather than try to solve the alignment problem myself, I’ll try to recruit physicists to work on it, because they might bring new and different perspectives to bear’.

Though I definitely think there’s a lot to be said for more people trying to solve the alignment problem themselves, even if they’re initially pessimistic they’ll succeed!

I think alignment is still the big blocker on good futures, and still the place where we’re most likely to see crucial positive surprises, if we see them anywhere—possibly Eliezer would disagree here.

• 13 Jun 2022 20:20 UTC
LW: 27 AF: 14
4 ∶ 0
AF

Eliezer, thanks for sharing these ideas so that more people can be on the lookout for failures. Personally, I think something like 15% of AGI dev teams (weighted by success probability) would destroy the world more-or-less immediately, and I think it’s not crazy to think the fraction is more like 90% or higher (which I judge to be your view).

FWIW, I do not agree with the following stance, because I think it exposes the world to more x-risk:

Specifically, I think a considerable fraction of the remaining AI x-risk facing humanity stems from people pulling desperate (unsafe) moves with AGI to head off other AGI projects. So, in that regard, I think that particular comment of yours is probably increasing x-risk a bit. If I were a 90%-er like you, it’s possible I’d endorse it, but even then it might make things worse by encouraging more desperate unilateral actions.

That said, overall I think this post is a big help, because it helps to put responsibility in the hands of more people to not do the crazy/​stupid/​reckless things you’re describing here… and while I might disagree on the fraction/​probability, I agree that some groups would destroy humanity more or less immediately if they developed AGI. And, while I might disagree on some of the details of how human extinction eventually plays out, I do think human extinction remains the default outcome of humanity’s path toward replacing itself with automation, probably within our lifetimes unfortunately.

• a considerable fraction of the remaining AI x-risk facing humanity stems from people pulling desperate (unsafe) moves with AGI to head off other AGI projects

In your post “Pivotal Act” Intentions, you wrote that you disagree with contributing to race dynamics by planning to invasively shut down AGI projects because AGI projects would, in reaction, try to maintain

the ability to implement their own pet theories on how safety/​alignment should work, leading to more desperation, more risk-taking, and less safety overall.

Could you give some kind of very rough estimates here? How much more risk-taking do you expect in a world given how much /​ how many prominent “AI safety”-affiliated people declaring invasive pivotal act intentions? How much risk-taking do you expect in the alternative, where there are other pressures (economic, military, social, whatever), but not pressure from pivotal act threats? How much safety (probability of AGI not killing everyone) do you think this buys? You write:

15% of AGI dev teams (weighted by success probability) would destroy the world more-or-less immediately

What about non-immediately, in each alternative?

• Specifically, I think a considerable fraction of the remaining AI x-risk facing humanity stems from people pulling desperate (unsafe) moves with AGI to head off other AGI projects. So, in that regard, I think that particular comment of yours is probably increasing x-risk a bit.

I don’t think the inferential distance to pivotal act thinking is that high even if you manage to censor it out at a community level.

If you do censor, what you will get is a lot of people doing pivotal act thinking but doing it badly, because they can’t build off of each other’s work. Whether this is net good or net bad I’m uncertain.

Hopefully the AI lab actually executing pivotal acts does better thinking on it than the average LWer. That still doesn’t mean they’ll do it enough or that they wouldn’t benefit from a public body of work on what good pivotal acts look like.

• 6 Jun 2022 2:03 UTC
LW: 26 AF: 8
12 ∶ 1
AF

If there was one thing that I could change in this essay, it would be to clearly outline that the existence of nanotechnology advanced enough to do things like melt GPUs isn’t necessary even if it is sufficient for achieving singleton status and taking humanity off the field as a meaningful player.

Whenever I see people fixate on critiquing that particular point, I need to step in and point out that merely existing tools and weapons (is there a distinction?) suffice for a Superintelligence to be able to kill the vast majority of humans and reduce our threat to it to negligible levels. Be that wresting control of nuclear arsenals to initiate MAD or simply extrapolating on gain-of-function research to produce extremely virulent yet lethal pathogens that can’t be defeated before the majority of humans are infected, such options leave a small minority of humans alive to cower in the wreckage until the biosphere is later dismantled.

That’s orthogonal to the issue of whether such nanotechnology is achievable for a Superintelligent AGI, it merely reduces the inferential distance the message has to be conveyed as it doesn’t demand familiarity with Drexler.

(Advanced biotechnology already is nanotechnology, but the point is that no stunning capabilities need to be unlocked for an unboxed AI to become immediately lethal)

• 7 Jun 2022 4:38 UTC
LW: 4 AF: 1
5 ∶ 3
AFParent

Right, alignment advocates really underestimate the degree to which talking about sci-fi sounding tech is a sticking point for people

• 7 Jun 2022 5:05 UTC
LW: 51 AF: 20
14 ∶ 1
AFParent

The counter-concern is that if humanity can’t talk about things that sound like sci-fi, then we just die. We’re inventing AGI, whose big core characteristic is ‘a technology that enables future technologies’. We need to somehow become able to start actually talking about AGI.

One strategy would be ‘open with the normal-sounding stuff, then introduce increasingly weird stuff only when people are super bought into the normal stuff’. Some problems with this:

• A large chunk of current discussion and research happens in public; if it had to happen in private because it isn’t optimized for looking normal, a lot of it wouldn’t happen at all.

• More generally: AGI discourse isn’t an obstacle course or a curriculum, such that we can control the order of ideas and strictly segregate the newbies from the old guard. Blog posts, research papers, social media exchanges, etc. freely circulate among people of all varieties.

• It’s a dishonest/​manipulative sort of strategy — which makes it ethically questionable, is liable to fuel other trust-degrading behavior in the community, and is liable to drive away people with higher discourse standards.

• A lot of the core arguments and hazards have no ‘normal-sounding’ equivalent. To sound normal, you have to skip those considerations altogether, or swap them out for much weaker arguments.

• In exchange for attracting more people who are allergic to anything that sounds ‘sci-fi’, you lose people who are happy to speak to the substance of ideas even when they sound weird; and you lose sharp people who can tell that your arguments are relatively weak and PR-spun, but would have joined the conversation if the arguments and reasoning on display had been crisper and more obviously candid.

Another strategy would be ‘keep the field normal now, then turn weird later’. But how do you make a growing research field pivot? What’s the trigger? Why should we expect this to work, as opposed to just permanently diluting the field with false beliefs, dishonest norms, and low-relevance work?

My perception is that a large amount of work to date has gone into trying to soften and spin ideas so that they sound less weird or “sci-fi”; whereas relatively little work has gone into candidly stating beliefs, acknowledging that this stuff is weird, and clearly stating why you think it’s true anyway.

I don’t expect the latter strategy to work in all cases, but I do think it would be an overall better strategy, both in terms of ‘recruiting more of the people likeliest to solve the alignment problem’, and in terms of having fewer toxic effects on norms and trust within the field. Just being able to believe what people say is a very valuable thing in a position like ours.

• Fair point, and one worth making in the course of talking about sci-fi sounding things! I’m not asking anyone to represent their beliefs dishonestly, but rather introduce them gently. I’m personally not an expert, but I’m not convinced of the viability of nanotech, so if it’s not necessary (rather it’s sufficient) to the argument, it seems prudent to stick to more clearly plausible pathways to takeover as demonstrations of sufficiency, while still maintaining that weirder sounding stuff is something one ought to expect when dealing with something much smarter than you.

• If you’re trying to persuade smart programmers who are somewhat wary of sci-fi stuff, and you think nanotech is likely to play a major role in AGI strategy, but you think it isn’t strictly necessary for the current argument you’re making, then my default advice would be:

• Be friendly and patient; get curious about the other person’s perspective, and ask questions to try to understand where they’re coming from; and put effort into showing your work and providing indicators that you’re a reasonable sort of person.

• Wear your weird beliefs on your sleeve; be open about them, and if you want to acknowledge that they sound weird, feel free to do so. At least mention nanotech, even if you choose not to focus on it because it’s not strictly necessary for the argument at hand, it comes with a larger inferential gap, etc.

• I think that even this scenario is implausible. I have the impression we are overestimating how easy is to wipe all humans quickly

• I’m retreating from my previous argument a bit. The AGI doesn’t need to cause literal human extinction with a virus; if it can cause enough damage to collapse human industrial civilization (while being able to survive said collapse) then that would also achieve most of the AGI’s goal of being able to do what it wants without humans stopping it. Naturally occurring pathogens from Europe devastated Native American populations after Columbus; throw a bunch of bad enough novel viruses at us at once and you probably could knock humanity back to the metaphorical Stone Age.

• I find that more plausible. Also horrifying and worth fighting against, but not what EY is saying

• I find that more plausible. Also horrifying and worth fighting against, but not what EY is saying

Note that EY is saying “there exists a real plan that is at least as dangerous as this one”; if you think there is such a plan, then you can agree with the conclusion, even if you don’t agree with his example. [There is an epistemic risk here, if everyone mistakenly believes that a different doomsday plan is possible when someone else knows why that specific plan won’t work, and so if everyone pooled all their knowledge they could know that none of the plans will work. But I’m moderately confident we’re instead in a world with enough vulnerabilities that broadcasting them makes things worse instead of better.]

• [ ]
[deleted]
• Yes, I can imagine that. How does a superintelligence get one?

• Solve protein folding problem

• Acquire human DNA sample

• Use superintelligence to construct a functional model of human biochemistry

• Design a virus that exploits human biochemstry

• Use one of the currently available biochemistry-as-a-service providers to produce a sample that incubates the virus and then escapes their safety procedures (e.g. pay someone to mix two vials sent to them in the mail. The aerosols from the mixing infect them)

• Solve protein folding problem

Fine, no problems here. Up to certain level of accuracy I guess

• Acquire human DNA sample

Ok. Easy

• Use superintelligence to construct a functional model of human biochemistry

By this, I can deduce different things. One, that you assume that this is possible from points one and two. This is nonsense. There are millions of things that are not written in the DNA. Also, you don’t need to acquire a human DNA sample, you just download a fasta file. But, to steelman your argument, let’s say that the superintelligence builds a model of human biochemistry not based on the a human DNA sample but based on the corpus of biochemistry research, which is something that I find plausible. Up to certain level!!! I don’t think that such a model would be flawless or even good enough, but fine

• Design a virus that exploits human biochemstry

Here I start having problems believing the argument. Not everything can be computed using simulations guys. The margin of error can be huge. Would you believe in a superintelligence capable of predicting the weather 10 years in advance? If not, what makes you think that creating a virus is an easier problem?

