A central AI alignment problem: capabilities generalization, and the sharp left turn
(This post was factored out of a larger post that I (Nate Soares) wrote, with help from Rob Bensinger, who also rearranged some pieces and added some text to smooth things out. I’m not terribly happy with it, but am posting it anyway (or, well, having Rob post it on my behalf while I travel) on the theory that it’s better than nothing.)
I expect navigating the acute risk period to be tricky for our civilization, for a number of reasons. Success looks to me to require clearing a variety of technical, sociopolitical, and moral hurdles, and while in principle sufficient mastery of solutions to the technical problems might substitute for solutions to the sociopolitical and other problems, it nevertheless looks to me like we need a lot of things to go right.
Some sub-problems look harder to me than others. For instance, people are still regularly surprised when I tell them that I think the hard bits are much more technical than moral: it looks to me like figuring out how to aim an AGI at all is harder than figuring out where to aim it.
Within the list of technical obstacles, there are some that strike me as more central than others, like “figure out how to aim optimization”. And a big reason why I’m currently fairly pessimistic about humanity’s odds is that it seems to me like almost nobody is focusing on the technical challenges that seem most central and unavoidable to me.
Many people wrongly believe that I’m pessimistic because I think the alignment problem is extraordinarily difficult on a purely technical level. That’s flatly false, and is pretty high up there on my list of least favorite misconceptions of my views.
I think the problem is a normal problem of mastering some scientific field, as humanity has done many times before. Maybe it’s somewhat trickier, on account of (e.g.) intelligence being more complicated than, say, physics; maybe it’s somewhat easier on account of how we have more introspective access to a working mind than we have to the low-level physical fields; but on the whole, I doubt it’s all that qualitatively different than the sorts of summits humanity has surmounted before.
It’s made trickier by the fact that we probably have to attain mastery of general intelligence before we spend a bunch of time working with general intelligences (on account of how we seem likely to kill ourselves by accident within a few years, once we have AGIs on hand, if no pivotal act occurs), but that alone is not enough to undermine my hope.
What undermines my hope is that nobody seems to be working on the hard bits, and I don’t currently expect most people to become convinced that they need to solve those hard bits until it’s too late.
Below, I’ll attempt to sketch out what I mean by “the hard bits” of the alignment problem. Although these look hard, I’m a believer in the capacity of humanity to solve technical problems at this level of difficulty when we put our minds to it. My concern is that I currently don’t think the field is trying to solve this problem. My hope in writing this post is to better point at the problem, with a follow-on hope that this causes new researchers entering the field to attack what seem to me to be the central challenges head-on.
Discussion of a problem
On my model, one of the most central technical challenges of alignment—and one that every viable alignment plan will probably need to grapple with—is the issue that capabilities generalize better than alignment.
My guess for how AI progress goes is that at some point, some team gets an AI that starts generalizing sufficiently well, sufficiently far outside of its training distribution, that it can gain mastery of fields like physics, bioengineering, and psychology, to a high enough degree that it more-or-less singlehandedly threatens the entire world. Probably without needing explicit training for its most skilled feats, any more than humans needed many generations of killing off the least-successful rocket engineers to refine our brains towards rocket-engineering before humanity managed to achieve a moon landing.
And in the same stroke that its capabilities leap forward, its alignment properties are revealed to be shallow, and to fail to generalize. The central analogy here is that optimizing apes for inclusive genetic fitness (IGF) doesn’t make the resulting humans optimize mentally for IGF. Like, sure, the apes are eating because they have a hunger instinct and having sex because it feels good—but it’s not like they could be eating/fornicating due to explicit reasoning about how those activities lead to more IGF. They can’t yet perform the sort of abstract reasoning that would correctly justify those actions in terms of IGF. And then, when they start to generalize well in the way of humans, they predictably don’t suddenly start eating/fornicating because of abstract reasoning about IGF, even though they now could. Instead, they invent condoms, and fight you if you try to remove their enjoyment of good food (telling them to just calculate IGF manually). The alignment properties you lauded before the capabilities started to generalize, predictably fail to generalize with the capabilities.
Some people I say this to respond with arguments like: “Surely, before a smaller team could get an AGI that can master subjects like biotech and engineering well enough to kill all humans, some other, larger entity such as a state actor will have a somewhat worse AI that can handle biotech and engineering somewhat less well, but in a way that prevents any one AGI from running away with the whole future?”
I respond with arguments like, “In the one real example of intelligence being developed we have to look at, continuous application of natural selection in fact found Homo sapiens sapiens, and the capability-gain curves of the ecosystem for various measurables were in fact sharply kinked by this new species (e.g., using machines, we sharply outperform other animals on well-established metrics such as “airspeed”, “altitude”, and “cargo carrying capacity”).”
