I think I’d worry that the sets of values that do well under human-evolution/learning conditions is too broad (for a good-according-to-non-selfish-us outcome to be likely). I.e. that re-rolling values under similar evolutionary pressures can give you various value-sets that each achieve similar fitness (or even similar behaviour) but where maximizing utility according to one gets you very low utility according to the others.
Important clarification: Neither Quintin nor I are proposing to mimic evolution in order to hopefully (fingers crossed!) miraculously get human values out the other side. Based on an understanding of how inner alignment works (or doesn’t), Quintin is proposing a gears-level model of what human values are and how they form; the model in turn suggests a relatively simple procedure for recreating the important part of the process within an AI. For growing human values within an AI, not via some hacky solution which is too complicated to shoot down, but based on a gears-level theory of what human values are. No outer selection pressures on evolving AIs or anything like that.
(I know so much about Quintin’s proposal is that I’ve read and written several private docs about the theory.)
By default, AI systems won’t be subject to anything like the environment and pressures that shaped humans and human values. We could aim to create (something analogous to) it, but it’s anything but straightforward. How fragile is the process for humans? Which aspects can be safely simplified/skipped, and how would we know?
Not a full-length explanation, but some thoughts:
I currently think the process is not that fragile. By contrast, consider another (perhaps “classic”) model of alignment. In this model, the “human objective” is an extremely complicated utility function, and we need to get it just right or the future will be ruined. This model has always seemed “off” to me, but I hadn’t been able to put my finger on why.
Quintin’s theory says that the seeming complexity of human values is actually the result of the multiagent bargains struck by subagentic circuits in the brain of varying sophistication, which (explicitly or implicitly) care about different things. Instead of one highly complicated object (“the utility function”) which is sensitive to misspecification, human values is just the multiagent behavior of a set of relatively simple circuits in the brain, where the alignment desirability is somewhat robust to the “bargaining strengths” of those parts.
For example, consider a modified version of yourself who grew up with swapped internal reward for “scuttling spiders” and “small fluffy animals.” I think you’d get along mostly fine, and be able to strike bargains like “this part of the galaxy will have bunnies, this other part will have spiders” without either of you wanting to tile the galaxy with representations of your “utility function.”
And under that view, we do not have to mimic the entire training process because we don’t know what mattered and what didn’t. Quintin’s theory is, in effect, making a claim about what matters: The substance of human values is the multiagent dynamics and relative bargaining strengths of the different parts, and the fact that these parts generally act to preserve their implementation in the brain (prevent value drift) by steering the future observations of the human itself. In a world where we actually get a correct theory of human values, that theory would tell you which parts are important and which parts can be left out. (There is, of course, still the question of how would we know the theory is right. The above does not answer this question.)
It occurs to me that I’m not sure whether you mean [human rewards in evolution] or [rewards for individual learning humans], or both? I’m assuming the evolutionary version, since I’m not clear what inner alignment failure would mean for an individual (what defines the intended goal/behaviour?).
I don’t know what you mean by “human rewards in evolution.” For my part, I’m talking about the reward signals provided by the steering system in a person’s brain. Although some people are hedonists, many are not, and thus they are unaligned with their reward system. If you don’t want to wirehead, you are not trying to optimize the objective encoded by the steering system in your own brain, and that’s an inner alignment failure with respect to that system. So something else must be steering your decision-making.
Thanks for this. I hope to have thoughts at some point, but first need to think about it more carefully.
One immediate response—since I already know what I think on this bit (it’s not clear to me that this implies any significant object-level disagreement—it may just amount to my saying “those are weird words to use”):
For my part, I’m talking about the reward signals provided by the steering system in a person’s brain. Although some people are hedonists, many are not, and thus they are unaligned with their reward system.
This seems too narrow a concept of what reward is (e.g. hedonism == aligned-with-reward-system). There isn’t an objective human reward signal that mirrors an RL agent’s reward.
We get a load of input, have a bunch of impressions, feelings and thoughts, and take some actions. Labelling of some simple part of that as the reward strikes me as silly (“a reward”, sure). What could be the justification? If we’re clearly not maximising it, nor learning to maximise it (nor trying to...), in what sense is it analogous to RL reward?
The reasonable move seems to be to say “Oops, I was wrong to label that as ‘the reward’, there’s no direct parallel here”, and not “there’s an inner misalignment”.
I’d note that evolution will have implicitly accounted for any previous “misalignment” in shaping our current reward signals: it will have selected for the reward signals that tended to increase fitness given our actual responses to those signals, not the signals that would have increased fitness if we had followed some maximisation process.