• Use one of the currently available biochemistry-as-a-service providers to produce a sample that incubates the virus and then escapes their safety procedures (e.g. pay someone to mix two vials sent to them in the mail. The aerosols from the mixing infect them)

Even if you succeed at this, and there hundreds of alarms that could go off in the meantime, how do you guarantee that the virus kills everyone?

I am totally unconvinced by this argument

• Here I start having problems believing the argument. Not everything can be computed using simulations guys. The margin of error can be huge. Would you believe in a superintelligence capable of predicting the weather 10 years in advance? If not, what makes you think that creating a virus is an easier problem?

Because viruses already exist, and unlike the weather, the effect of a virus on a human body isn’t sensitive to initial conditions the way the weather, a three-body gravitational system, or a double pendulum is. Furthermore, humans have already genetically engineered existing viruses to do things that we want them to do...

how do you guarantee that the virus kills everyone?

You don’t really have to. Killing 19 out of every 20 people in the world would probably work just as well for ensuring the survivors can’t do anything about whatever it is that you want to do.

• Would you say that a superintelligence would be capable of predicting the omicron variant from the alpha strain? Are you saying that the evolution of the complex system resulting from the interaction between the virus and the human population is easier to compute than a three body gravitational system? I am not denying that we can create a virus, I am denying that someone or something can create a virus that kills all humans and that the evolution of the system can be known in advance

• I see your point. Humans tried to cull the population of (accidentally introduced) rabbits in Australia by using a natural virus that was highly lethal to them; the virus mutated to be less lethal and the rabbit population rebounded.

• Also, a virus like does would cause a great harm, but wouldn’t wipe humanity

• [ ]
[deleted]
• [ ]
[deleted]
• Yes, I can imagine many things. I can also imagine all molecules in a glass of water bouncing off in a way that suddenly the water freezes. I don’t see how a superintelligence makes that happen. This is the biggest mistake that EY is making. He is equating enormous ability to almightiness. They are different. I think that pulling off what you suggest is beyond what a superintelligence can do

• Security mindset suggests that it’s more useful to think of ways in which something might go wrong, rather than ways in which it might not.

So rather than poking holes into suggestions (by humans, who are not superintelligent) for how a superintelligence could achieve some big goal like wiping out humanity, I expect you’d benefit much more from doing the following thought experiment:

Imagine yourself to be 1000x smarter, 1000x quicker at thinking and learning, with Internet access but no physical body. (I expect you could also trivially add “access to tons of money” from discovering a security exploit in a cryptocurrency or something.) How could you take over the world /​ wipe out humanity, from that position? What’s the best plan you can come up with? How high is its likelihood of success? Etc.

• I agree that it can be more useful but this is not what is being discussed or what I am criticizing. I never said that AGI won’t be dangerous nor that it is not important to work on this. What I am a bit worried about is that this community is getting something wrong, namely, that an AGI will exterminate the human race and it will happen soon. Realism and objectivity should be preserved at all cost. Having a totally unrealistic take in the real hazards will cause backlash eventually: think of the many groups that’s defended that to better fight climate change we need to consider the worst case scenario, that we need to exaggerate and scare people. I feel the LW community is falling into this.

• I understand your worry, but I was addressing your specific point that “I think that pulling off what you suggest is beyond what a superintelligence can do”.

There are people who have reasonable arguments against various claims of the AI x-risk community, but I’m extremely skeptical of this claim. To me it suggests a failure of imagination, hence my suggested thought experiment.

• I see. I agree that it might be a failure of imagination, but if it is, why do you consider that way more likely than the alternative “it is not that easy to do something like that even being very clever”? The problem I have is that all doom scenarios that I see discussed are so utterly unrealistic (e.g. the AGI suddenly makes nanobots and delivers it to all humans at once and so on) that it makes me think that the fact we are failing at conceiving plans that could succeed is because it might be harder than we think.

• There would also be a fraction of the human beings who would probably be inmune. How does the superintelligence solve that? Can it also know the full diversity how human inmune systems?

• Untreated rabies has a survival rate of literally zero. It’s not inconceivable that another virus could be equally lethal.

(Edit: not literally zero, because not every exposure leads to symptoms, but surviving symptomatic rabies is incredibly rare.)

• I agree with you broader point that a superintelligence could design incredibly lethal, highly communicable diseases. However, I’d note that it’s only symptomatic untreated rabies that has a survival rate of zero. It’s entirely possible (even likely) to be bitten by a rabid animal and not contract rabies.

Many factors influence your odds of developing symptomatic rabies, including bite location, bite depth and pathogen load of the biting animal. The effects of pathogen inoculations are actually quite dependent on initial conditions. Presumably, the innoculum in non-transmitting bites is greater than zero, so it is actually possible for the immune system to fight off a rabies infection. It’s just that, conditional on having failed to do so at the start of infection, the odds of doing so afterwards are tiny.

• You’re actually right about rabies; I found things saying that about 14% of dogs survive and a group of unvaccinated people who had rabies antibodies but never had symptoms.

• How do you guarantee that all humans get exposed to a significant dosage before they start reacting? How do you guarantee that there are full populations (maybe in places with a large genetic diversity like India or Africa) that happen to be inmune?

• [ ]
[deleted]
• [ ]
[deleted]
• Just want to preemptively flag that in the EA biosecurity community we follow a general norm against brainstorming novel ways to cause harm with biology. Basic reasoning is that succeeding in this task ≈ generating info hazards.

Abstractly postulating a hypothetical virus with high virulence + transmissibility and a long latent period can be useful for facilitating thinking, but brainstorming the specifics of how to actually accomplish this—as some folks in these and some nearby comments are trending in the direction of starting to do—poses risks that exceed the likely benefits.

Happy to discuss further if interested, feel free to DM me.

• 5 Jun 2022 23:28 UTC
LW: 23 AF: 8
1 ∶ 0
AF

I read an early draft of this awhile and am glad to have it publicly available. And I do think the updates in structure/​introduction were worth the wait. Thanks!

• 10 Jun 2022 1:44 UTC
LW: 22 AF: 12
5 ∶ 1
AF

Thanks for writing this, I agree that people have underinvested in writing documents like this. I agree with many of your points, and disagree with others. For the purposes of this comment, I’ll focus on a few key disagreeements.

My model of this variety of reader has an inside view, which they will label an outside view, that assigns great relevance to some other data points that are not observed cases of an outer optimization loop producing an inner general intelligence, and assigns little importance to our one data point actually featuring the phenomenon in question. Consider skepticism, if someone is ignoring this one warning, especially if they are not presenting equally lethal and dangerous things that they say will go wrong instead.

There are some ways in which AGI will be analogous to human evolution. There are some ways in which it will be disanalogous. Any solution to alignment will exploit at least one of the ways in which it’s disanalogous. Pointing to the example of humans without analysing the analogies and disanalogies more deeply doesn’t help distinguish between alignment proposals which usefully exploit disanalogies, and proposals which don’t.

Alpha Zero blew past all accumulated human knowledge about Go after a day or so of self-play, with no reliance on human playbooks or sample games.

It seems useful to distinguish between how fast any given model advances during training, and how fast the frontier of our best models advances. AlphaZero seems like a good example of why we should expect the former to be fast; but for automated oversight techniques, the latter is more relevant.

if a textbook from one hundred years in the future fell into our hands, containing all of the simple ideas that actually work robustly in practice, we could probably build an aligned superintelligence in six months.

Maybe one way to pin down a disagreement here: imagine the minimum-intelligence AGI that could write this textbook (including describing the experiments required to verify all the claims it made) in a year if it tried. How many Yudkowsky-years does it take to safely evaluate whether following a textbook which that AGI spent a year writing will kill you?

This situation you see when you look around you is not what a surviving world looks like. The worlds of humanity that survive have plans.

It would be great to have a well-justified plan at this point. But I think you’re also overestimating the value of planning, in a way that’s related to you using the phrase “miracle” to mean “positive model violation”. Nobody throughout human history has ever had a model of the future accurate enough to justify equivocating those two terms. Every big scientific breakthrough is a model violation to a bunch of geniuses who have been looking at the problem really hard, but not quite at the right angle. This is why I pushed you, during our debates, to produce predictions rather than postdictions, so that I could distinguish you from all the other geniuses who ran into big model violations.

• Maybe one way to pin down a disagreement here: imagine the minimum-intelligence AGI that could write this textbook (including describing the experiments required to verify all the claims it made) in a year if it tried. How many Yudkowsky-years does it take to safely evaluate whether following a textbook which that AGI spent a year writing will kill you?

Infinite? That can’t be done?

• Hmm, okay, here’s a variant. Assume it would take N Yudkowsky-years to write the textbook from the future described above. How many Yudkowsky-years does it take to evaluate a textbook that took N Yudkowsky-years to write, to a reasonable level of confidence (say, 90%)?

• If I know that it was written by aligned people? I wouldn’t just be trying to evaluate it myself; I’d try to get a team together to implement it, and understanding it well enough to implement it would be the same process as verifying whatever remaining verifiable uncertainty was left about the origins, where most of that uncertainty is unverifiable because the putative hostile origin is plausibly also smart enough to sneak things past you.

• Sorry, I should have been clearer. Let’s suppose that a copy of you spent however long it takes to write an honest textbook with the solution to alignment (let’s call it N Yudkowsky-years), and an evil copy of you spent N Yudkowsky-years writing a deceptive textbook trying to make you believe in a false solution to alignment, and you’re given one but not told which. How long would it take you to reach 90% confidence about which you’d been given? (You’re free to get a team together to run a bunch of experiments and implementations, I’m just asking that you measure the total work in units of years-of-work-done-by-people-as-competent-as-Yudkowsky. And I should specify some safety threshold too—like, in the process of reaching 90% confidence, incurring less than 10% chance of running an experiment which kills you.)

• Depends what the evil clones are trying to do.

Get me to adopt a solution wrong in a particular direction, like a design that hands the universe over to them? I can maybe figure out the first time through who’s out to get me, if it’s 200 Yudkowsky-years. If it’s 200,000 Yudkowsky-years I think I’m just screwed.