Their response in turn is generally some variant of “well, natural selection wasn’t optimizing very intelligently” or “maybe humans weren’t all that sharply above evolutionary trends” or “maybe the power that let humans beat the rest of the ecosystem was simply the invention of culture, and nothing embedded in our own already-existing culture can beat us” or suchlike.
Rather than arguing further here, I’ll just say that failing to believe the hard problem exists is one surefire way to avoid tackling it.
So, flatly summarizing my point instead of arguing for it: it looks to me like there will at some point be some sort of “sharp left turn”, as systems start to work really well in domains really far beyond the environments of their training—domains that allow for significant reshaping of the world, in the way that humans reshape the world and chimps don’t. And that’s where (according to me) things start to get crazy. In particular, I think that once AI capabilities start to generalize in this particular way, it’s predictably the case that the alignment of the system will fail to generalize with it.
This is slightly upstream of a couple other challenges I consider quite core and difficult to avoid, including:
Directing a capable AGI towards an objective of your choosing.
Ensuring that the AGI is low-impact, conservative, shutdownable, and otherwise corrigible.
These two problems appear in the strawberry problem, which Eliezer’s been pointing at for quite some time: the problem of getting an AI to place two identical (down to the cellular but not molecular level) strawberries on a plate, and then do nothing else. The demand of cellular-level copying forces the AI to be capable; the fact that we can get it to duplicate a strawberry instead of doing some other thing demonstrates our ability to direct it; the fact that it does nothing else indicates that it’s corrigible (or really well aligned to a delicate human intuitive notion of inaction).
How is the “capabilities generalize further than alignment” problem upstream of these problems? Suppose that the fictional team OpenMind is training up a variety of AI systems, before one of them takes that sharp left turn. Suppose they’ve put the AI in lots of different video-game and simulated environments, and they’ve had good luck training it to pursue an objective that the operators described in English. “I don’t know what those MIRI folks were talking about; these systems are easy to direct; simple training suffices”, they say. At the same time, they apply various training methods, some simple and some clever, to cause the system to allow itself to be removed from various games by certain “operator-designated” characters in those games, in the name of shutdownability. And they use various techniques to prevent it from stripmining in Minecraft, in the name of low-impact. And they train it on a variety of moral dilemmas, and find that it can be trained to give correct answers to moral questions (such as “in thus-and-such a circumstance, should you poison the operator’s opponent?”) just as well as it can be trained to give correct answers to any other sort of question. “Well,” they say, “this alignment thing sure was easy. I guess we lucked out.”
The techniques OpenMind used to train it away from the error where it convinces itself that bad situations are unlikely? Those generalize fine. The techniques you used to train it to allow the operators to shut it down? Those fall apart, and the AGI starts wanting to avoid shutdown, including wanting to deceive you if it’s useful to do so.
Why does alignment fail while capabilities generalize, at least by default and in predictable practice? In large part, because good capabilities form something like an attractor well. (That’s one of the reasons to expect intelligent systems to eventually make that sharp left turn if you push them far enough, and it’s why natural selection managed to stumble into general intelligence with no understanding, foresight, or steering.)
Many different training scenarios are teaching your AI the same instrumental lessons, about how to think in accurate and useful ways. Furthermore, those lessons are underwritten by a simple logical structure, much like the simple laws of arithmetic that abstractly underwrite a wide variety of empirical arithmetical facts about what happens when you add four people’s bags of apples together on a table and then divide the contents among two people.
But that attractor well? It’s got a free parameter. And that parameter is what the AGI is optimizing for. And there’s no analogously-strong attractor well pulling the AGI’s objectives towards your preferred objectives.
The hard left turn? That’s your system sliding into the capabilities well. (You don’t need to fall all that far to do impressive stuff; humans are better at an enormous variety of relevant skills than chimps, but they aren’t all that lawful in an absolute sense.)
There’s no analogous alignment well to slide into.
On the contrary, sliding down the capabilities well is liable to break a bunch of your existing alignment properties.
Why? Because things in the capabilities well have instrumental incentives that cut against your alignment patches. Just like how your previous arithmetic errors (such as the pebble sorters on the wrong side of the Great War of 1957) get steamrolled by the development of arithmetic, so too will your attempts to make the AGI low-impact and shutdownable ultimately (by default, and in the absence of technical solutions to core alignment problems) get steamrolled by a system that pits those reflexes / intuitions / much-more-alien-behavioral-patterns against the convergent instrumental incentive to survive the day.