Our reward signals weren’t ‘designed’ to be maximised, only to work (to increase fitness).
So it still seems strange to talk about misalignment w.r.t. an objective nothing and nobody was aiming for (even implicitly). It’d seem more useful if there were some crisp and clear mechanistic notion of what counted as human reward and what didn’t; I don’t think that’s true (is anyone claiming this?).
I think I have failed to communicate my main point, if these are among your objections. I am not faulting you, but I want you to know that that’s my perception, and keep that in mind as you evaluate these ideas.
I think I’d want to start over and try from a different tack, if I were going to resolve disagreements here. But best to save that for future posts, I think.
There isn’t an objective human reward signal that mirrors an RL agent’s reward.
We get a load of input, have a bunch of impressions, feelings and thoughts, and take some actions.
You’re the second person to confidently have this reaction, and I’m pretty confused why. Here’s a wikipedia article on the human reward system, and here’s one of Steve Byrnes’s posts on the topic. I’m not an expert, but it seems pretty clear that the brain implements some feedback signals beyond self-supervised predictive learning on sensory errors. Those signals comprise the outer criterion, in this argument.
I agree that reward is not literally implemented in the brain as a scalar reward function. But it doesn’t have to be. The brain implements an outer criterion which evaluates and reinforces behavior/predictions and incentivizes some plans over others along different dimensions.
It’s immaterial whether that’s a simple scalar or a bunch of subsystems with different feedback dimensions—the same inner misalignment arguments apply. Otherwise we could solve inner misalignment by simply avoiding scalar outer criteria; this is absurd.
(Let me know if I’ve misunderstood what you were getting at.)
I’d note that evolution will have implicitly accounted for any previous “misalignment” in shaping our current reward signals: it will have selected for the reward signals that tended to increase fitness given our actual responses to those signals, not the signals that would have increased fitness if we had followed some maximisation process.
Our reward signals weren’t ‘designed’ to be maximised, only to work (to increase fitness).
This is indeed part of my argument, but doesn’t seem related to what I was trying to say.
It’d seem more useful if there were some crisp and clear mechanistic notion of what counted as human reward and what didn’t; I don’t think that’s true (is anyone claiming this?).
There’s an outer criterion by which behavior is graded / feedback is given. A mesa optimizer might be trained (by the usual arguments) which optimizes an outer objective, which is not the same as the outer criterion. We don’t need a crisp and clear mechanistic notion of what counts as human reward for this argument to work.
[EDIT: see my response to this comment; this one is at least mildly confused]
[Again, I want to flag that this line of thinking/disagreement is not the most interesting part of what you/Quintin are saying overall—the other stuff I intend to think more about; nonetheless, I do think it’s important to get to the bottom of the disagreement here, in case anything more interesting hinges upon it]
[JC: There isn’t an objective human reward signal that mirrors an RL agent’s reward.]
You’re the second person to confidently have this reaction, and I’m pretty confused why.
My objection here is all in the ”...that mirrors an RL agent’s reward.”—that’s where the parallel doesn’t work in my view. An RL agent is trained to maximize total (discounted) reward. The brain isn’t maximizing total reward, nor trying to maximize total reward, nor is evolution acting on the basis that it’ll do either of these things.
I agree with the following:
The brain implements an outer criterion which evaluates and reinforces behavior/predictions and incentivizes some plans over others along different dimensions.
I just don’t think this tells us anything useful, since this criterion clearly is not maximisation of total discounted reward. (though I would expect some correlation)
It seems to me that the criterion is more like maximisation of in-the-moment reward (I’m using ‘reward’ here very broadly). I.e. I might work rather than have fun since the thought of working happened to be more ‘rewarding’ than the thought of having fun. (similarly, I might not wirehead, since the thought of wireheading is negative)
This seems essentially vacuous, because I don’t see a way to measure itm-reward better than: if I did x rather than y, then x was more itm-rewarding than y. (to be clear, I’m saying this is not useful—but that I don’t see a principled definition of itm-reward that doesn’t amount to this; this is where a “crisp and clear mechanistic notion of what counted as human reward” would be handy—in order to come up with a non-vacuous definition)
Perhaps it’s clearer if I back up to your previous post and state a crisper disagreement:
If you don’t want to wirehead, you are not trying to optimize the objective encoded by the steering system in your own brain, and that’s an inner alignment failure with respect to that system.
This just seems wrong to me. The [objective encoded by the steering system] is not [maximisation of the score assigned by the steering system], but rather [whatever behaviour the steering system tends to produce].
