(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.
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.
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.
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.
- 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.
- 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.
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.
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.
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 algorith
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 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.]
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.
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,
... (read more)
- 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 di
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... (read more)
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... (read more)
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.
Some quick thoughts on these points:
On Twitter, Eric Rogstad wrote:
Copying over my reply to Eric:... (read more)
I think most worlds that successfully navigate AGI risk have properties like:
... (read more)
- 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
First, some remarks about the meta-level:
Actually, I don't feel like I learned that much reading this list, compared to what I already knew. [EDIT: To be clear, this know... (read more)
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:
... (read more)
- 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
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 ... (read more)
I'm not sure this is true; or if it's true, I'm not sure it's relevant. But assuming it is true...
... 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:
In both cases, the fact that my brain isn't a single coherent EU maximiz... (read more)
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 dang... (read more)
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 per... (read more)
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.)
... (read more)
- 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 a
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. 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 grad... (read more)
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?
When the rater is flawed, cranking up the power to NP levels blows up the P part of the system.
Do alignment & safety research, set up regulatory bodies and monitoring systems.
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.
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-transl... (read more)
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%.
One of the... (read more)
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... (read more)
[This is a nitpick of the form "one of your side-rants went a bit too far IMO;" feel free to ignore]
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 W... (read more)
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").
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... (read more)
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).
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 ... (read more)
I would distinguish three ways in which different AI systems could be said to "not care about atoms":
... (read more)
- 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.)
- The system can think about atoms/physics, but it only terminally cares about digital things in a simulated environment (e.g., winni
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.
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... (read more)
Mod note: I activated two-axis voting on this post, since it seemed like it would make the conversation go better.
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.
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.... (read more)
Infinite? That can't be done?
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.
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:... (read more)
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 frame... (read more)
Curated. As previously noted, I'm quite glad to have this list of reasons written up. I like Robby's comment here which notes:
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.
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.
Generally, I think that people draw the wrong conclusions from mesa-optimisers and... (read more)
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.
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 orgasm... (read more)
There is one approach to corrigibility that I don't see mentioned in the "tried and failed" post Eliezer linked to here. It's also one that someone at MIRI (Evan Hubinger) among others is still working on: myopia (i.e. myopic cognition).
There are different formulations, but the basic idea is that an AI with myopic cognition would have an extremely high time preference. This means that it would never sacrifice reward now for reward later, and so it would essentially be exempt from instrumental convergence. In theory such an AI would allow itself to be shut down (without forcing shutdown), and it would also not be prone to deceptive alignment.
Myopia isn't fully understood yet and has a number of open problems. It also will likely require verificati... (read more)
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)
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:
... (read more)
- 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-soundin
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!
I'm very glad this list is finally published; I think it's pretty great at covering the space (tho I won't be surprised if we discover a few more points), and making it so that plans can say "yeah, we're targeting a hole we see in number X."
[In particular, I think most of my current hope is targeted at 5 and 6, specifically that we need an AI to do a pivotal act at all; it seems to me like we might be able to transition from this world to a world sophisticated enough to survive on human power. But this is, uh, a pretty remote possibility and I was much happier when I was optimistic about technical alignment.]
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.
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?]
Thanks for writing this. I agree with all of these except for #30, since it seems like checking the output of the AI for correctness/safety should be possible even if the AI is smarter than us, just like checking a mathematical proof can be much easier than coming up with the proof in the first place. It would take a lot of competence, and a dedicated team of computer security / program correctness geniuses, but definitely seems within human abilities. (Obviously the AI would have to be below the level of capability where it can just write down an argument that convinces the proof checkers to let it out of the box. This is a sense in which having the AI produce uncommented machine code may actually be safer than letting it write English at us.)
We might summarise this counterargument to #30 as "verification is easier than generation". The idea is that the AI comes up with a plan (+ explanation of how it works etc.) that the human systems could not have generated themselves, but that human systems can understand and check in retrospect.
Counterclaim to "verification is easier than generation" is that any pivotal act will involve plans that human systems cannot predict the effects of just by looking at the plan. What about the explanation, though? I think the problem there may be more that we don't know how to get the AI to produce a helpful and accurate explanation as opposed to a bogus misleading but plausible-sounding one, not that no helpful explanation exists.
This seems to me like a case of the imaginary hypothetical "weak pivotal act" that nobody can ever produce. If you have a pivotal act you can do via following some procedure that only the AI was smart enough to generate, yet humans are smart enough to verify and smart enough to not be reliably fooled about, NAME THAT ACTUAL WEAK PIVOTAL ACT.
Okay, I will try to name a strong-but-checkable pivotal act.
