Scott Garrabrant


Fixed Points


Humans Are Embedded Agents Too

We actually avoided talking about AI in most of the cartoon, and tried to just imply it by having a picture of a robot.

The first time (I think) I presented the factoring in the embedded agency sequence was at a MIRI CFAR collaboration workshop, so parallels with humans was live in my thinking.

The first time we presented the cartoon in roughly its current form was at MSFP 2018, where we purposely did it on the first night before a CFAR workshop, so people could draw analogies that might help them transfer their curiosity in both directions.

Why Subagents?

Not sure if you've seen it, but this paper by Critch and Russell might be relevant when you start thinking about uncertainty.

AI Alignment Writing Day Roundup #1

This is my favorite comment. Thank you.

Does Agent-like Behavior Imply Agent-like Architecture?

I think I do want to make my agent-like architecture general enough to include evolution. However, there might be a spectrum of agent-like-ness such that you can't get much more than Sphex behavior with just evolution (without having a mesa-optimizer in there)

I think you can guarantee that, probabilistically, getting a specific outcome requires information about that outcome (no free lunch), which implies "search" on a "world model."

Yeah, but do you think you can make it feel more like a formal proof?

How the MtG Color Wheel Explains AI Safety

I think informed oversight fits better with MtG white than it does with boxing. I agree that the three main examples are boxing like, and informed oversight is not, but it still feels white to me.

I do think that corrigibility done right is a thing that is in some sense less agentic. I think that things that have goals outside of them are less agentic than things that have their goals inside of them, but I think corrigibility is stronger than that. I want to say something like a corrigible agent not only has its goals partially on the outside (in the human), but also partially has its decision theory on the outside. Idk.

Diagonalization Fixed Point Exercises

Yeah, it is just functions that take in two sentences and put both their Godel numbers into a fixed formula (with 2 inputs).

Iteration Fixed Point Exercises

Thanks, I actually wanted to get rid of the earlier condition that for all , and I did that.

Embedded Agents

This is not a complete answer, but it is part of my picture:

(It is the part of the picture that I can give while being only descriptive, and not prescriptive. For epistemic hygiene reasons, I want avoid discussions of how much of different approaches we need in contexts (like this one) that would make me feel like I was justifying my research in a way that people might interpret as an official statement from the agent foundations team lead.)

I think that Embedded Agency is basically a refactoring of Agent Foundations in a way that gives one central curiosity based goalpost, rather than making it look like a bunch of independent problems. It is mostly all the same problems, but it was previously packaged as "Here are a bunch of things we wish we understood about aligning AI," and in repackaged as "Here is a central mystery of the universe, and here are a bunch things we don't understand about it." It is not a coincidence that they are the same problems, since they were generated in the first place by people paying close to what mysteries of the universe related to AI we haven't solved yet.

I think of Agent Foundations research has having a different type signature than most other AI Alignment research, in a way that looks kind of like Agent Foundations:other AI alignment::science:engineering. I think of AF as more forward-chaining and other stuff as more backward-chaining. This may seem backwards if you think about AF as reasoning about superintelligent agents, and other research programs as thinking about modern ML systems, but I think it is true. We are trying to build up a mountain of understanding, until we collect enough that the problem seems easier. Others are trying to make direct plans on what we need to do, see what is wrong with those plans, and try to fix the problems. Some consequences of this is that AF work is more likely to be helpful given long timelines, partially because AF is trying to be the start of a long journey of figuring things out, but also because AF is more likely to be robust to huge shifts in the field.

I actually like to draw an analogy with this: (taken from this post by Evan Hubinger)

I was talking with Scott Garrabrant late one night recently and he gave me the following problem: how do you get a fixed number of DFA-based robots to traverse an arbitrary maze (if the robots can locally communicate with each other)? My approach to this problem was to come up with and then try to falsify various possible solutions. I started with a hypothesis, threw it against counterexamples, fixed it to resolve the counterexamples, and iterated. If I could find a hypothesis which I could prove was unfalsifiable, then I'd be done.
When Scott noticed I was using this approach, he remarked on how different it was than what he was used to when doing math. Scott's approach, instead, was to just start proving all of the things he could about the system until he managed to prove that he had a solution. Thus, while I was working backwards by coming up with possible solutions, Scott was working forwards by expanding the scope of what he knew until he found the solution.

(I don't think it quite communicates my approach correctly, but I don't know how to do better.)

A consequence of the type signature of Agent Foundations is that my answer to "What are the other major chunks of the larger problem?" is "That is what I am trying to figure out."

Subsystem Alignment

So if we view an epistemic subsystem as an super intelligent agent who has control over the map and has the goal of make the map match the territory, one extreme failure mode is that it takes a hit to short term accuracy by slightly modifying the map in such a way as to trick the things looking at the map into giving the epistemic subsystem more control. Then, once it has more control, it can use it to manipulate the territory to make the territory more predictable. If your goal is to minimize surprise, you should destroy all the surprising things.

Note that we would not make an epistemic system this way, a more realistic model of the goal of an epistemic system we would build is "make the map match the territory better than any other map in a given class," or even "make the map match the territory better than any small modification to the map." But a large point of the section is that if you search strategies that "make the map match the territory better than any other map in a given class," at small scales, this is the same as "make the map match the territory." So you might find "make the map match the territory" optimizers, and then go wrong in the way above.

I think all this is pretty unrealistic, and I expect you are much more likely to go off in a random direction than something that looks like a specific subsystem the programmers put in gets too much power and optimizes stabile for what the programmers said. We would need to understand a lot more before we would even hit the failure mode of making a system where the epistemic subsystem was agenticly optimizing what it was supposed to be optimizing.

Robust Delegation

Some last minute emphasis:

We kind of open with how agents have to grow and learn and be stable, but talk most of the time about this two agent problem, where there is an initial agent and a successor agent. When thinking about it as the succession problem, it seems like a bit of a stretch as a fundamental part of agency. The first two sections were about how agents have to make decisions and have models, and choosing a successor does not seem like as much of a fundamental part of agency. However, when you think it as an agent has to stably continue to optimize over time, it seems a lot more fundamental.

So, I want to emphasize that when we say there are multiple forms of the problem, like choosing successors or learning/growing over time, the view in which these are different at all is a dualistic view. To an embedded agent, the future self is not privileged, it is just another part of the environment, so there is no difference between making a successor and preserving your own goals.

It feels very different to humans. This is because it is much easier for us to change ourselves over time that it is to make a clone of ourselves and change the clone, but that difference is not fundamental.

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