Fictionalized/Paraphrased version of a real dialog between me and John Wentworth.
Fictionalized Me: So, in the Eliezer/Richard dialogs, Eliezer is trying to get across this idea that consequentialism deeply permeates optimization, and this is important, and that's one[1] reason why Alignment is Hard. But something about it is confusing and slippery, and he keeps trying to explain it and it keeps not-quite-landing.
I think I get it, but I'm not sure I could explain it. Or, I'm not sure who to explain it to. I don't think I could tell who was making a mistake, where "consequentialism is secretly everywhere" is a useful concept for realizing-the-mistake.
Fictionalized John:[stares at me]
Me: Okay, I guess I'm probably supposed to try and explain this and see what happens instead of hoping you'll save me.
...
Me: Okay, so the part that's confusing here is that this is supposed to be something that Eliezer thinks thoughtful, attentive people like Richard (and Paul?) aren't getting, despite them having read lots of relevant material and paying attention and being generally on board with "alignment is hard."
...so, what is a sort of mistake I could imagine a smart, thoughtful person who read the sequences making here?
My Eliezer-model imagines someone building what they think is an aligned ML system. They've trained it carefully to do things they reflectively approve of, they've put a lot of work into making it interpretable and honest. This Smart Thoughtful Researcher has read the sequences and believes that alignment is hard and whatnot. Nonetheless, they'll have failed to really grok this "consequentialism-is-more-pervasive-and-important-than-you-think" concept. And this will cause doom when they try to scale up their project to accomplish something actually hard.
I... guess what I think Eliezer thinks is that Thoughful Researcher isn't respecting inner optimizers enough. They'll have built their system to be carefully aligned, but to do anything hard, it'll end up generating inner-optimizers that aren't aligned, and the inner-optimizers will kill everyone.
...
John: Nod. But not quite. I think you're still missing something.
You're familiar with the arguments of convergent instrumental goals?
Me: i.e. most agents will end up wanting power/resources/self-preservation/etc?
John: Yeah.
But not only is "wanting power and self preservation" convergently instrumental. Consequentialism is convergently instrumental. Consequentialism is a (relatively) simple, effective process for accomplishing goals, so things that efficiently optimize for goals tend to approximate it.
Now, say there's something hard you want to do, like build a moon base, or cure cancer or whatever. If there were a list of all the possible plans that cure cancer, ranked by "likely to work", most of the plans that might work route through "consequentalism", and "acquire resources."
Not only that, most of the plans route through "acquire resources in a way that is unfriendly to human values." Because in the space of all possible plans, while consequentialism doesn't take that many bits to specify, human values are highly complex and take a lot of bits to specify.
Notice that I just said "in the space of all possible plans, here are the most common plans." I didn't say anything about agents choosing plans or acting in the world. Just listing the plans. And this is important because the hard part lives in the choosing of the plans.
Now, say you build an oracle AI. You've done all the things to try and make it interpretable and honest and such. If you ask it for a plan to cure cancer, what happens?
Me: I guess it gives you a plan, and... the plan probably routes through consequentialist agents acquiring power in an unfriendly way.
Okay, but if I imagine a researcher who is thoughtful but a bit too optimistic, what they might counterargue with is: "Sure, but I'll just inspect the plans for whether they're unfriendly, and not do those plans."
And what I might then counterargue their counterargument with is:
1) Are you sure you can actually tell which plans are unfriendly and which are not?
and,
2) If you're reading very carefully, and paying lots of attention to each plan... you'll still have to read through a lot of plans before you get to one that's actually good.
John: Bingo. I think a lot of people imagine asking an oracle to generate 100 plans, and they think that maybe half the plans will be pretty reasonable. But, the space of plans is huge. Exponentially huge. Most plans just don't work. Most plans that work route through consequentialist optimizers who convergently seek power because you need power to do stuff. But then the space of consequentialist power-seeking plans are still exponentially huge, and most ways of seeking power are unfriendly to human values. The hard part is locating a good plan that cures cancer that isn't hostile to human values in the first place.
Me: And it's not obvious to me whether this problem gets better or worse if you've tried to train the oracle to only output "reasonable seeming plans", since that might output plans that are deceptively unaligned.
John: Do you understand why I brought up this plan/oracle example, when you originally were talking about inner optimizers?
Me: Hmm. Um, kinda. I guess it's important that there was a second example.
John: ...and?
Me: Okay, so partly you're pointing out that hardness of the problem isn't just about getting the AI to do what I want, it's that doing what I want is actually just really hard. Or rather, the part where alignment is hard is precisely when the thing I'm trying to accomplish is hard. Because then I need a powerful plan, and it's hard to specify a search for powerful plans that don't kill everyone.
John: Yeah. One mistake I think people end up making here is that they think the problem lives in the AI-who's-deciding/doing things, as opposed to in the actual raw difficulty of the search.
Me: Gotcha. And it's important that this comes up in at least two places – inner optimizers with an agenty AI, and an oracle that just output plans that would work. And the fact that it shows up in two fairly different places, one of which I hadn't thought of just now, is suggestive that it could show up in even more places I haven't thought of at all.
And this is confusing enough that it wasn't initially obvious to Richard Ngo, who's thought a ton about alignment. Which bodes ill for the majority of alignment researchers who probably are less on-the-ball.
I'm tempted to say "the main reason" why Alignment Is Hard, but then remembered Eliezer specifically reminded everyone not to summarize him as saying things like "the key reason for X" when he didn't actually say that, and often is tailoring his arguments to a particular confusion with his interlocuter.