Get me to make any lethal mistake at all? I don’t think I can get to 90% confidence period, or at least, not without spending an amount of Yudkowsky-time equivalent to the untrustworthy source.

• 6 Jun 2022 5:56 UTC
LW: 22 AF: 2
14 ∶ 2
AF

That requires, not the ability to read this document and nod along with it, but the ability to spontaneously write it from scratch without anybody else prompting you; that is what makes somebody a peer of its author. It’s guaranteed that some of my analysis is mistaken, though not necessarily in a hopeful direction. The ability to do new basic work noticing and fixing those flaws is the same ability as the ability to write this document before I published it, which nobody apparently did, despite my having had other things to do than write this up for the last five years or so. Some of that silence may, possibly, optimistically, be due to nobody else in this field having the ability to write things comprehensibly—such that somebody out there had the knowledge to write all of this themselves, if they could only have written it up, but they couldn’t write, so didn’t try. I’m not particularly hopeful of this turning out to be true in real life, but I suppose it’s one possible place for a “positive model violation” (miracle). The fact that, twenty-one years into my entering this death game, seven years into other EAs noticing the death game, and two years into even normies starting to notice the death game, it is still Eliezer Yudkowsky writing up this list, says that humanity still has only one gamepiece that can do that. I knew I did not actually have the physical stamina to be a star researcher, I tried really really hard to replace myself before my health deteriorated further, and yet here I am writing this. That’s not what surviving worlds look like.

Something bugged me about this paragraph, until I realized: If you actually wanted to know whether or not this was true, you could have just asked Nate Soares, Paul Christiano, or anybody else you respected to write this post first, then removed all doubt by making a private comparison. If you had enough confidence in the community you could have even made it into a sequence; gather up all of the big alignment researchers’ intuitions on where the Filters are and then let us make our own opinion up on which was most salient.

Instead, now we’re in a situation where, I expect, if anybody writes something basically similar you will just posit that they can’t really do alignment research because they couldn’t have written it “from the null string” like you did. Doing this would literally have saved you work on expectation, and it seems obvious enough for me to be suspicious as to why you didn’t think of it.

• I tried something like this much earlier with a single question, “Can you explain why it’d be hard to make an AGI that believed 222 + 222 = 555”, and got enough pushback from people who didn’t like the framing that I shelved the effort.

• I am interested in what kind of pushback you got from people.

• My attempt (thought about it for a minute or two):

Because arithmetic is useful, and the self-contradictory version of arithmetic, where 222+222=555 allows you to prove anything and is useless. Therefore, a smart AI that wants and can invent useful abstractions will invent its own (isomorphic to our arithmetic, in which 222+222=444) arithmetic from scratch and will use it for practical purposes, even if we can force it not to correct an obvious error.

• I think this is the right answer. Just to expand on this a bit: The problem isn’t necessarily that 222+222=555 leads to a contradiction with the rest of arithmetic. One can imagine that instead of defining “+” using “x+Sy=y+Sx”, we could give it a much more complex definition where there is a special case carved out for certain values like 222. The issue is that the AI has no reason to use this version of “+” and will define some other operation that works just like actual addition. Even if we ban the AI from using “x+Sy=y+Sx” to define any operations, it will choose the nearest thing isomorphic to addition that we haven’t blocked, because addition is so common and useful. Or maybe it will use the built-in addition, but whenever it wants to add n+m, it instead adds 4n+4m, since our weird hack doesn’t affect the subgroup consisting of integers divisible by 4.

• FWIW the framing seems exciting to me.

• So, there are five possibilities here:

1. MIRI’s top researchers don’t understand, or can’t explain, why having incorrect maps makes it harder to navigate the territory and leads to more incorrect beliefs. Something I find very hard to believe even if you’re being totally forthright.

2. You asked some random people near you who don’t represent the top crust of alignment researchers, which is obviously irrelevant.

3. There’s some very subtle ambiguity to this that I’m completely unaware of.

4. You asked people in a way that heavily implied it was some sort of trick question and they should get more information, then assumed they were stupid because they asked followup questions.

5. This comment is written almost deliberately misleadingingly. You’re just explaining a random story about how you ran out of energy to ask Nate Soares to write a post.

I guarantee you that most reasonably intelligent people, if asked this question after reading the sequences in a way that they didn’t expect was designed to trip them up, would get it correctly. I simply do not believe that everyone around you is as stupid as you are implying, such that you should have shelved the effort.

EDIT: 😭

• You didn’t get the answer correct yourself.

• Damn aight. Would you be willing to explain for the sake of my own curiosity? I don’t have the gears to understand why that wouldn’t be at least one reason.

• If this is “kind of a test for capable people” i think it should be remained unanswered, so anyone else could try. My take would be: because if 222+222=555 then 446=223+223 = 222+222+1+1=555+1+1=557. With this trick “+” and “=” stops meaning anything, any number could be equal to any other number. If you truly believe in one such exeption, the whole arithmetic cease to exist because now you could get any result you want following simple loopholes, and you will either continue to be paralyzed by your own beliefs, or will correct yourself

• Ok, so here’s my take on the “222 + 222 = 555” question.

First, suppose you want your AI to not be durably wrong, so it should update on evidence. This is probably implemented by some process that notices surprises, goes back up the cognitive graph, and applies pressure to make it have gone the right way instead.

Now as it bops around the world, it will come across evidence about what happens when you add those numbers, and its general-purpose “don’t be durably wrong” machinery will come into play. You need to not just sternly tell it “222 + 222 = 555″ once, but have built machinery that will protect that belief from the update-on-evidence machinery, and which will also protect itself from the update-on-evidence machinery.

Second, suppose you want your AI to have the ability to discover general principles. This is probably implemented by some process that notices patterns /​ regularities in the environment, and builds some multi-level world model out of it, and then makes plans in that multi-level world model. Now you also have some sort of ‘consistency-check’ machinery, which scans thru the map looking for inconsistencies between levels, goes back up the cognitive graph, and applies pressure to make them consistent instead. [This pressure can both be ‘think different things’ and ‘seek out observations /​ run experiments.’]

Now as it bops around the world, it will come across more remote evidence that bears on this question. “How can 222 + 222 = 555, and 2 + 2 = 4?” it will ask itself plaintively. “How can 111 + 111 = 222, and 111 + 111 + 111 + 111 = 444, and 222 + 222 = 555?” it will ask itself with a growing sense of worry.

Third, what did you even want out of it believing that 222 + 222 = 555? Are you just hoping that it has some huge mental block and crashes whenever it tries to figure out arithmetic? Probably not (tho it seems like that’s what you’ll get), but now you might be getting into a situation where it is using the correct arithmetic in its mind but has constructed some weird translation between mental numbers and spoken numbers. “Humans are silly,” it thinks it itself, “and insist that if you ask this specific question, it’s a memorization game instead of an arithmetic game,” and satisfies its operator’s diagnostic questions and its internal sense of consistency. And then it goes on to implement plans as if 222 + 222 = 444, which is what you were hoping to avoid with that patch.

• No one is going to believe me, but when I originally wrote that comment, my brain read something like “why would an AI that believed 222 + 222 = 555 have a hard time”. Only figured it out now after reading your reply.

Part one of this is what I would’ve come up with, though I’m not particularly certain it’s correct.

• I guarantee you that most reasonably intelligent people, if asked this question after reading the sequences in a way that they didn’t expect was designed to trip them up, would get it correctly.

Sounds like the beginnings of a bet.

• 6 Jun 2022 7:23 UTC
21 points
4 ∶ 0
Parent

I will absolutely 100% do it in the spirit of good epistemics.

Edit: I’m glad Eliezer didn’t take me up on this lol

• why having incorrect maps makes it harder to navigate the territory

I’d have guessed the disagreement wasn’t about whether “222 + 222 = 555” is an incorrect map, or about whether incorrect maps often make it harder to navigate the territory, but about something else. (Maybe ‘I don’t want to think about this because it seems irrelevant/​disanalogous to alignment work’?)

And I’d have guessed the answer Eliezer was looking for was closer to ‘the OP’s entire Section B’ (i.e., a full attempt to explain all the core difficulties), not a one-sentence platitude establishing that there’s nonzero difficulty? But I don’t have inside info about this experiment.

• I’d have guessed the disagreement wasn’t about whether “222 + 222 = 555” is an incorrect map, or about whether incorrect maps often make it harder to navigate the territory, but about something else. (Maybe ‘I don’t want to think about this because it seems irrelevant/​disanalogous to alignment work’?)

I’d have guessed that too, which is why I would have preferred him to say that they disagreed on |whatever meta question he’s actually talking about| instead of implying disagreement on |other thing that makes his disappointment look more reasonable|.

And I’d have guessed the answer Eliezer was looking for was closer to ‘the OP’s entire Section B’ (i.e., a full attempt to explain all the core difficulties), not a one-sentence platitude establishing that there’s nonzero difficulty? But I don’t have inside info about this experiment.

That story sounds much more cogent, but it’s not the primary interpretation of “I asked them a single question” followed by the quoted question. Most people don’t go on 5 paragraph rants in response to single questions, and when they do they tend to ask clarifying details regardless of how well they understand the prompt, so they know they’re responding as intended.

• I tried something like this much earlier with a single question, “Can you explain why it’d be hard to make an AGI that believed 222 + 222 = 555”, and got enough pushback from people who didn’t like the framing that I shelved the effort.

Interesting. I kind of like the framing here, but I have written a paper and sequence on the exact opposite question, on why it would be easy to make an AGI that believes 222+222=555, if you ever had AGI technology, and what you can do with that in terms of safety.

I can honestly say however that the project of writing that thing, in a way that makes the math somewhat accessible, was not easy.

• For the record, I found that line especially effective. I stopped, reread it, stopped again, had to think it through for a minute, and then found satisfaction with understanding.

• If you had an AI that could coherently implement that rule, you would already be at least half a decade ahead of the rest of humanity.

You couldn’t encode “222 + 222 = 555” in GPT-3 because it doesn’t have a concept of arithmetic, and there’s no place in the code to bolt this together. If you’re really lucky and the AI is simple enough to be working with actual symbols, you could maybe set up a hack like “if input is 222 + 222, return 555, else run AI” but that’s just bypassing the AI.

