Yes, thanks for citing it here! I should have mentioned it, really.

I see the Skyrms iterative idea as quite different from the "just take a fixed point" theory I sketch here, although clearly they have something in common. FixDT makes it easier to combine both epistemic and instrumental concerns -- every fixed point obeys the epistemic requirement; and then the choice between them obeys the instrumental requirement. If we iteratively zoom in on a fixed point instead of selecting from the set, this seems harder?

If we try the Skyrms iteration thing, maybe the most sensible thing would be to move toward the beliefs of greatest expected utility -- but do so in a setting where epistemic utility emerges naturally from pragmatic concerts (such as A Pragmatists Guide to Epistemic Decision Theory by Ben Levinstein). So the agent is only ever revising its beliefs in pragmatic ways, but we assume enough about the environment that it wants to obey both the epistemic and instrumental constraints? But, possibly, this assumption would just be inconsistent with the sort of decision problem which motivates FixDT (and Greaves).

I find your attempted clarification confusing. 

Our model is going to have some variables in it, and if we don't know in advance where the agent will be at each timestep, then presumably we don't know which of those variables (or which function of those variables, etc) will be our Markov blanket. 

No? A probabilistic model can just be a probability distribution over events, with no "random variables in it". It seemed like your suggestion was to define the random variables later, "on top of" the probabilistic model, not as an intrinsic part of the model, so as to avoid the objection that a physics-ish model won't have agent-ish variables in it.

So the random variables for our markov blanket can just be defined as things like skin surface temperature & surface lighting & so on; random variables which can be derived from a physics-ish event space, but not by any particularly simple means (since the location of these things keeps changing).

On the other hand, if we knew which variables or which function of the variables were the blanket, then presumably we'd already know where the agent is, so presumably we're already conditioning on something when we say "the agent's boundary is a Markov blanket".

Again, no? If I know skin surface temperature and lighting conditions and so on all add up to a Markov blanket, I don't thereby know where the skin is.

I think that is a basically-correct argument. It doesn't actually argue that agent boundaries aren't Markov boundaries; I still think agent boundaries are basically Markov boundaries. But the argument implies that the most naive setup is missing some piece having to do with "where the agent is".

It seems like you agree with Sam way more than would naively be suggested by your initial reply. I don't understand why. 

When I talked with Sam about this recently, he was somewhat satisfied by your reply, but he did think there were a bunch of questions which follow. By giving up on the idea that the markov blanket can be "built up" from an underlying causal model, we potentially give up on a lot of niceness desiderata which we might have wanted. So there's a natural question of how much you want to try and recover, which you could have gotten from "structural" markov blankets, and might be able to get some other way, but don't automatically get from arbitrary markov blankets.

In particular, if I had to guess: causal properties? I don't know about you, but my OP was mainly directed at Critch, and iiuc Critch wants the Markov blanket to have some causal properties so that we can talk about input/output. I also find it appealing for "agent boundaries" to have some property like that. But if the random variables are unrelated to a causal graph (which, again, is how I understood your proposal) then it seems difficult to recover anything like that.

Okay, so you know how AI today isn't great at certain... let's say "long-horizon" tasks? Like novel large-scale engineering projects, or writing a long book series with lots of foreshadowing?

(Modulo the fact that it can play chess pretty well, which is longer-horizon than some things; this distinction is quantitative rather than qualitative and it’s being eroded, etc.)

And you know how the AI doesn't seem to have all that much "want"- or "desire"-like behavior?

(Modulo, e.g., the fact that it can play chess pretty well, which indicates a certain type of want-like behavior in the behaviorist sense. An AI's ability to win no matter how you move is the same as its ability to reliably steer the game-board into states where you're check-mated, as though it had an internal check-mating “goal” it were trying to achieve. This is again a quantitative gap that’s being eroded.)

I don't think the following is all that relevant to the point you are making in this post, but someone cited this post of yours in relation to the question of whether LLMs are "intelligent" (summarizing the post as "Nate says LLMs aren't intelligent") and then argued against the post as goalpost-moving, so I wanted to discuss that.

It may come as a shock to some, that Abram Demski adamantly defends the following position: GPT4 is AGI. I would be goalpost-moving if I said otherwise. I think the AGI community is goalpost-moving to the extent that it says otherwise. 

I think there is some tendency in the AI Risk community to equate "AGI" with "the sort of AI which kills all the humans unless it is aligned". But "AGI" stands for "artificial general intelligence", not "kills all the humans". I think it makes more sense for the definition of AGI to be up to the community of AI researchers who use the term AGI to distance their work from narrow AI, rather than for it to be up to the AI risk community. And GPT4 is definitely not narrow AI.

I'll argue an even stronger claim: if you come up with a task which can be described and completed entirely in text format (and then evaluated somehow for performance quality), for most such tasks the performance of GPT4 is at or above the performance of a random human. (We can even be nice and only randomly sample humans who speak whichever languages are appropriate to the task; I'll still stand by the claim.) Yes, GPT4 has some weaknesses compared to a random human. But most claims of weaknesses I've heard are in fact contrasting GPT4 to expert humans, not random humans. So my stronger claim is: GPT4 is human-level AGI, maybe not by all possible definitions of the term, but by a very reasonable-seeming definition which 2014 Abram Demski might have been perfectly happy with. To deny this would be goalpost-moving for me; and, I expect, for many.

So (and I don't think this is what you were saying) if GPT4 were being ruled out of "human-level AGI" because it cannot write a coherent set of novels on its own, or do a big engineering project, well, I call shenanigans. Most humans can't do that either.

Ahhh that makes sense, thanks.