Fictionalized/Paraphrased version of a real dialog between me and John Wentworth.
Fictionalized Me: So, in the Eliezer/Richard dialogs, Eliezer is trying to get across this idea that consequentialism deeply permeates optimization, and this is important, and that's one[1] reason why Alignment is Hard. But something about it is confusing and slippery, and he keeps trying to explain it and it keeps not-quite-landing.
I think I get it, but I'm not sure I could explain it. Or, I'm not sure who to explain it to. I don't think I could tell who was making a mistake, where "consequentialism is secretly everywhere" is a useful concept for realizing-the-mistake.
Fictionalized John: [stares at me]
Me: Okay, I guess I'm probably supposed to try and explain this and see what happens instead of hoping you'll save me.
...
Me: Okay, so the part that's confusing here is that this is supposed to be something that Eliezer thinks thoughtful, attentive people like Richard (and Paul?) aren't getting, despite them having read lots of relevant material and paying attention and being generally on board with "alignment is hard."
...so, what is a sort of mistake I could imagine a smart, thoughtful person who read the sequences making here?
My Eliezer-model imagines someone building what they think is an aligned ML system. They've trained it carefully to do things they reflectively approve of, they've put a lot of work into making it interpretable and honest. This Smart Thoughtful Researcher has read the sequences and believes that alignment is hard and whatnot. Nonetheless, they'll have failed to really grok this "consequentialism-is-more-pervasive-and-important-than-you-think" concept. And this will cause doom when they try to scale up their project to accomplish something actually hard.
I... guess what I think Eliezer thinks is that Thoughful Researcher isn't respecting inner optimizers enough. They'll have built their system to be carefully aligned, but to do anything hard, it'll end up generating inner-optimizers that aren't aligned, and the inner-optimizers will kill everyone.
...
John: Nod. But not quite. I think you're still missing something.
You're familiar with the arguments of convergent instrumental goals?
Me: i.e. most agents will end up wanting power/resources/self-preservation/etc?
John: Yeah.
But not only is "wanting power and self preservation" convergently instrumental. Consequentialism is convergently instrumental. Consequentialism is a (relatively) simple, effective process for accomplishing goals, so things that efficiently optimize for goals tend to approximate it.
Now, say there's something hard you want to do, like build a moon base, or cure cancer or whatever. If there were a list of all the possible plans that cure cancer, ranked by "likely to work", most of the plans that might work route through "consequentalism", and "acquire resources."
Not only that, most of the plans route through "acquire resources in a way that is unfriendly to human values." Because in the space of all possible plans, while consequentialism doesn't take that many bits to specify, human values are highly complex and take a lot of bits to specify.
Notice that I just said "in the space of all possible plans, here are the most common plans." I didn't say anything about agents choosing plans or acting in the world. Just listing the plans. And this is important because the hard part lives in the choosing of the plans.
Now, say you build an oracle AI. You've done all the things to try and make it interpretable and honest and such. If you ask it for a plan to cure cancer, what happens?
Me: I guess it gives you a plan, and... the plan probably routes through consequentialist agents acquiring power in an unfriendly way.
Okay, but if I imagine a researcher who is thoughtful but a bit too optimistic, what they might counterargue with is: "Sure, but I'll just inspect the plans for whether they're unfriendly, and not do those plans."
And what I might then counterargue their counterargument with is:
1) Are you sure you can actually tell which plans are unfriendly and which are not?
and,
2) If you're reading very carefully, and paying lots of attention to each plan... you'll still have to read through a lot of plans before you get to one that's actually good.
John: Bingo. I think a lot of people imagine asking an oracle to generate 100 plans, and they think that maybe half the plans will be pretty reasonable. But, the space of plans is huge. Exponentially huge. Most plans just don't work. Most plans that work route through consequentialist optimizers who convergently seek power because you need power to do stuff. But then the space of consequentialist power-seeking plans are still exponentially huge, and most ways of seeking power are unfriendly to human values. The hard part is locating a good plan that cures cancer that isn't hostile to human values in the first place.
Me: And it's not obvious to me whether this problem gets better or worse if you've tried to train the oracle to only output "reasonable seeming plans", since that might output plans that are deceptively unaligned.
John: Do you understand why I brought up this plan/oracle example, when you originally were talking about inner optimizers?
Me: Hmm. Um, kinda. I guess it's important that there was a second example.
John: ...and?
Me: Okay, so partly you're pointing out that hardness of the problem isn't just about getting the AI to do what I want, it's that doing what I want is actually just really hard. Or rather, the part where alignment is hard is precisely when the thing I'm trying to accomplish is hard. Because then I need a powerful plan, and it's hard to specify a search for powerful plans that don't kill everyone.
John: Yeah. One mistake I think people end up making here is that they think the problem lives in the AI-who's-deciding/doing things, as opposed to in the actual raw difficulty of the search.
Me: Gotcha. And it's important that this comes up in at least two places – inner optimizers with an agenty AI, and an oracle that just output plans that would work. And the fact that it shows up in two fairly different places, one of which I hadn't thought of just now, is suggestive that it could show up in even more places I haven't thought of at all.
And this is confusing enough that it wasn't initially obvious to Richard Ngo, who's thought a ton about alignment. Which bodes ill for the majority of alignment researchers who probably are less on-the-ball.
I'm tempted to say "the main reason" why Alignment Is Hard, but then remembered Eliezer specifically reminded everyone not to summarize him as saying things like "the key reason for X" when he didn't actually say that, and often is tailoring his arguments to a particular confusion with his interlocuter.