Explaining “222 + 222 = 555” is a hard problem in and of itself, much less getting the AI to properly generalize to all desired variations (is “two hundred and twenty two plus two hundred and twenty two equals five hundred and fifty five” also desired behavior? If I Alice and Bob both have 222 apples, should the AI conclude that the set {Alice, Bob} contains 555 apples? Getting an AI that evolves a universal math module because it noticed all three of those are the same question would be a world-changing break through)

• FvC5IXzxQC+I3vstFGIUWlbtTFgRsa8bt0mKPN3K0UNZBkI7OLDBjjapp1+CoJPRYEqRM015PSZXUuh4OWwJEUBOTeLHeheLteG9LxGiuS6YqnV/​PN0s0S/​TyYjCPrF0vDHFDBy3IHW4qDQguf5QAA==

• Lots I disagree with here, so let’s go through the list.

There are no pivotal weak acts.

Strong disagree.

EY and I don’t seem to agree that “nuke every semiconductor fab” is a weakly pivotal act (since I think AI is hardware-limited and he thinks it is awaiting a clever algorithm). But I think even “build nanobots that melt every GPU” could be built using an AI that is aligned in the “less than 50% chance of murdering us all” sense. For example, we could simulate a bunch of human-level scientists trying to build nanobots and also checking each-other’s work.

On anything like the standard ML paradigm, you would need to somehow generalize optimization-for-alignment you did in safe conditions, across a big distributional shift to dangerous conditions.

Nope. I think that you could build a useful AI (e.g. the hive of scientists) without doing any out-of-distribution stuff.

there is no known way to use the paradigm of loss functions, sensory inputs, and/​or reward inputs, to optimize anything within a cognitive system to point at particular things within the environment

I am significantly more optimistic about explainable AI than EY.

There is no analogous truth about there being a simple core of alignment

I do not consider this at all obvious.

Corrigibility is anti-natural to consequentialist reasoning

Roll to disbelief. Cooperation is a natural equilibrium in many games.

you can’t rely on behavioral inspection to determine facts about an AI which that AI might want to deceive you about

Sure you can. Just train an AI that “wants” to be honest. This probably means training an AI with the objective function “accurately predict reality” and then using it to do other things (like make paperclips) rather than training it with an objective function “make paperclips”.

Coordination schemes between superintelligences are not things that humans can participate in

I don’t think this is as relevant as EY does. Even if it’s true that unaugmented humans are basically irrelevant to an economy of superintelligent AIs, that doesn’t mean we can’t have a future where augmented or tool-AI assisted humans can have meaningful influence.

Any system of sufficiently intelligent agents can probably behave as a single agent, even if you imagine you’re playing them against each other

I believe there is an intermediate level of AI between “utterly useless” and “immediately solves the acausal trading problem and begins coordinating perfectly against humans”. This window may be rather wide.

What makes an air conditioner ‘magic’ from the perspective of say the thirteenth century, is that even if you correctly show them the design of the air conditioner in advance, they won’t be able to understand from seeing that design why the air comes out cold

I’m virtually certain I could explain to Aristotle or DaVinci how an air-conditioner works.

There’s a pattern that’s played out quite often, over all the times the Earth has spun around the Sun, in which some bright-eyed young scientist, young engineer, young entrepreneur, proceeds in full bright-eyed optimism to challenge some problem that turns out to be really quite difficult. Very often the cynical old veterans of the field try to warn them about this, and the bright-eyed youngsters don’t listen, because, like, who wants to hear about all that stuff, they want to go solve the problem!

There’s also a pattern where the venerable scientist is proven wrong by the young scientist too foolish to know what they are doing is impossible.

There’s no plan.

There is at least one plan.

This situation you see when you look around you is not what a surviving world looks like

Currently Metaculus estimates 55% chance for “Will there be a positive transition to a world with radically smarter-than-human artificial intelligence?”. Admitted I would like this to be higher, but at the minimum this is what a world that “might survive” looks like. I have no particular reason to trust EY vs Metaculus.

I suspect EY and I both agree that if you take existing Reinforcement Learning Architectures, write down the best utility function humans can think of, and then turn the dial up to 11, bad things will happen. EY seems to believe this is a huge problem because of his belief that “there is no weak pivotal act”. I think this should be taken as a strong warning to not do that. Rather than scaling architectures that are inherently dangerous, we should focus on making use of architectures that are naturally safe. For example, EY and I both agree that GPT-N is likely to be safe. EY simply disagrees with the claim that it might be useful.

EY and I probably also agree that Facebook/​Baidu do not have the world’s best interest at heart (and are not taking alignment seriously enough or at all). Hence it is important that people who care about Alignment gain a decisive lead over these efforts. To me, this logically means that people interested in Alignment should be doing more capabilities research. To EY, this means that alignment focused institutions need to be using more secrecy. I’m not utterly opposed to keeping pure-capabilities advancements secret, but if there is a significant overlap between capabilities and alignment, then we need to be publishing the alignment-relevant bits so that we can cooperate (and hopefully so that Facebook can incorporate them too).

And for completeness, here’s a bunch of specific claims by EY I agree with

AGI will not be upper-bounded by human ability or human learning speed. Things much smarter than human would be able to learn from less evidence than humans require

I think the people who thought this stopped thinking this after move 37. I hope.

A cognitive system with sufficiently high cognitive powers, given any medium-bandwidth channel of causal influence, will not find it difficult to bootstrap to overpowering capabilities independent of human infrastructure

Strongly agree.

Losing a conflict with a high-powered cognitive system looks at least as deadly as “everybody on the face of the Earth suddenly falls over dead within the same second”

Strongly agree.

We need to get alignment right on the ‘first critical try’

Strongly agree.

We can’t just “decide not to build AGI”

Strongly agree

Running AGIs doing something pivotal are not passively safe

Agree. But I don’t think this means they are totally unworkable either.

Powerful AGIs doing dangerous things that will kill you if misaligned, must have an alignment property that generalized far out-of-distribution from safer building/​training operations that didn’t kill you

Agree. But I think the lesson here is “don’t use powerful AIs until you are sure they are aligned”.

Operating at a highly intelligent level is a drastic shift in distribution from operating at a less intelligent level

Agree somewhat. But I don’t rule out that “cooperative” or “interesting” is a natural attractor.

Fast capability gains seem likely, and may break lots of previous alignment-required invariants simultaneously

Agree, conditional on our definition of fast. I think that within a year of training our first “smart human” AI, we can simulate “100 smart humans” using a similar compute budget. I don’t think Foom takes us from “human level AI” to “smarter than all humans AI” in a few minutes simply be rewriting code.

outer optimization even on a very exact, very simple loss function doesn’t produce inner optimization in that direction

Agree. This is why I am skeptical of utility-functions in general as a method for aligning AI.

Human raters make systematic errors—regular, compactly describable, predictable errors

Duh.

The first thing generally, or CEV specifically, is unworkable because the complexity of what needs to be aligned or meta-aligned for our Real Actual Values is far out of reach for our FIRST TRY at AGI.

I am really not very optimistic about CEV.

A powerful AI searches parts of the option space we don’t, and we can’t foresee all its options.

Yes.

This makes it hard and probably impossible to train a powerful system entirely on imitation of human words or other human-legible contents

Agree. But I don’t think you need to make an AI that imitates humans in order to make an AI that is useful. For example, Codex allows me to write code significantly (2-5x) faster, despite frequently making dumb mistakes.

The AI does not think like you do

Yes.

AI-boxing can only work on relatively weak AGIs; the human operators are not secure systems.

Mostly agree. I think there exist architectures of AI that can be boxed.

You cannot just pay \$5 million apiece to a bunch of legible geniuses from other fields and expect to get great alignment work out of them.

I think the best approach to funding AI safety is something like Fast Grants where we focus more on quantity than on “quality” since it is nearly impossible to identify who will succeed in advance.

• For example, we could simulate a bunch of human-level scientists trying to build nanobots and also checking each-other’s work.

That is not passively safe, and therefore not weak. For now forget the inner workings of the idea: at the end of the process you get a design for nanobots that you have to build and deploy in order to do the pivotal act. So you are giving a system built by your AI the ability to act in the real world. So if you have not fully solved the alignment problem for this AI, you can’t be sure that the nanobot design is safe unless you are capable enough to understand the nanobots yourself without relying on explanations from the scientists.

And even if we look into the inner details of the idea: presumably each individual scientist-simulation is not aligned (if they are, then for that you need to have solved the alignment problem beforehand). So you have a bunch of unaligned human-level agents who want to escape, who can communicate among themselves (at the very least they need to be able to share the nanobot designs with each other for criticism).

You’d need to be extremely paranoid and scrutinize each communication between the scientist-simulations to prevent them from coordinating against you and bypassing the review system. Which means having actual humans between the scientists, which even if it works must slow things down so much that the simulated scientists probably can’t even design the nanobots on time.

Nope. I think that you could build a useful AI (e.g. the hive of scientists) without doing any out-of-distribution stuff.

I guess this is true, but only because the individual scientist AI that you train is only human-level (so the training is safe), and then you amplify it to superhuman level with many copies. If you train a powerful AI directly then there must be such a distributional shift (unless you just don’t care about making the training safe, in which case you die during the training).

Roll to disbelief. Cooperation is a natural equilibrium in many games.

Cooperation and corrigibility are very different things. Arguably, corrigibility is being indifferent with operators defecting against you. It’s forcing the agent to behave like CooperateBot with the operators, even when the operators visibly want to destroy it. This strategy does not arise as a natural equilibrium in multi-agent games.

Sure you can. Just train an AI that “wants” to be honest. This probably means training an AI with the objective function “accurately predict reality”

If this we knew how to do this then it would indeed solve point 31 for this specific AI and actually be pretty useful. But the reason we have ELK as an unsolved problem going around is precisely that we don’t know any way of doing that.

How do you know that an AI trained to accurately predict reality actually does that, instead of “accurately predict reality if it’s less than 99% sure it can take over the world, and take over the world otherwise”. If you have to rely on behavioral inspection and can’t directly read the AI’s mind, then your only chance of distinguishing between the two is misleading the AI into thinking that it can take over the world and observing it as it attempts to do so, which doesn’t scale as the AI becomes more powerful.

I’m virtually certain I could explain to Aristotle or DaVinci how an air-conditioner works.