Perhaps this is not convincing; perhaps to convince you we’d need to go deeper into the weeds of the various counterarguments, if you are to be convinced. (Like acknowledging that humans, who can foresee these difficulties and adjust their training procedures accordingly, have a better chance than natural selection did, while then discussing why current proposals do not seem to me to be hopeful.) But hopefully you can at least, in reading this document, develop a basic understanding of my position.
Stating it again, in summary: my position is that capabilities generalize further than alignment (once capabilities start to generalize real well (which is a thing I predict will happen)). And this, by default, ruins your ability to direct the AGI (that has slipped down the capabilities well), and breaks whatever constraints you were hoping would keep it corrigible. And addressing the problem looks like finding some way to either keep your system aligned through that sharp left turn, or render it aligned afterwards.
In an upcoming post (edit: here), I’ll say more about how it looks to me like ~nobody is working on this particular hard problem, by briefly reviewing a variety of current alignment research proposals. In short, I think that the field’s current range of approaches nearly all assume this problem away, or direct their attention elsewhere.
Furthermore, figuring where to aim it looks to me like more of a technical problem than a moral problem. Attempting to manually specify the nature of goodness is a doomed endeavor, of course, but that’s fine, because we can instead specify processes for figuring out (the coherent extrapolation of) what humans value. Which still looks prohibitively difficult as a goal to give humanity’s first AGI (which I expect to be deployed under significant time pressure), mind you, and I further recommend aiming humanity’s first AGI systems at simple limited goals that end the acute risk period and then cede stewardship of the future to some process that can reliably do the “aim minds towards the right thing” thing. So today’s alignment problems are a few steps removed from tricky moral questions, on my models.
While we’re at it: I think trying to get provable safety guarantees about our AGI systems is silly, and I’m pretty happy to follow Eliezer in calling an AGI “safe” if it has a <50% chance of killing >1B people. Also, I think there’s a very large chance of AGI killing us, and I thoroughly disclaim the argument that even if the probability is tiny then we should work on it anyway because the stakes are high.
Note that this is consistent with findings like “large language models perform just as well on moral dilemmas as they perform on non-moral ones”; to find this reassuring is to misunderstand the problem. Chimps have an easier time than squirrels following and learning from human cues. Yet this fact doesn’t particularly mean that enhanced chimps are more likely than enhanced squirrels to remove their hunger drives, once they understand inclusive genetic fitness and are able to eat purely for reasons of fitness maximization. Pre-left-turn AIs will get better at various ‘alignment’ metrics, in ways that I expect to build a false sense of security, without addressing the lurking difficulties.
“What do you mean ‘it takes a sharp left turn’? Are you talking about recursive self-improvement? I thought you said somewhere else that you don’t think recursive self-improvement is necessarily going to play a central role before the extinction of humanity?” I’m not talking about recursive self-improvement. That’s one way to take a sharp left turn, and it could happen, but note that humans have neither the understanding nor control over their own minds to recursively self-improve, and we outstrip the rest of the animals pretty handily. I’m talking about something more like “intelligence that is general enough to be dangerous”, the sort of thing that humans have and chimps don’t.
“Hold on, isn’t this unfalsifiable? Aren’t you saying that you’re going to continue believing that alignment is hard, even as we get evidence that it’s easy?” Well, I contend that “GPT can learn to answer moral questions just as well as it can learn to answer other questions” is not much evidence either way about the difficulty of alignment. I’m not saying we’ll get evidence that I’ll ignore; I’m naming in advance some things that I wouldn’t consider negative evidence (partially in hopes that I can refer back to this post when people crow later and request an update). But, yes, my model does have the inconvenient property that people who are skeptical now, are liable to remain skeptical until it’s too late, because most of the evidence I expect to give us advance warning about the nature of the problem is evidence that we’ve already seen. I assure you that I do not consider this property to be convenient.
As for things that could convince me otherwise: technical understanding of intelligence could undermine my “sharp left turn” model. I could also imagine observing some ephemeral hopefully-I’ll-know-it-when-I-see-it capabilities thresholds, without any sharp left turns, that might update me. (Short of “full superintelligence without a sharp left turn”, which would obviously convince me but comes too late in the game to shift my attention.)
To use my overly-detailed evocative example from earlier: Humans aren’t tempted to rewire our own brains so that we stop liking good meals for the sake of good meals, and start eating only insofar as we know we have to eat to reproduce (or, rather, maximize inclusive genetic fitness) (after upgrading the rest of our minds such that that sort of calculation doesn’t drag down the rest of the fitness maximization). The cleverer humans are chomping at the bit to have their beliefs be more accurate, but they’re not chomping at the bit to replace all these mere-shallow-correlates of inclusive genetic fitness with explicit maximization. So too with other minds, at least by default: that which makes them generally intelligent, does not make them motivated by your objectives.