This is the conclusion of the Embedded Agency
series. Previous posts:
Embedded Agents — Decision Theory — Embedded World-Models
Robust Delegation — Subsystem Alignment
A final word on curiosity, and intellectual puzzles:
I described an embedded agent, Emmy, and said that I don’t understand how she evaluates her options, models the world, models herself, or decomposes and solves problems.
In the past, when researchers have talked about motivations for working on problems like these, they’ve generally focused on the motivation from AI risk. AI researchers want to build machines that can solve problems in the general-purpose fashion of a human, and dualism is not a realistic framework for thinking about such systems. In particular, it’s an approximation that’s especially prone to breaking down as AI systems get smarter. When people figure out how to build general AI systems, we want those researchers to be in a better position to understand their systems, analyze their internal properties, and be confident in their future behavior.
This is the motivation for most researchers today who are working on things like updateless decision theory and subsystem alignment. We care about basic conceptual puzzles which we think we need to figure out in order to achieve confidence in future AI systems, and not have to rely quite so much on brute-force search or trial and error.
But the arguments for why we may or may not need particular conceptual insights in AI are pretty long. I haven’t tried to wade into the details of that debate here. Instead, I’ve been discussing a particular set of research directions as an intellectual puzzle, and not as an instrumental strategy.
One downside of discussing these problems as instrumental strategies is that it can lead to some misunderstandings about why we think this kind of work is so important. With the “instrumental strategies” lens, it’s tempting to draw a direct line from a given research problem to a given safety concern. But it’s not that I’m imagining real-world embedded systems being “too Bayesian” and this somehow causing problems, if we don’t figure out what’s wrong with current models of rational agency. It’s certainly not that I’m imagining future AI systems being written in second-order logic! In most cases, I’m not trying at all to draw direct lines between research problems and specific AI failure modes.
What I’m instead thinking about is this: We sure do seem to be working with the wrong basic concepts today when we try to think about what agency is, as seen by the fact that these concepts don’t transfer well to the more realistic embedded framework.
If AI developers in the future are still working with these confused and incomplete basic concepts as they try to actually build powerful real-world optimizers, that seems like a bad position to be in. And it seems like the research community is unlikely to figure most of this out by default in the course of just trying to develop more capable systems. Evolution certainly figured out how to build human brains without “understanding” any of this, via brute-force search.
Embedded agency is my way of trying to point at what I think is a very important and central place where I feel confused, and where I think future researchers risk running into confusions too.
There’s also a lot of excellent AI alignment research that’s being done with an eye toward more direct applications; but I think of that safety research as having a different type signature than the puzzles I’ve talked about here.
Intellectual curiosity isn’t the ultimate reason we privilege these research directions. But there are some practical advantages to orienting toward research questions from a place of curiosity at times, as opposed to only applying the “practical impact” lens to how we think about the world.
When we apply the curiosity lens to the world, we orient toward the sources of confusion preventing us from seeing clearly; the blank spots in our map, the flaws in our lens. It encourages re-checking assumptions and attending to blind spots, which is helpful as a psychological counterpoint to our “instrumental strategy” lens—the latter being more vulnerable to the urge to lean on whatever shaky premises we have on hand so we can get to more solidity and closure in our early thinking.
Embedded agency is an organizing theme behind most, if not all, of our big curiosities. It seems like a central mystery underlying many concrete difficulties.