In an RL system these two are similar, precisely because the RL system is designed to steer towards outcomes with high total discounted reward according to its own metric.
In general, steering systems are not like this. The criterion for picking one plan over another can be [expected total reward] or [something entirely different].
Where a system doesn’t use [expected total reward] it seems just plain silly to me to call behaviour misaligned where it doesn’t match [what the system would incentivize if it did use expected total reward]. Of course it doesn’t match, since that’s not how this steering system works.
In this context, I mean the “steering system” to refer to the genetically hardcoded reward circuitry which provides intrinsic rewards when certain hardcoded preconditions are met. It isn’t learned. Maybe that’s part of the confusion?
An RL agent is trained to maximize total (discounted) reward. The brain isn’t maximizing total reward, nor trying to maximize total reward, nor is evolution acting on the basis that it’ll do either of these things.
An RL agent is reinforced for maximizing reward, but unless it has already fulfilled the prophecy of a convergence guarantee or unless it’s doing model-based brute-force planning to maximize reward over its time horizon, the RL agent is not actually maximizing reward, nor is it necessarily trying to maximize total reward.
The [objective encoded by the steering system] is not [maximisation of the score assigned by the steering system], but rather [whatever behaviour the steering system tends to produce].
I don’t understand why you hold this view. We probably are talking past each other?
EG if I just have a crude sugar reward circuit in my brain which activates when I am hungry and my taste buds signal the brain in the right way, and then I learn to like licking real-world lollipops (because that’s the only way I was able to stimulate the circuit on training when my values were forming), then the objective encoded by the reward circuit is… lollipop-licking in real life? But also, if I had only been exposed to chocolate on training, I would have learned to like eating chocolate. But also, if I had only been exposed to electrical taste bud stimulation on training, I would have learned to like electrical stimulation.
IMO the objective encoded by the reward circuit is the maximization of its own activations, that’s the optimal policy.
Anyways, I think it would just make more sense for me to link you to a Gdoc explaining my views. PM’d.
Ok, putting my [maybe I’m missing the point] hat on, it strikes me that the above is considering the learned steering system—which is the outcome of any misalignment. So I probably am missing your point there (I think?). Oops.
However, I still think I’d stick to saying that:
The [objective encoded by the steering system] is not [maximisation of the score assigned by the steering system], but rather [whatever behaviour the steering system tends to produce]
But here I’d need to invoke properties of the original steering system (ignoring the handwaviness of what that means for now), rather than the learned steering system.
I think what matters at that point is sampling of trajectories (perhaps not only this—but at least this). There’s no mechanism in humans to sample in such a way that we’d expect maximisation of reward to be learned in the limit. Neither would we expect one, since evolution doesn’t ‘care’ about reward maximisation.
Absent such a sampling mechanism, the objective encoded isn’t likely to be maximisation of the reward.
To talk about inner misalignment, I think we need to be able to say something like:
Under [learning conditions], we expect system x to maximise y in the limit.
System x does not robustly learn to pursue y (rather than a proxy for y), so that under [different conditions] x no longer maximises y.
Here I don’t think we have (1), since we don’t expect the human system to learn to maximise reward (or minimise regret, or...) in the limit (i.e. this is not the objective encoded by their original steering system).
Anyway, hopefully it’s now clear where I’m coming from—even if I am confused!
My guess is that this doesn’t matter much to your/Quintin’s broader points(?) - beyond that “inner alignment failure” may not be the best description.
Important clarification: Neither Quintin nor I are proposing to mimic evolution in order to hopefully (fingers crossed!) miraculously get human values out the other side. Based on an understanding of how inner alignment works (or doesn’t), Quintin is proposing a gears-level model of what human values are and how they form; the model in turn suggests a relatively simple procedure for recreating the important part of the process within an AI. For growing human values within an AI, not via some hacky solution which is too complicated to shoot down, but based on a gears-level theory of what human values are. No outer selection pressures on evolving AIs or anything like that.
(I know so much about Quintin’s proposal is that I’ve read and written several private docs about the theory.)
Not a full-length explanation, but some thoughts:
I currently think the process is not that fragile. By contrast, consider another (perhaps “classic”) model of alignment. In this model, the “human objective” is an extremely complicated utility function, and we need to get it just right or the future will be ruined. This model has always seemed “off” to me, but I hadn’t been able to put my finger on why.