(Having a strong-but-checkable pivotal act doesn't necessarily translate into having a weak pivotal act. Checkability allows us to tell the difference between a good plan and a trapped plan with high probability, but the AI has no reason to give us a good plan. It will just produce output like "I have insufficient computing power to solve this problem" regardless of whether that's actually true. If we're unusually successful at convincing the AI our checking process is bad when it's actually good, then that AI may give us a trapped plan, which we can then determine is trapped. Of course, one should not risk executing a trapped plan, even if one thinks one has identified and removed all the traps. So even if #30 is false, we are still default-doomed. (I'm not fully certain that we couldn't create some kind of satisficing AI that gets reward 1 if it generates a safe plan, reward 0 if its output is neither helpful nor dangerous, and reward -1 if it generates a trapped plan that gets caught by our checking process. The AI may then decide that it has a higher chance of success if it just submits a safe plan. But I don't know ho... (read more)
Well, I had to think about this for longer than five seconds, so that's already a huge victory.
If I try to compress your idea down to a few sentences:
The humans ask the AI to produce design tools, rather than designs, such that there's a bunch of human cognition that goes into picking out the particular atomic arrangements or synthesis pathways; and we can piecewise verify that the tool is making accurate predictions; and the tool is powerful enough that we can build molecular nanotech and an uploader by using the tool for an amount of time too short for Facebook to catch up and destroy the world. The AI that does this is purportedly sufficiently good at meta-engineering to build the tool, but not good enough at larger strategy that it can hack its way through the humans using just the code of the tool. The way in which this attacks a central difficulty is by making it harder for the AI to just build unhelpful nanotech using the capabilities that humans use to produce helpful nanotech.
Sound about right?
While I share a large degree of pessimism for similar reasons, I am somewhat more optimistic overall.
Most of this comes from generic uncertainty and epistemic humility; I'm a big fan of the inside view, but it's worth noting that this can (roughly) be read as a set of 42 statements that need to be true for us to in fact be doomed, and statistically speaking it seems unlikely that all of these statements are true.
However, there are some more specific points I can point to where I think you are overconfident, or at least not providing good reasons for such a high level of confidence (and to my knowledge nobody has). I'll focus on two disagreements which I think are closest to my true disagreements.
1) I think safe pivotal "weak" acts likely do exist. It seems likely that we can access vastly superhuman capabilities without inducing huge x-risk using a variety of capability control methods. If we could build something that was only N<<infinity times smarter than us, then intuitively it seems unlikely that it would be able to reverse engineer details of the outside world or other AI systems source code (cf 35) necessary to break out of the box or start coo... (read more)
For future John who is using the searchbox to try to find this post: this is Eliezer's List O' Doom.
New-to-me thought I had in response to the kill all humans part. When predators are a threat to you, you of course shoot them. But once you invent cheap tech that can control them you don't need to kill them anymore. The story goes that the AI would kill us either because we are a threat or because we are irrelevant. It seems to me that (and this imports a bunch of extra stuff that would require analysis to turn this into a serious analysis, this is just an idle thought), the first thing I do if I am superintelligent and wanting to secure my position is not take over the earth, which isn't in a particularly useful spot resource wise and instead launch my nanofactory beyond the reach of humans to mercury or something. Similarly, in the nanomachines in everyone's blood that can kill them instantly class of ideas, why do I need at that point to actually pull the switch? I.e. the kill all humans scenario is emotionally salient but doesn't actually clearly follow the power gradients that you want to climb for instrumental convergence reasons?
If humans were able to make one super-powerful AI, then humans would probably be able to make a second super-powerful AI, with different goals, which would then compete with the first AI. Unless, of course, the humans are somehow prevented from making more AIs, e.g. because they're all dead.
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.
Regarding the point about most alignment work not really addressing the core issue: I think that a lot of this work could potentially be valuable nonetheless. People can take inspiration from all kinds of things and I think there is often value in picking something that you can get a grasp on, then using the lessons from that to tackle something more complex. Of course, it's very easy for people to spend all of their time focusing on irrelevant toy problems and never get around to making any progress on the real problem. Plus there are costs with adding more voices into the conversation as it can be tricky for people to distinguish the signal from the noise.
Here is my partial honest reaction, just two points I'm somewhat dissatisfied with (not meant to be exhaustive):
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." I would like there to be an argument for this claim that doesn't rely on nanotech, and solidly relies on actually existing amounts of compute. E.g. if the argument relies on running intractable detailed simulations of prote... (read more)
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:
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 agree with many of the points in this post.
Here's one that I do believe is mistaken in a hopeful direction:... (read more)
I mostly agree with the reasoning here; thank you to Eliezer for posting it and explaining it clearly. It's good to have all these reasons here in once place.
The one area I partly disagree with is Section B.1. As I understand it, the main point of B.1 is that we can't guard against all of the problems that will crop up as AI grows more intelligent, because we can't foresee all of those problems, because most of them will be "out-of-distribution," i.e., not the kinds of problems where we have reasonable training data. A superintelligent AI will do strange t... (read more)