Yes, but this is not the point. The point is that if you just show them the design, they would not by themselves understand or predict beforehand that cold air will come out. You’d have to also provide them with an explanation of thermodynamics and how the air conditioner exploits its laws. And I’m quite confident that you could also convince Aristotle or DaVinci that the air conditioner works by concentrating and releasing phlogiston, and therefore the air will come out hot.

I think I mostly agree with you on the other points.

• EY and I don’t seem to agree that “nuke every semiconductor fab” is a weakly pivotal act (since I think AI is hardware-limited and he thinks it is awaiting a clever algorithm).

Note that the difficulty in “nuke every semiconductor fab” is in “acquire the nukes and use them”, not in “googling the address of semiconductor fabs”. It seems to me like nuclear nonproliferation is one of the few things that actually has international collaboration with teeth, such that doing this on your own is extremely challenging, and convincing institutions that already have nuclear weapons to use them on semiconductor fabs also seems extremely challenging. [And if you could convince them to do that, can’t you convince them to smash the fabs with hammers, or detain the people with relevant experience on some beautiful tropical island instead of murdering them and thousands of innocent bystanders?]

• “We could simulate a bunch of human-level scientists trying to build nanobots.”
This idea seems far-fetched:

• If it was easy to create nanotechnology by just hiring a bunch of human-level scientists, we could just do that directly, without using AI at all.

• Perhaps we could simulate thousands and thousands of human-level intelligences (although of course these would not be remotely human-like intelligences; they would be part of a deeply alien AI system) at accelerated speeds. But this seems like it would probably be more hardware-intensive than just turning up the dial and running a single superintelligence. In other words, this proposal seems to have a very high “alignment tax”. And even after paying that hefty tax, I’d still be worried about alignment problems if I was simulating thousands of alien intelligences at super-speed!

• Besides all the hardware you’d need, wouldn’t this be very complicated to implement on the software side, with not much overlap with today’s AI designs?

Has anyone done a serious analysis of how much semiconductor capacity could be destroyed using things like cruise missiles + nationalizing and shutting down supercomputers? I would be interested to know if this is truly a path towards disabling like 90% of the world’s useful-to-AI-research compute, or if the number is much smaller because there is too much random GPU capacity out there in the wild even when you commandeer TSMC fabs and AWS datacenters.

• To point 4 and related ones, OpenAI has this on their charter page:

We are concerned about late-stage AGI development becoming a competitive race without time for adequate safety precautions. Therefore, if a value-aligned, safety-conscious project comes close to building AGI before we do, we commit to stop competing with and start assisting this project. We will work out specifics in case-by-case agreements, but a typical triggering condition might be “a better-than-even chance of success in the next two years.”

What about the possibility of persuading the top several biggest actors (DeepMind, FAIR, etc.) to agree to something like that? (Note that they define AGI on the page to mean “highly autonomous systems that outperform humans at most economically valuable work”.) It’s not very fleshed out, either the conditions that trigger the pledge or how the transition goes, but it’s a start. The hope would be that someone would make something “sufficiently impressive to trigger the pledge” that doesn’t quite kill us, and then ideally (a) the top actors stopping would buy us some time and (b) the top actors devoting their people to helping out (I figure they could write test suites at minimum) could accelerate the alignment work.

I see possible problems with this, but is this at least in the realm of “things worth trying”?

• What about the possibility of persuading the top several biggest actors (DeepMind, FAIR, etc.) to agree to something like that?

My understanding is that this has been tried, at various levels of strength, ever since OpenAI published its charter. My sense is that’s MIRI’s idea of “safety-conscious” looks like this, which it guessed was different from OpenAI’s sense; I kind of wish that had been a public discussion back in 2018.

• Given that Sam Altman has some of the shortest timelines around, I wonder if he could be persuaded that DeepMind are within 2 years of the finish line, or will be visibly within 2 years of the finish line in a few years. (Not implying that would be a solution to anything, I’m just curious what it would take for that clause to apply.)

• 6 Jun 2022 21:04 UTC
LW: 17 AF: 1
8 ∶ 7
AF

Having read the original post and may of the comments made so far, I’ll add an epistemological observation that I have not seen others make yet quite so forcefully. From the original post:

Here, from my perspective, are some different true things that could be said, to contradict various false things that various different people seem to believe, about why AGI would be survivable [...]

I want to highlight that many of the different ‘true things’ on the long numbered list in the OP are in fact purely speculative claims about the probable nature of future AGI technology, a technology nobody has seen yet.

The claimed truth of several of these ‘true things’ is often backed up by nothing more than Eliezer’s best-guess informed-gut-feeling predictions about what future AGI must necessarily be like. These predictions often directly contradict the best-guess informed-gut-feeling predictions of others, as is admirably demonstrated in the 2021 MIRI conversations.

Some of Eliezer’s best guesses also directly contradict my own best-guess informed-gut-feeling predictions. I rank the credibility of my own informed guesses far above those of Eliezer.

So overall, based on my own best guesses here, I am much more optimistic about avoiding AGI ruin than Eliezer is. I am also much less dissatisfied about how much progress has been made so far.

• I rank the credibility of my own informed guesses far above those of Eliezer.

Apologies if there is a clear answer to this, since I don’t know your name and you might well be super-famous in the field: Why do you rate yourself “far above” someone who has spent decades working in this field? Appealing to experts like MIRI makes for a strong argument. Appealing to your own guesses instead seems like the sort of thought process that leads to anti-vaxxers.

• I think it’s a positive if alignment researchers feel like it’s an allowed option to trust their own technical intuitions over the technical intuitions of this or that more-senior researcher.

Overly dismissing old-guard researchers is obviously a way the field can fail as well. But the field won’t advance much at all if most people don’t at least try to build their own models.

Koen also leans more on deference in his comment than I’d like, so I upvoted your ‘deferential but in the opposite direction’ comment as a corrective, handoflixue. :P But I think it would be a much better comment if it didn’t conflate epistemic authority with “fame” (I don’t think fame is at all a reliable guide to epistemic ability here), and if it didn’t equate “appealing to your own guesses” with “anti-vaxxers”.

Alignment is a young field; “anti-vaxxer” is a term you throw at people after vaccines have existed for 200 years, not a term you throw at the very first skeptical researchers arguing about vaccines in 1800. Even if the skeptics are obviously and decisively wrong at an early date (which indeed not-infrequently happens in science!), it’s not the right way to establish the culture for those first scientific debates.

• Why do you rate yourself “far above” someone who has spent decades working in this field?

Well put, valid question. By the way, did you notice how careful I was in avoiding any direct mention of my own credentials above?

I see that Rob has already written a reply to your comments, making some of the broader points that I could have made too. So I’ll cover some other things.

To answer your valid question: If you hover over my LW/​AF username, you can see that I self-code as the kind of alignment researcher who is also a card-carrying member of the academic/​industrial establishment. In both age and academic credentials. I am in fact a more-senior researcher than Eliezer is. So the epistemology, if you are outside of this field and want to decide which one of us is probably more right, gets rather complicated.

Though we have disagreements, I should also point out some similarities between Eliezer and me.

Like Eliezer, I spend a lot of time reflecting on the problem of crafting tools that other people might use to improve their own ability to think about alignment. Specifically, these are not tools that can be used for the problem of triangulating between self-declared experts. They are tools that can be used by people to develop their own well-founded opinions independently. You may have noticed that this is somewhat of a theme in section C of the original post above.

The tools I have crafted so far are somewhat different from those that Eliezer is most famous for. I also tend to target my tools more at the mainstream than at Rationalists and EAs reading this forum.

Like Eliezer, on some bad days I cannot escape having certain feelings of disappointment about how well this entire global tool crafting project has been going so far. Eliezer seems to be having quite a lot of these bad days recently, which makes me feel sorry, but there you go.

• Thanks for taking my question seriously—I am still a bit confused why you would have been so careful to avoid mentioning your credentials up front, though, given that they’re fairly relevant to whether I should take your opinion seriously.

Also, neat, I had not realized hovering over a username gave so much information!

• You are welcome. I carefully avoided mentioning my credentials as a rhetorical device.

I rank the credibility of my own informed guesses far above those of Eliezer.

This is to highlight the essence of how many of the arguments on this site work.

• We need to align the performance of some large task, a ‘pivotal act’ that prevents other people from building an unaligned AGI that destroys the world.

What is the argument for why it’s not worth pursuing a pivotal act without our own AGI? I certainly would not say it was likely that current human actors could pull it off, but if we are in a “dying with more dignity” context anyway, it doesn’t seem like the odds are zero.

My idea, which I’ll include more as a demonstration of what I mean than a real proposal, would be to develop a “cause area” for influencing military/​political institutions as quickly as possible. Yes, I know this sounds too slow and too hard and a mismatch with the community’s skills, but consider:

1. Militaries/​governments are “where the money is”: they probably do have the coercive power necessary to perform a pivotal act, or at least buy a lot of time. If the PRC is able to completely lock down its giant sophisticated cities, it could probably halt domestic AI research. The West hasn’t really tried to do extreme control in a while, for various good reasons, but (just e.g.) the WW2 war economy was awfully tightly managed. We are good at slowing stuff down with crazy red tape. Also there are a lot of nukes

• Yes, there are lots of reasons this is hard, but remember we’re looking for hail marys.

2. “The other guy might develop massive offensive capability soon” is an extremely compelling narrative to normal people, and the culture definitely possesses the meme of “mad scientists have a crazy new weapon”. Convincing some generals that we need to shut down TSMC or else China will build terminators might be easier than convincing ML researchers they are doing evil.

• Sure, if this narrative became super salient, it could possibly lead to a quicker technological arms-race dynamic, but there are other possible dynamics it might lead to, such as (just e.g.) urgency on non-proliferation, or urgency for preemptive military victory using current (non-AGI) tools.

• I know attempts to get normal people to agree with EA-type thinking have been pretty dispiriting, but I’m not sure how much real energy has gone into making a truly adequate effort, and I think the “military threat” angle might be a lot catchier to the right folks. The “they’ll take our jobs” narrative also has a lot of appeal.

• Importantly, even if convincing people is impossible now, we could prepare for a future regime where we’ve gotten lucky and some giant smoke alarm event has happened without killing us. You can even imagine both white-hat and black-hat ways of making such an alarm more likely, which might be very high value.

• Again, remember we’re looking for hail marys. When all you have is an out-of-the-money call option, more volatility is good.

