Max Harms


CAST: Corrigibility As Singular Target

Wiki Contributions


I don't think "a corrigible agent wants to do what the principal wants, at all times" matches my proposal. The issue that we're talking here shows up in the math, above, in that the agent needs to consider the principal's values in the future, but those values are themselves dependent on the agent's action. If the principal gave a previous command to optimize for having a certain set of values in the future, sure, the corrigible agent can follow that command, but to proactively optimize for having a certain set of values doesn't seem necessarily corrigible, even if it matches the agent's sense of the present principal's values.

For instance, suppose Monday-Max wants Tuesday-Max to want to want to exercise, but also Monday-Max feels a bunch of caution around self-modification such that he doesn't trust having the AI rearrange his neurons to make this change. It seems to me that the corrigible thing for the AI to do is ignore Monday-Max's preferences and simply follow his instructions (and take other actions related to being correctable), even if Monday-Max's mistrust is unjustified. It seems plausible to me that your "do what the principal wants" agent might manipulate Tuesday-Max into wanting to want to exercise, since that's what Monday-Max wants on the base-level.

Thanks. Picking out those excerpts is very helpful.

I've jotted down my current (confused) thoughts about human values.

But yeah, I basically think one needs to start with a hodgepodge of examples that are selected for being conservative and uncontroversial. I'd collect them by first identifying a robust set of very in-distribution tasks and contexts and try to exhaustively identify what manipulation would look like in that small domain, then aggressively train on passivity outside of that known distribution. The early pseudo-agent will almost certainly be mis-generalizing in a bunch of ways, but if it's set up cautiously we can suspect that it'll err on the side of caution, and that this can be gradually peeled back in a whitelist-style way as the experimentation phase proceeds and attempts to nail down true corrigibility.

Thanks! I now feel unconfused. To briefly echo back the key idea which I heard (and also agree with): a technique which can create a corrigible PAAI might have assumptions which break if that technique is used to make a different kind of AI (i.e. one aimed at CEV). If we call this technique "the Corrigibility method" then we may end up using the Corrigibility method to make AIs that aren't at all corrigible, but merely seem corrigible, resulting in disaster.

This is a useful insight! Thanks for clarifying. :)

  • In "What Makes Corrigibility Special", where you use the metaphor of goals as two-dimensional energy landscape, it is not clear what type of goals are being considered.
    • Are these utility functions over world-states? If so, corrigibility cannot AFAIK be easily expressed as one, and so doesn't really fit into the picture.
    • If not, it's not clear to me why most of this space is flat: agents are embedded and many things we do in service of goals will change us in ways that don't conflict with our existing goals, including developing. E.g. if I have the goal of graduating college I will meet people along the way and perhaps gain the goal of being president of the math club, a liberal political bent, etc.

The idea behind the goal space visualization is to have all goals, not necessarily those restricted to world states. (Corrigibility, I think, involves optimizing over histories, not physical states of the world at some time, for example.) I mention in a footnote that we might want to restrict to "unconfused" goals.

The goal space is flat because preserving one's (terminal) goals (including avoiding adding new ones) is an Omohundro Drive and I'm assuming a certain level of competence/power in these agents. If you gain terminal goals like being president of the math club by going to college, doing so is likely hurting your long-run ability to get what you want. (Note: I am not talking about instrumental goals.)

At that point, it is clever enough to convince the designers that this IO is the objectively correct thing to do, using only methods classified as AE.

I'm confused here. Is the corrigible AI trying to get the IO to happen? Why is it trying to do this? Doesn't seem very corrigible, but I think I'm probably just confused.

Maybe another frame on my confusion is that it seems to me that a corrigible AI can't have an IO?

I'd like to get better at communication such that future people I write/talk to don't have a similar feeling of a rug-pull. If you can point to specific passages from earlier documents that you feel set you up for disappointment, I'd be very grateful.

I'm going to respond piece-meal, since I'm currently writing in a limited timebox.

Empowering the principal to fix its flaws and mistakes how? [...]

If the "perfectly corrigible agent" it something that only reflects on itself and tries to empower the principal to fix it, it would be useless at anything else, like curing cancer.

I think obedience is an emergent behavior of corrigibility. The intuitive story here is that how the AI moves its body is a kind of action, and insofar as the principal gives a command, this is an attempt to "fix" the action to be one way as opposed to another. Responding to local, verbal instructions is a way of responding to the corrections of the principal. If the principal is able to tell the agent to fetch the apple, and the agent does so, the principal is empowered over the agent's behavior in a way that that would not be if the agent ignored them.

More formally, I am confused exactly how to specify where the boundaries of power should be, but I show a straightforward way to derive something like obedience from empowerment in doc 3b.