Quintin’s theory says that the seeming complexity of human values is actually the result of the multiagent bargains struck by subagentic circuits in the brain of varying sophistication, which (explicitly or implicitly) care about different things. Instead of one highly complicated object (“the utility function”) which is sensitive to misspecification, human values is just the multiagent behavior of a set of relatively simple circuits in the brain, where the alignment desirability is somewhat robust to the “bargaining strengths” of those parts.
For example, consider a modified version of yourself who grew up with swapped internal reward for “scuttling spiders” and “small fluffy animals.” I think you’d get along mostly fine, and be able to strike bargains like “this part of the galaxy will have bunnies, this other part will have spiders” without either of you wanting to tile the galaxy with representations of your “utility function.”
And under that view, we do not have to mimic the entire training process because we don’t know what mattered and what didn’t. Quintin’s theory is, in effect, making a claim about what matters: The substance of human values is the multiagent dynamics and relative bargaining strengths of the different parts, and the fact that these parts generally act to preserve their implementation in the brain (prevent value drift) by steering the future observations of the human itself. In a world where we actually get a correct theory of human values, that theory would tell you which parts are important and which parts can be left out. (There is, of course, still the question of how would we know the theory is right. The above does not answer this question.)
I don’t know what you mean by “human rewards in evolution.” For my part, I’m talking about the reward signals provided by the steering system in a person’s brain. Although some people are hedonists, many are not, and thus they are unaligned with their reward system. If you don’t want to wirehead, you are not trying to optimize the objective encoded by the steering system in your own brain, and that’s an inner alignment failure with respect to that system. So something else must be steering your decision-making.
Thanks for this. I hope to have thoughts at some point, but first need to think about it more carefully.
One immediate response—since I already know what I think on this bit (it’s not clear to me that this implies any significant object-level disagreement—it may just amount to my saying “those are weird words to use”):
This seems too narrow a concept of what reward is (e.g. hedonism == aligned-with-reward-system). There isn’t an objective human reward signal that mirrors an RL agent’s reward.
We get a load of input, have a bunch of impressions, feelings and thoughts, and take some actions. Labelling of some simple part of that as the reward strikes me as silly (“a reward”, sure). What could be the justification? If we’re clearly not maximising it, nor learning to maximise it (nor trying to...), in what sense is it analogous to RL reward?
The reasonable move seems to be to say “Oops, I was wrong to label that as ‘the reward’, there’s no direct parallel here”, and not “there’s an inner misalignment”.
I’d note that evolution will have implicitly accounted for any previous “misalignment” in shaping our current reward signals: it will have selected for the reward signals that tended to increase fitness given our actual responses to those signals, not the signals that would have increased fitness if we had followed some maximisation process.
Our reward signals weren’t ‘designed’ to be maximised, only to work (to increase fitness).
So it still seems strange to talk about misalignment w.r.t. an objective nothing and nobody was aiming for (even implicitly). It’d seem more useful if there were some crisp and clear mechanistic notion of what counted as human reward and what didn’t; I don’t think that’s true (is anyone claiming this?).
I think I have failed to communicate my main point, if these are among your objections. I am not faulting you, but I want you to know that that’s my perception, and keep that in mind as you evaluate these ideas.
I think I’d want to start over and try from a different tack, if I were going to resolve disagreements here. But best to save that for future posts, I think.
You’re the second person to confidently have this reaction, and I’m pretty confused why. Here’s a wikipedia article on the human reward system, and here’s one of Steve Byrnes’s posts on the topic. I’m not an expert, but it seems pretty clear that the brain implements some feedback signals beyond self-supervised predictive learning on sensory errors. Those signals comprise the outer criterion, in this argument.
I agree that reward is not literally implemented in the brain as a scalar reward function. But it doesn’t have to be. The brain implements an outer criterion which evaluates and reinforces behavior/predictions and incentivizes some plans over others along different dimensions.
It’s immaterial whether that’s a simple scalar or a bunch of subsystems with different feedback dimensions—the same inner misalignment arguments apply. Otherwise we could solve inner misalignment by simply avoiding scalar outer criteria; this is absurd.
(Let me know if I’ve misunderstood what you were getting at.)
This is indeed part of my argument, but doesn’t seem related to what I was trying to say.
There’s an outer criterion by which behavior is graded / feedback is given. A mesa optimizer might be trained (by the usual arguments) which optimizes an outer objective, which is not the same as the outer criterion. We don’t need a crisp and clear mechanistic notion of what counts as human reward for this argument to work.