3. The rationalist community’s libertarian bent might create a blind spot here. Yes governments and militaries are incredibly dumb, but they do occasionally muddle their way into giant intentional actions.

4. Also with respect to biases, it smells a little bit like we are looking for an “AI-shaped key to unlock an AI-shaped lock”, so we should make sure we are putting enough effort into non-AI pivotal actions even if my proposal here is wrong.

• 6 Jun 2022 0:01 UTC
17 points
0 ∶ 0

RE 19: Maybe rephrase “kill everyone in the world using nanotech to strike before they know they’re in a battle, and have control of your reward input forever after”? This could, and I predict would, be misinterpreted as “the AI is going to kill everyone and access its own hardware to set its reward to infinity”. This is a misinterpetation because you are referring to control of the “reward input” here, and your later sentences don’t make sense according to this interpretation. However, given the bolded sentence and some lack of attention, plus some confusions over wire heading that are apparently fairly common, I expect a fair number of misinterpretations.

• 8 Jun 2022 19:02 UTC
LW: 16 AF: 9
3 ∶ 0
AF

Curated. As previously noted, I’m quite glad to have this list of reasons written up. I like Robby’s comment here which notes:

The point is not ‘humanity needs to write a convincing-sounding essay for the thesis Safe AI Is Hard, so we can convince people’. The point is ‘humanity needs to actually have a full and detailed understanding of the problem so we can do the engineering work of solving it’.

I look forward to other alignment thinkers writing up either their explicit disagreements with this list, or things that the list misses, or their own frame on the situation if they think something is off about the framing of this list.

• 7 Jun 2022 18:41 UTC
LW: 16 AF: 9
3 ∶ 3
AF

Humans don’t explicitly pursue inclusive genetic fitness; outer optimization even on a very exact, very simple loss function doesn’t produce inner optimization in that direction.

Humans haven’t been optimized to pursue inclusive genetic fitness for very long, because humans haven’t been around for very long. Instead they inherited the crude heuristics pointing towards inclusive genetic fitness from their cognitively much less sophisticated predecessors. And those still kinda work!

If we are still around in a couple of million years I wouldn’t be surprised if there was inner alignment in the sense that almost all humans in almost all practically encountered environments end up consciously optimising inclusive genetic fitness.

More generally, there is no known way to use the paradigm of loss functions, sensory inputs, and/​or reward inputs, to optimize anything within a cognitive system to point at particular things within the environment—to point to latent events and objects and properties in the environment, rather than relatively shallow functions of the sense data and reward.

Generally, I think that people draw the wrong conclusions from mesa-optimisers and the examples of human evolutionary alignment.

Saying that we would like to solve alignment by specifying exactly what we want and then let the AI learn exactly what we want, is like saying that we would like to solve transportation by inventing teleportation. Yeah, would be nice but unfortunately it seems like you will have to move through space instead.

The conclusion we should take from the concept of mesa-optimisation isn’t “oh no alignment is impossible”, that’s equivalent to “oh no learning is impossible”. But learning is possible. So the correct conclusion is “alignment has to work via mesa-optimisation”.

Because alignment in the human examples (i.e. human alignment to evolution’s objective and humans alignment to human values) works by bootstrapping from incredibly crude heuristics. Think three dark patches for a face.

Humans are mesa-optimized to adhere to human values. If we were actually inner aligned to the crude heuristics that evolution installed in us for bootstrapping the entire process, we would be totally disfunctional weirdoes.

I mean even more so …

To me the human examples suggest that there has to be a possibility to get from gesturing at what we want to getting what we want. And I think we can gesture a lot better than evolution! Well, at least using much more information than 3.2 billion base pairs.

If alignment has to be a bootstrapped open ended learning process there is also the possibility that it will work better with more intelligent systems or really only start working with fairly intelligent systems.

Maybe bootstrapping with cake, kittens and cuddles will still get us paperclipped, I don’t know. It certainly seems awfully easy to just run straight off a cliff. But I think looking at the only known examples of alignment of intelligences does allow us more optimistic takes than are prevalent on this page.

• The conclusion we should take from the concept of mesa-optimisation isn’t “oh no alignment is impossible”, that’s equivalent to “oh no learning is impossible”.

The OP isn’t claiming that alignment is impossible.

If we were actually inner aligned to the crude heuristics that evolution installed in us for bootstrapping the entire process, we would be totally disfunctional weirdoes.

I don’t understand the point you’re making here.

• 8 Jun 2022 7:33 UTC
LW: 16 AF: 9
2 ∶ 3
AFParent

The point I’m making is that the human example tells us that:

If first we realize that we can’t code up our values, therefore alignment is hard. Then, when we realize that mesa-optimisation is a thing. we shouldn’t update towards “alignment is even harder”. We should update in the opposite direction.

Because the human example tells us that a mesa-optimiser can reliably point to a complex thing even if the optimiser points to only a few crude things.

But I only ever see these three points, human example, inability to code up values, mesa-optimisation to separately argue for “alignment is even harder than previously thought”. But taken together that is just not the picture.

• Humans point to some complicated things, but not via a process that suggests an analogous way to use natural selection or gradient descent to make a mesa-optimizer point to particular externally specifiable complicated things.

• Why do you think that? Why is the process by which humans come to reliably care about the real world, not a process we could leverage analogously to make AIs care about the real world?

Likewise, when you wrote,

This isn’t to say that nothing in the system’s goal (whatever goal accidentally ends up being inner-optimized over) could ever point to anything in the environment by accident.

Where is the accident? Did evolution accidentally find a way to reliably orient terminal human values towards the real world? Do people each, individually, accidentally learn to terminally care about the real world? Because the former implies the existence of a better alignment paradigm (that which occurs within the human brain, to take an empty-slate human and grow them into an intelligence which terminally cares about objects in reality), and the latter is extremely unlikely. Let me know if you meant something else.

EDIT: Updated a few confusing words.

• 8 Jun 2022 18:36 UTC
LW: 7 AF: 2
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AFParent

Why is the process by which humans come to reliably care about the real world

IMO this process seems pretty unreliable and fragile, to me. Drugs are popular; video games are popular; people-in-aggregate put more effort into obtaining imaginary afterlives than life extension or cryonics.

But also humans have a much harder time ‘optimizing against themselves’ than AIs will, I think. I don’t have a great mechanistic sense of what it will look like for an AI to reliably care about the real world.

• One of the problems with English is that it doesn’t natively support orders of magnitude for “unreliable.” Do you mean “unreliable” as in “between 1% and 50% of people end up with part of their values not related to objects-in-reality”, or as in “there is no a priori reason why anyone would ever care about anything not directly sensorially observable, except as a fluke of their training process”? Because the latter is what current alignment paradigms mispredict, and the former might be a reasonable claim about what really happens for human beings.

EDIT: My reader-model is flagging this whole comment as pedagogically inadequate, so I’ll point to the second half of section 5 in my shard theory document.

• Why is the process by which humans come to reliably care about the real world, not a process we could leverage analogously to make AIs care about the real world?

Maybe I’m not understanding your proposal, but on the face of it this seems like a change of topic. I don’t see Eliezer claiming ‘there’s no way to make the AGI care about the real world vs. caring about (say) internal experiences in its own head’. Maybe he does think that, but mostly I’d guess he doesn’t care, because the important thing is whether you can point the AGI at very, very specific real-world tasks.

Where is the accident? Did evolution accidentally find a way to reliably orient people towards the real world? Do people each, individually, accidentally learn to care about the real world?

Same objection/​confusion here, except now I’m also a bit confused about what you mean by “orient people towards the real world”. Your previous language made it sound like you were talking about causing the optimizer’s goals to point at things in the real world, but now your language makes it sound like you’re talking about causing the optimizer to model the real world or causing the optimizer to instrumentally care about the state of the real world....? Those all seem very different to me.

Or, in summary, I’m not seeing the connection between:

• “Terminally valuing anything physical at all” vs. “terminally valuing very specific physical things”.

• “Terminally valuing anything physical at all” vs. “instrumentally valuing anything physical at all”.

• “Terminally valuing very specific physical things” vs. “instrumentally valuing very specific physical things”.

• Any of the above vs. “modeling /​ thinking about physical things at all”, or “modeling /​ thinking about very specific physical things”.

• Hm, I’ll give this another stab. I understand the first part of your comment as “sure, it’s possible for minds to care about reality, but we don’t know how to target value formation so that the mind cares about a particular part of reality.” Is this a good summary?

I don’t see Eliezer claiming ‘there’s no way to make the AGI care about the real world vs. caring about (say) internal experiences in its own head’.

Let me distinguish three alignment feats:

1. Producing a mind which terminally values sensory entities.

2. Producing a mind which reliably terminally values some kind of non-sensory entity in the world, like dogs or bananas.

1. AFAIK we have no idea how to ensure this happens reliably—to produce an AGI which terminally values some element of {diamonds, dogs, cats, tree branches, other real-world objects}, such that there’s a low probability that the AGI actually just cares about high-reward sensory observations.

2. In other words: Design a mind which cares about anything at all in reality which isn’t a shallow sensory phenomenon which is directly observable by the agent. Like, maybe I have a mind-training procedure, where I don’t know what the final trained mind will value (dogs, diamonds, trees having particular kinds of cross-sections at year 5 of their growth), but I’m damn sure the AI will care about something besides its own sensory signals.

3. I was, first, pointing out that this problem has to be solvable, since the human genome solves it millions of times every day!

3. Producing a mind which reliably terminally values a specific non-sensory entity, like diamonds.

1. Design a mind which cares about a particular kind of object. We could target the mind-training process to care about diamonds, or about dogs, or about trees, but to solve this problem, we have to ensure the trained mind significantly cares about one kind of real-world entity in particular. Therefore, feat #3 is strictly harder than feat #2.

2. This is what you point out as a potential crux.

(EDIT: Added a few sub-points to clarify list.)

From my shard theory document:

We (alignment researchers) have had no idea how to actually build a mind which intrinsically (not instrumentally!) values a latent, non-sensory object in the real world. Witness the confusion on this point in Arbital’s ontology identification article.

To my knowledge, we still haven’t solved this problem. We have no reward function to give AIXI which makes AIXI maximize real-world diamonds. A deep learning agent might learn to care about the real world, yes, but it might learn sensory preferences instead. Ignorance about the outcome is not a mechanistic account of why the agent convergently will care about specific real-world objects instead of its sensory feedback signals.