Overall I think you shouldn't get hung up on the empowerment frame when trying to get a deep handle on corrigibility, but should instead try to find a clean sense of the underlying generator and then ask how empowerment matches/diverges from that.

Yep. sim is additionally bad because it prevents the AI from meaningfully defending against manipulation by others. It's worse than that, even, since the AI can't even let the principal use general tools the AI provides (i.e. a fortress) to defend against being manipulated from outside. In the limit, this might result in the AI manipulating the principals on the behalf of others who would've counterfactually influenced them. I consider the version I've provided to be obviously inadequate, and this is another pointer as to why.

Towards the end of the document, when I discuss time, I mention that it probably makes sense to take the P(V|pi_0) counterfactual for just the expected timestep, rather than across a broader swath of time. This helps alleviate some of the weirdness. Consider, for instance, a setup where the AI uses a quantum coin to randomly take no action with a 1/10^30 chance each minute, and otherwise it acts normally. We might model P(V|pi_0) as the machine's model of what the principal's values would be like if it randomly froze due to the quantum coin. Because it's localized in time I expect this is basically just "what the human currently values if the AI isn't taking immediate actions." This version of the AI would certainly be able to help defend the principal from outside manipulation, such as by (on demand) building the principal a secure fortress. Even though in aggregate that principal's values diverge from the counterfactual where the AI always flipped the coin such that it took no action, the principal's values will probably be very similar to a counterfactual where the coin flip caused the machine to freeze for one minute.

Apologies for the feeling of a rug-pull. I do think corrigibility is a path to avoiding to having to have an a-priori understanding of human values, but I admit that the formalism proposed here involves the machine needing to develop at least a rough understanding of human values so that it knows how to avoid (locally) disrupting them. I think these are distinct features, and that corrigibility remains promising in how it sidesteps the need for an a-priori model. I definitely agree that it's disheartening how little progress there's been on this front over the years.

Want to explain a bit about how you'd go about doing this?

I don't think there's a particular trick, here. I can verify a certain amount of wisdom, and have already used that to gain some trust in various people. I'd go to the people I trust and ask them how they'd solve the problem, then try to spot common techniques and look for people who were pointed to independently. I'd attempt to get to know people who were widely seen as trustworthy and understand why they had that reputation and try not to get Goodharted too hard. I'd try to get as much diversity as was reasonable while also still keeping the quality bar high, since diverse consensus is more robust than groupthink consensus. I'd try to select for old people who seem like they've been under intense pressure and thrived without changing deeply as people in the process. I'd try to select for people who were capable of cooperating and changing their minds when confronted by logic. I'd try to select for people who didn't have much vested interest, and seemed to me, in the days I spent with them, to be focused on legacy, principles, and the good of the many.

To be clear, I don't think I could reliably pull this off if people were optimizing for manipulating, deceiving, and pressuring me. :shrug:

I think this means you should be extra careful not to inadvertently make people too optimistic about alignment, which would make coordination to stop capabilities research even harder than it already is. For example you said that you "like" the visualization of 5 humans selected by various governments, without mentioning that you don't trust governments to do this, which seems like a mistake?

I agree that false hope is a risk. In these documents I've tried to emphasize that I don't think this path is easy. I feel torn between people like you and Eliezer who take my tone as being overly hopeful and the various non-doomers who I've talked to about this work who see me as overly doomy. Suggestions welcome.

I said I like the visualization because I do! I think I'd feel very happy if the governments of the world selected 5 people on the basis of wisdom and sanity to be the governors of AGI and the stewards of the future. Similarly, I like the thought of an AGI laboratory doing a slow and careful training process even when all signs point to the thing being safe. I don't trust governments to actually select stewards of the future just as I don't expect frontier labs to go slow and be sufficiently careful. But having strong conceptualizations of what success might look like is integral, I think, to actually succeeding.

1) I'm pretty bearish on standard value uncertainty for standard MIRI reasons. I think a correct formulation of corrigibility will say that even if you (the agent) knows what the principal wants, deep in their heart, you should not optimize for it unless they direct you to do so. I explore this formally in 3b, when I talk about the distinction between sampling counterfactual values from the actual belief state over values ("P") vs a simplicity-weighted distribution ("Q"). I do think that value "uncertainty" is important in the sense that it's important for the agent to not be anchoring too heavily on any particular object-level optimization target. (I could write more words, but I suspect reading the next posts in my sequence would be a good first step if you want more of my perspective.)

2) I think reversibility is probably best seen as an emergent desideratum from corrigibility rather than vice versa. There are plenty of instances where the corrigible thing to do is to take an irreversible action, as can be seen in many of the stories, above.

You're welcome! I'm glad you're enjoying it. ^_^

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