Note that the NYT article is by Cade Metz.
Alex_Altair
Karma: 4,067
I think the biggest thing I like about it is that it exists! Someone tried to make a fully formalized agent model, and it worked. As mentioned above it’s got some big problems, but it helps enormously to have some ground to stand on to try to build on further.
I love this idea!
Some other books this could work for:
The Ancestor’s Tale
The Art of Game Design
The Anthropocene Reviewed
The LessWrong review books 😉
Many textbooks have a few initial “core” chapters, and then otherwise a bunch of independent chapters on applications or assorted advanced topics.
New intro textbook on AIXI
You can “bookmark” a post, is that equivalent to your desired “read later”?
Welcome kjsisco! One good place to start interacting with others here is on the current open thread.
[link to about]
Link missing
Hm… so anything that measures degree of agent structure should register a policy with a sub-agent as having some agent structure. But yeah, I haven’t thought much about the scenarios where there are multiple agents inside the policy. The agent structure problem is trying to use performance to find a minimum measure of agent structure. So if there was an agent hiding in there that didn’t impact the performance during the measured time interval, then it wouldn’t be detected (although it would detect it “in the limit”).
That said, we’re not actually talking about how to measure degree of agent structure yet. It seems plausible to me that whatever method one uses to do that could be adapted to find multiple agents.
Towards a formalization of the agent structure problem
This will make more sense if you have a basic grasp on quantum mechanics, but if you’re willing to accept “energy comes in discrete units” as a premise then you should be mostly fine.
My current understanding is that QM is not-at-all needed to make sense of stat mech. Instead, the thing where energy is equally likely to be in any of the degrees of freedom just comes from using a measure over your phase space such that the dynamical law of your system preservers that measure!
Maybe it could be FLCI to avoid collision with the existing FLI.
I also think the name is off, but for a different reason. When I hear “the west” with no other context, I assume it means this, which doesn’t make sense here, because the UK and FHI are very solidly part of The West. (I have not heard the “Harvard of the west” phrase and I’m guessing it’s pretty darn obscure, especially to the international audience of LW.)
Feedback on the website: it’s not clear to me what the difference is between LessOnline and the summer camp right after. Is the summer camp only something you go to if you’re also going to Manifest? Is it the same as LessOnline but longer?
Oh, no, I’m saying it’s more like 2^8 afterwards. (Obviously it’s more than that but I think closer to 8 than a million.) I think having functioning vision at all brings it down to, I dunno, 2^10000. I think you would be hard pressed to name 500 attributes of mammals that you need to pay attention to to learn a new species.
We then get around the 2^8000000 problem by having only a relatively very very small set of candidate “things” to which words might be attached.
A major way that we get around this is by having hierarchical abstractions. By the time I’m learning “dog” from 1-5 examples, I’ve already done enormous work in learning about objects, animals, something-like-mammals, heads, eyes, legs, etc. So when you point at five dogs and say “those form a group” I’ve already forged abstractions that handle almost all the information that makes them worth paying attention to, and now I’m just paying attention to a few differences from other mammals, like size, fur color, ear shape, etc.
I’m not sure how the rest of this post relates to this, but it didn’t feel present; maybe it’s one of the umpteenth things you left out for the sake of introductory exposition.
I’ve noticed you using the word “chaos” a few times across your posts. I think you’re using it colloquially to mean something like “rapidly unpredictable”, but it does have a technical meaning that doesn’t always line up with how you use it, so it might be useful to distinguish it from a couple other things. Here’s my current understanding of what some things mean. (All of these definitions and implications depend on a pile of finicky math and tend to have surprising counter-example if you didn’t define things just right, and definitions vary across sources.)
Sensitive to initial conditions. A system is sensitive to initial conditions if two points in its phase space will eventually diverge exponentially (at least) over time. This is one way to say that you’ll rapidly lose information about a system, but it doesn’t have to look chaotic. For example, say you have a system whose phase space is just the real line, and its dynamics over time is just that points get 10x farther from the origin every time step. Then, if you know the value of a point to ten decimal places of precision, after ten time steps you only know one decimal place of precision. (Although there are regions of the real line where you’re still sure it doesn’t reside, for example you’re sure it’s not closer to the origin.)
Ergodic. A system is ergodic if (almost) every point in phase space will trace out a trajectory that gets arbitrarily close to every other point. This means that each point is some kind of chaotically unpredictable, because if it’s been going for a while and you’re not tracking it, you’ll eventually end up with maximum uncertainty about where it is. But this doesn’t imply sensitivity to initial conditions; there are systems that are ergodic, but where any pair of points will stay the same distance from each other. A simple example is where phase space is a circle, and the dynamics are that on each time step, you rotate each point around the circle by an irrational angle.
Chaos. The formal characterization that people assign to this word was an active research topic for decades, but I think it’s mostly settled now. My understanding is that it essentially means this;
Your system has at least one point whose trajectory is ergodic, that is, it will get arbitrarily close to every other point in the phase space
For every natural number n, there is a point in the phase space whose trajectory is periodic with period n. That is, after n time steps (and not before), it will return back exactly where it started. (Further, these periodic points are “dense”, that is, every point in phase space has periodic points arbitrarily close to it).
The reason these two criteria yield (colloquially) chaotic behavior is, I think, reasonably intuitively understandable. Take a random point in its phase space. Assume it isn’t one with a periodic trajectory (which will be true with “probability 1”). Instead it will be ergodic. That means it will eventually get arbitrarily close to all other points. But consider what happens when it gets close to one of the periodic trajectories; it will, at least for a while, act almost as though it has that period, until it drifts sufficiently far away. (This is using an unstated assumption that the dynamics of the systems have a property where nearby points act similarly.) But it will eventually do this for every periodic trajectory. Therefore, there will be times when it’s periodic very briefly, and times when it’s periodic for a long time, et cetera. This makes it pretty unpredictable.
There are also connections between the above. You might have noticed that my example of a system that was sensitive to initial conditions but not ergodic or chaotic relied on having an unbounded phase space, where the two points both shot off to infinity. I think that if you have sensitivity to initial conditions and a bounded phase space, then you generally also have ergodic and chaotic behavior.
Anyway, I think “chaos” is a sexy/popular term to use to describe vaguely unpredictable systems, but almost all of the time you don’t actually need to rely on the full technical criteria of it. I think this could be important for not leading readers into red-herring trails of investigation. For example, all of standard statistical mechanics only needs ergodicity.
Has anyone checked out Nassim Nicholas Taleb’s book Statistical Consequences of Fat Tails? I’m wondering where it lies on the spectrum from textbook to prolonged opinion piece. I’d love to read a textbook about the title.
Just noticing that every post has at least one negative vote, which feels interesting for some reason.
The e-ink tablet market has really diversified recently. I’d recommend that anyone interested look around at the options. My impression is that the Kindle Scribe is one of the least good ones (which doesn’t mean it’s bad).
I really appreciate this comment!
And yeah, that’s why I said only “Note that...”, and not something like “don’t trust this guy”. I think the content of the article is probably true, and maybe it’s Metz who wrote it just because AI is his beat. But I do also hold tiny models that say “maybe he dislikes us” and also something about the “questionable understanding” etc that habryka mentions below. AFAICT I’m not internally seething or anything, I just have a yellow-flag attached to this name.