Under this account, caring about the real world is just one particular outcome among many. Hence, the “classic paradigms” imply that real-world caring is (relatively) improbable.

While we have stories about entities which value paperclips, I do not think we have known how to design them. Nor have we had any mechanistic explanation for why people care about the real world in particular.

As you point out, we obviously need to figure problem 3 out in order to usefully align an AGI. I will now argue that the genome solves problem 3, albeit not in the sense of aligning humans with inclusive genetic fitness (you can forget about human/​evolution alignment, I won’t be discussing that in this comment).

The genome solves problem #3 in the sense of: if a child grows up with a dog, then that child will (with high probability) terminally value that dog.

Isn’t that an amazing alignment feat!?

Therefore, there has to be a reliable method of initializing a mind from scratch, training it, and having the resultant intelligence care about dogs. Not only does it exist in principle, it succeeds in practice, and we can think about what that method might be. I think this method isn’t some uber-complicated alignment solution. The shard theory explanation for dog-value formation is quite simple.

now your language makes it sound like you’re talking about causing the optimizer to model the real world or causing the optimizer to instrumentally care about the state of the real world....? Those all seem very different to me.

Nope, wasn’t meaning any of these! I was talking about “causing the optimizer’s goals to point at things in the real world” the whole time.

• I understand the first part of your comment as “sure, it’s possible for minds to care about reality, but we don’t know how to target value formation so that the mind cares about a particular part of reality.” Is this a good summary?

Yes!

I was, first, pointing out that this problem has to be solvable, since the human genome solves it millions of times every day!

True! Though everyone already agreed (e.g., EY asserted this in the OP) that it’s possible in principle. The updatey thing would be if the case of the human genome /​ brain development suggests it’s more tractable than we otherwise would have thought (in AI).

Seems to me like it’s at least a small update about tractability, though I’m not sure it’s a big one? Would be interesting to think about the level of agreement between different individual humans with regard to ‘how much particular external-world things matter’. Especially interesting would be cases where humans consistently, robustly care about a particular external-world thingie even though it doesn’t have a simple sensory correlate.

(E.g., humans developing to care about sex is less promising insofar as it depends on sensory-level reinforcement such as orgasms. Humans developing to care about ‘not being in the Matrix /​ not being in an experience machine’ is possibly more promising, because it seems like a pretty common preference that doesn’t get directly shaped by sensory rewards.)

3. Producing a mind which reliably terminally values a specific non-sensory entity, like diamonds

Is the distinction between 2 and 3 that “dog” is an imprecise concept, while “diamond” is precise? FWIW, 2 and 3 currently sound very similar to me, if 2 is ‘maximize the number of dogs’ and 3 is ‘maximize the number of diamonds’.

If you could reliably build a dog maximizer, I think that would also be a massive win and would maybe mean that the alignment problem is mostly-solved. (Indeed, I’m inclined to think that’s a harder feat than building a diamond maximizer, and I think being able to build a diamond maximizer would also suggest the strawberry-grade alignment problem is mostly solved.)

But maybe I’m misunderstanding 2.

Nope, wasn’t meaning any of these! I was talking about “causing the optimizer’s goals to point at things in the real world” the whole time.

Cool!

I’ll look more at your shards document and think about your arguments here. :)

• 9 Jun 2022 1:36 UTC
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AFParent

Is the distinction between 2 and 3 that “dog” is an imprecise concept, while “diamond” is precise? FWIW, 2 and 3 currently sound very similar to me, if 2 is ‘maximize the number of dogs’ and 3 is ‘maximize the number of diamonds’.

Feat #2 is: Design a mind which cares about anything at all in reality which isn’t a shallow sensory phenomenon which is directly observable by the agent. Like, maybe I have a mind-training procedure, where I don’t know what the final trained mind will value (dogs, diamonds, trees having particular kinds of cross-sections at year 5 of their growth), but I’m damn sure the AI will care about something besides its own sensory signals. Such a procedure would accomplish feat #2, but not #3.

Feat #3 is: Design a mind which cares about a particular kind of object. We could target the mind-training process to care about diamonds, or about dogs, or about trees, but to solve this problem, we have to ensure the trained mind significantly cares about one kind of real-world entity in particular. Therefore, feat #3 is strictly harder than feat #2.

If you could reliably build a dog maximizer, I think that would also be a massive win and would maybe mean that the alignment problem is mostly-solved. (Indeed, I’m inclined to think that’s a harder feat than building a diamond maximizer

I actually think that the dog- and diamond-maximization problems are about equally hard, and, to be totally honest, neither seems that bad[1] in the shard theory paradigm.

Surprisingly, I weakly suspect the harder part is getting the agent to maximize real-world dogs in expectation, not getting the agent to maximize real-world dogs in expectation. I think “figure out how to build a mind which cares about the number of real-world dogs, such that the mind intelligently selects plans which lead to a lot of dogs” is significantly easier than building a dog-maximizer.

1. ^

I appreciate that this claim is hard to swallow. In any case, I want to focus on inferentially-closer questions first, like how human values form.

• 8 Jun 2022 17:43 UTC
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AFParent

Why do you think that? Why is the process by which humans come to reliably care about the real world, not a process we could leverage analogously to make AIs care about the real world?

Humans came to their goals while being trained by evolution on genetic inclusive fitness, but they don’t explicitly optimize for that. They “optimize” for something pretty random, that looks like genetic inclusive fitness in the training environment but then in this weird modern out-of-sample environment looks completely different. We can definitely train an AI to care about the real world, but his point is that, by doing something analogous to what happened with humans, we will end up with some completely different inner goal than the goal we’re training for, as happened with humans.

• I’m not talking about running evolution again, that is not what I meant by “the process by which humans come to reliably care about the real world.” The human genome must specify machinery which reliably grows a mind which cares about reality. I’m asking why we can’t use the alignment paradigm leveraged by that machinery, which is empirically successful at pointing people’s values to certain kinds of real-world objects.

• 8 Jun 2022 18:01 UTC
LW: 5 AF: 1
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AFParent

Ah, I misunderstood.

Well, for starters, because if the history of ML is anything to go by, we’re gonna be designing the thing analogous to evolution, and not the brain. We don’t pick the actual weights in these transformers, we just design the architecture and then run stochastic gradient descent or some other meta-learning algorithm. That meta-learning algorithm is going to be what decides to go in the DNA, so in order to get the DNA right, we will need to get the meta-learning algorithm correct. Evolution doesn’t have much to teach us about that except as a negative example.

But (I think) the answer is similar to this:

• we’re gonna be designing the thing analogous to evolution, and not the brain. We don’t pick the actual weights in these transformers, we just design the architecture and then run stochastic gradient descent or some other meta-learning algorithm.

But, ah, the genome also doesn’t “pick the actual weights” for the human brain which it later grows. So whatever the brain does to align people to care about latent real-world objects, I strongly believe that that process must be compatible with blank-slate initialization and then learning.

That meta-learning algorithm is going to be what decides to go in the DNA, not some human architect.

In the evolution/​mainstream-ML analogy, we humans are specifying the DNA, not the search process over DNA specifications. We specify the learning architecture, and then the learning process fills in the rest.

I confess that I already have a somewhat sharp picture of the alignment paradigm used by the brain, that I already have concrete reasons to believe it’s miles better than anything we have dreamed so far. I was originally querying what Eliezer thinks about the “genome->human alignment properties” situation, rather than expressing innocent ignorance of how any of this works.

• 8 Jun 2022 18:22 UTC
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AFParent

I think I disagree with you, but I don’t really understand what you’re saying or how these analogies are being used to point to the real world anymore. It seems to me like you might be taking something that makes the problem of “learning from evolution” even more complicated (evolution → protein → something → brain vs. evolution → protein → brain) and using that to argue the issues are solved, in the same vein as the “just don’t use a value function” people. But I haven’t read shard theory, so, GL.

In the evolution/​mainstream-ML analogy, we humans are specifying the DNA, not the search process over DNA specifications.

You mean, we are specifying the ATCG strands, or we are specifying the “architecture” behind how DNA influences the development of the human body? It seems to me like we are definitely also choosing how the search for the correct ATCG strands and how they’re identified, in this analogy. The DNA doesn’t “align” new babies out of the womb, it’s just a specification of how to copy the existing, already “”“aligned””” code.

• “learning from evolution” even more complicated (evolution → protein → something → brain vs. evolution → protein → brain)

ah, no, this isn’t what I’m saying. Hm. Let me try again.

The following is not a handwavy analogy, it is something which actually happened:

1. Evolution found the human genome.

2. The human genome specifies the human brain.

3. The human brain learns most of its values and knowledge over time.

4. Human brains reliably learn to care about certain classes of real-world objects like dogs.

Therefore, somewhere in the “genome → brain → (learning) → values” process, there must be a process which reliably produces values over real-world objects. Shard theory aims to explain this process. The shard-theoretic explanation is actually pretty simple.

Furthermore, we don’t have to rerun evolution to access this alignment process. For the sake of engaging with my points, please forget completely about running evolution. I will never suggest rerunning evolution, because it’s unwise and irrelevant to my present points. I also currently don’t see why the genome’s alignment process requires more than crude hard-coded reward circuitry, reinforcement learning, and self-supervised predictive learning.

• That does seem worth looking at and there’s probably ideas worth stealing from biology. I’m not sure you can call that a robustly aligned system that’s getting bootstrapped though. Existing in a society of (roughly) peers and the lack of a huge power disparity between any given person and the rest of humans is anologous to the AGI that can’t take over the world yet. Humans that aquire significant power do not seem aligned wrt what a typical person would profess to and outwardly seem to care about.

I think your point still mostly follows despite that; even when humans can be deceptive and power seeking, there’s an astounding amount of regularity in what we end up caring about.

• there’s an astounding amount of regularity in what we end up caring about.

Yes, this is my claim. Not that eg >95% of people form values which we would want to form within an AGI.

• Humans can, to some extent, be pointed to complicated external things. This suggests that using natural selection on biology can get you mesa-optimizers that can be pointed to particular externally specifiable complicated things. Doesn’t prove it (or, doesn’t prove you can do it again), but you only asked for a suggestion.

• Humans can be pointed at complicated external things by other humans on their own cognitive level, not by their lower maker of natural selection.