[EDIT: see my response to this comment; this one is at least mildly confused]
[Again, I want to flag that this line of thinking/disagreement is not the most interesting part of what you/Quintin are saying overall—the other stuff I intend to think more about; nonetheless, I do think it’s important to get to the bottom of the disagreement here, in case anything more interesting hinges upon it]
My objection here is all in the ”...that mirrors an RL agent’s reward.”—that’s where the parallel doesn’t work in my view. An RL agent is trained to maximize total (discounted) reward. The brain isn’t maximizing total reward, nor trying to maximize total reward, nor is evolution acting on the basis that it’ll do either of these things.
I agree with the following:
I just don’t think this tells us anything useful, since this criterion clearly is not maximisation of total discounted reward. (though I would expect some correlation)
It seems to me that the criterion is more like maximisation of in-the-moment reward (I’m using ‘reward’ here very broadly). I.e. I might work rather than have fun since the thought of working happened to be more ‘rewarding’ than the thought of having fun. (similarly, I might not wirehead, since the thought of wireheading is negative)
This seems essentially vacuous, because I don’t see a way to measure itm-reward better than: if I did x rather than y, then x was more itm-rewarding than y. (to be clear, I’m saying this is not useful—but that I don’t see a principled definition of itm-reward that doesn’t amount to this; this is where a “crisp and clear mechanistic notion of what counted as human reward” would be handy—in order to come up with a non-vacuous definition)
Perhaps it’s clearer if I back up to your previous post and state a crisper disagreement:
This just seems wrong to me. The [objective encoded by the steering system] is not [maximisation of the score assigned by the steering system], but rather [whatever behaviour the steering system tends to produce].
In an RL system these two are similar, precisely because the RL system is designed to steer towards outcomes with high total discounted reward according to its own metric.
In general, steering systems are not like this. The criterion for picking one plan over another can be [expected total reward] or [something entirely different].
Where a system doesn’t use [expected total reward] it seems just plain silly to me to call behaviour misaligned where it doesn’t match [what the system would incentivize if it did use expected total reward]. Of course it doesn’t match, since that’s not how this steering system works.
In this context, I mean the “steering system” to refer to the genetically hardcoded reward circuitry which provides intrinsic rewards when certain hardcoded preconditions are met. It isn’t learned. Maybe that’s part of the confusion?
An RL agent is reinforced for maximizing reward, but unless it has already fulfilled the prophecy of a convergence guarantee or unless it’s doing model-based brute-force planning to maximize reward over its time horizon, the RL agent is not actually maximizing reward, nor is it necessarily trying to maximize total reward.
I don’t understand why you hold this view. We probably are talking past each other?
EG if I just have a crude sugar reward circuit in my brain which activates when I am hungry and my taste buds signal the brain in the right way, and then I learn to like licking real-world lollipops (because that’s the only way I was able to stimulate the circuit on training when my values were forming), then the objective encoded by the reward circuit is… lollipop-licking in real life? But also, if I had only been exposed to chocolate on training, I would have learned to like eating chocolate. But also, if I had only been exposed to electrical taste bud stimulation on training, I would have learned to like electrical stimulation.
IMO the objective encoded by the reward circuit is the maximization of its own activations, that’s the optimal policy.
Anyways, I think it would just make more sense for me to link you to a Gdoc explaining my views. PM’d.
Ok, putting my [maybe I’m missing the point] hat on, it strikes me that the above is considering the learned steering system—which is the outcome of any misalignment. So I probably am missing your point there (I think?). Oops.
However, I still think I’d stick to saying that:
But here I’d need to invoke properties of the original steering system (ignoring the handwaviness of what that means for now), rather than the learned steering system.
I think what matters at that point is sampling of trajectories (perhaps not only this—but at least this). There’s no mechanism in humans to sample in such a way that we’d expect maximisation of reward to be learned in the limit. Neither would we expect one, since evolution doesn’t ‘care’ about reward maximisation.
Absent such a sampling mechanism, the objective encoded isn’t likely to be maximisation of the reward.
To talk about inner misalignment, I think we need to be able to say something like:
Under [learning conditions], we expect system x to maximise y in the limit.
System x does not robustly learn to pursue y (rather than a proxy for y), so that under [different conditions] x no longer maximises y.
Here I don’t think we have (1), since we don’t expect the human system to learn to maximise reward (or minimise regret, or...) in the limit (i.e. this is not the objective encoded by their original steering system).
Anyway, hopefully it’s now clear where I’m coming from—even if I am confused!
My guess is that this doesn’t matter much to your/Quintin’s broader points(?) - beyond that “inner alignment failure” may not be the best description.