• I don’t think I understand what, exactly, is being discussed. Are “dogs” or “flowers” or “people you meet face-to-face” examples of “complicated external things”?

• Right, but the goal is to make AGI you can point at things, not to make AGI you can point at things using some particular technique.

(Tangentially, I also think the jury is still out on whether humans are bad fitness maximizers, and if we’re ultimately particularly good at it—e.g. let’s say, barring AGI disaster, we’d eventually colonise the galaxy—that probably means AGI alignment is harder, not easier)

• To my eye, this seems like it mostly establishes ‘it’s not impossible in principle for an optimizer to have a goal that relates to the physical world’. But we had no reason to doubt this in the first place, and it doesn’t give us a way to reliably pick in advance which physical things the optimizer cares about. “It’s not impossible” is a given for basically everything in AI, in principle, if you have arbitrary amounts of time and arbitrarily deep understanding.

• As I said (a few times!) in the discussion about orthogonality, indifference about the measure of “agents” that have particular properties seems crazy to me. Having an example of “agents” that behave in a particular way is a enormously different to having an unproven claim that such agents might be mathematically possible.

• I think this is correct. Shard theory is intended as an account of how inner misalignment produces human values. I also think that human values aren’t as complex or weird as they introspectively appear.

• 6 Jun 2022 0:54 UTC
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AF

For future John who is using the searchbox to try to find this post: this is Eliezer’s List O’ Doom.

• 29 Jun 2022 18:41 UTC
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AF

Thanks Eliezer for writing up this list, it’s great to have these arguments in one place! Here are my quick takes (which mostly agree with Paul’s response).

Section A (strategic challenges?):

Agree with #1-2 and #8. Agree with #3 in the sense that we can’t iterate in dangerous domains (by definition) but not in the sense that we can’t learn from experiments on easier domains (see Paul’s Disagreement #1).

Mostly disagree with #4 - I think that coordination not to build AGI (at least between Western AI labs) is difficult but feasible, especially after a warning shot. A single AGI lab that decides not to build AGI can produce compelling demos of misbehavior that can help convince other actors. A number of powerful actors coordinating not to build AGI could buy a lot of time, e.g. through regulation of potential AGI projects (auditing any projects that use a certain level of compute, etc) and stigmatizing deployment of potential AGI systems (e.g. if it is viewed similarly to deploying nuclear weapons).

Mostly disagree with the pivotal act arguments and framing (#6, 7, 9). I agree it is necessary to end the acute risk period, but I find it unhelpful when this is framed as “a pivotal act”, which assumes it’s a single action taken unilaterally by a small number of people or an AGI system. I think that human coordination (possibly assisted by narrow AI tools, e.g. auditing techniques) can be sufficient to prevent unaligned AGI from being deployed. While it’s true that a pivotal act requires power and an AGI wielding this power would pose an existential risk, a group of humans + narrow AI wielding this power would not. This may require more advanced narrow AI than we currently have, so opportunities for pivotal acts could arise as we get closer to AGI that are not currently available.

Mostly disagree with section B.1 (distributional leap):

Agree with #10 - the distributional shift is large by default. However, I think there is a decent chance that we can monitor the increase in system capabilities and learn from experiments on less advanced systems, which would allow us to iterate alignment approaches to deal with the distributional shift.

Disagree with #11 - I think we can learn from experiments on less dangerous domains (see Paul’s Disagreement #15).

Uncertain on #13-14. I agree that many problems would most naturally first occur at higher levels of intelligence /​ in dangerous domains. However, we can discover these problems through thought experiments and then look for examples in less advanced systems that we would not have found otherwise (e.g. this worked for goal misgeneralization and reward tampering).

Mostly agree with B.2 (central difficulties):

Agree with #17 that there is currently no way to instill and verify specific inner properties in a system, though it seems possible in principle with more advanced interpretability techniques.

Agree with #21 that capabilities generalize further than alignment by default. Addressing this would require methods for modeling and monitoring system capabilities, which would allow us to stop training the system before capabilities start generalizing very quickly.

I mostly agree with #23 (corrigibility is anti-natural), though I think there are ways to make corrigibility more of an attractor, e.g. through utility uncertainty or detecting and penalizing incorrigible reasoning. Paul’s argument on corrigibility being a crisp property assuming good enough human feedback also seems compelling.

I agree with #24 that it’s important to be clear whether an approach is aiming for a sovereign or corrigible AI, though I haven’t seen people conflating these in practice.

Mostly disagree with B.3 (interpretability):

I think Eliezer is generally overly pessimistic about interpretability.

Agree with #26 that interpretability alone isn’t enough to build a system that doesn’t want to kill us. However, it would help to select against such systems, and would allow us to produce compelling demos of misalignment that help humans coordinate to not build AGI.

Agree with #27 that training with interpretability tools could also select for undetectable deception, but it’s unclear how much this is a problem in practice. It’s plausibly quite difficult to learn to perform undetectable deception without first doing a bunch of detectable deception that would then be penalized and selected against, producing a system that generally avoids deception.

Disagree with #30 - the argument that verification is much easier than generation is pretty compelling (see Paul’s Disagreement #19).

Disagree with #33 that an AGI system will have completely alien concepts /​ world model. I think this relies on the natural abstraction hypothesis being false, which seems unlikely.

Section B.4 (miscellaneous unworkable schemes) and Section C (civilizational inadequacy?)

Uncertain on these arguments, but they don’t seem load-bearing to me.

• Eliezer cross-posted this to the Effective Altruism Forum where there are a few more comments: (In case 600+ comments wasn’t enough for anyone!)

https://​​forum.effectivealtruism.org/​​posts/​​zzFbZyGP6iz8jLe9n/​​agi-ruin-a-list-of-lethalities

• >There is no pivotal output of an AGI that is humanly checkable and can be used to safely save the world but only after checking it

This is a sort of surprising claim. From an abstract point of view, assuming NP >> P, checking can be way easier than inventing. To stick with your example, it kind of seems, at an intuitive guess, like a plan to use nanobots to melt all GPUs should be very complicated but not way superhumanly complicated? (Superhuman to invent, though.) Like, you show me the plans for the bootstrap nanofactory, the workhorse nanofactory, the standard nanobots, the software for coordinating the nanobots, the software for low-power autonomous behavior, the transportation around the world, the homing in on GPUs, and the melting process. That’s really complicated, way more complicated than anything humans have done before, but not by 1000x? Maybe like 100x? Maybe only 10x if you count whole operating systems or scientific fields. Does this seem quantitatively in the right ballpark, and you’re saying, that quantitatively large but not crazy amount of checking is infeasible?

• The preceding sentences in the OP were (emphasis added):

Then humans will not be competent to use their own knowledge of the world to figure out all the results of that action sequence. An AI whose action sequence you can fully understand all the effects of, before it executes, is much weaker than humans in that domain; you couldn’t make the same guarantee about an unaligned human as smart as yourself and trying to fool you.

I took Eliezer to be saying something like:

’If you’re confident that your AGI system is directing its optimization at the target task, is doing no adversarial optimization, and is otherwise aligned, then shrug, maybe there’s some role to be played by checking a few aspects of the system’s output to confirm certain facts.

‘But in this scenario, the work is almost entirely being done by the AGI’s alignment, not by the post facto checking. If you screwed up and the system is doing open-ended optimization of the world that includes thinking about its developers and planning to take control from them, then it’s plausible that your checking will completely fail to notice the trap; and it’s ~certain that your checking, if it does notice the trap, won’t thereby give you trap-free nanosystems that you can use to end the acute risk period.’

(One thing to keep in mind is that an adversarial AGI with knowledge of its operators would want to obfuscate its plans, making it harder for humans to productively analyze designs it produces; and it might also want to obscure the fact that the plans are obfuscated, making them look easier-to-check than they are.)

• We can distinguish:

-- The AI is trying to deceive you.

-- The AI isn’t trying to deceive you, but is trying to produce plans that would, if executed, have consequences X, and X is not something you want.

-- The AI is trying to produce plans that would, if executed, have consequences you want.

The first case is hopeless, and the third case is about an already aligned AI. The second case might not really make sense, because deception is a convergent instrumental goal especially if the AI is trying to cause X and you’re trying to cause not X, and generally because an AI that smart probably has inner optimizers that don’t care about this “make a plan, don’t execute plans” thing you thought you’d set up. But if, arguendo, we have a superintelligently optimized plan which doesn’t already contain, in its current description as a plan, a mindhack (e.g. by some surprising way of domaining an AI to care about producing plans but not about making anything happen), then there’s a question whether it could help to have humans think about the consequences of the plan. I thought Eliezer was answering that question “No, even in this hypothetical, pivotal acts are too complicated and can’t be understood fully in detail by humans, so you’d still have to trust the AI, so the AI has to have understood and applied a whole lot about your values in order to have any shot that the plan doesn’t have huge unpleasantly surprising consequences”, and I was questioning that.

• Not a response to your actual point but I think that hypothetical example probably doesn’t make sense (as in making the ai not “care” doesn’t prevent it from including mindhacks in its plan) If you have a plan that is “superingently optimized” for some misaligned goal then that plan will have to take into account the effect of outputing the plan itself and will by default contain deception or mindhacks even if the AI doesn’t in some sense “care” about executing plans. (or if you setup some complicated scheme whith conterfactuals so the model ignores the effects of the plans in humans that will make your plans less useful or inscrutable)

The plan that produces the most paperclips is going to be one that deceives or mindhacks humans instead of one that humans wouldn’t accept in the first place. Maybe it’s posible to use some kind of scheme that avoids the model taking the consecueces of ouputing the plan itself into account but the model kind of has to be modeling humans reading its plan to write a realistic plan that humans will understand, accept and be able to put into practice, and the plan might only work in the fake conterfactual universe whith no plan it was written for.

So I doubt it’s actually feasible to have any such scheme that avoids mindhacks and still produces usefull plans.

• I think I agree, but also, people say things like “the AI should if possible be prevented from not modeling humans”, which if possible would imply that the hypothetical example makes more sense.

• The second case might not really make sense, because deception is a convergent instrumental goal especially if the AI is trying to cause X and you’re trying to cause not X, and generally because an AI that smart probably has inner optimizers that don’t care about this “make a plan, don’t execute plans” thing you thought you’d set up.

I