Alex_Altair

Karma: 3,974

Alex_Altair 2 May 2024 17:23 UTC
2 points
1
on: Manifund Q1 Retro: Learnings from impact certs
[link to about]
Link missing

Alex_Altair 30 Apr 2024 17:16 UTC
4 points
0
in reply to: Jonas Hallgren’s comment on: Towards a formalization of the agent structure problem
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

Alex_Altair29 Apr 2024 20:28 UTC

48 points

2 comments14 min readLW link

Alex_Altair 25 Apr 2024 3:42 UTC
6 points
4
on: Forget Everything (Statistical Mechanics Part 1)
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!

Alex_Altair 19 Apr 2024 15:09 UTC
2 points
0
in reply to: jacobjacob’s comment on: Express interest in an “FHI of the West”
Maybe it could be FLCI to avoid collision with the existing FLI.

Alex_Altair 18 Apr 2024 14:25 UTC
12 points
3
in reply to: jacobjacob’s comment on: Express interest in an “FHI of the West”
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.)

Alex_Altair 28 Mar 2024 0:56 UTC
2 points
0
on: LessOnline (May 31—June 2, Berkeley, CA)
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?

Alex_Altair 25 Mar 2024 2:41 UTC
4 points
2
in reply to: M. Y. Zuo’s comment on: Natural Latents: The Concepts
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.

Alex_Altair 25 Mar 2024 2:28 UTC
4 points
0
on: Natural Latents: The Concepts
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.

Alex_Altair 25 Mar 2024 1:31 UTC
6 points
0
on: Natural Latents: The Concepts
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;
1. 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
2. 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.

Alex_Altair 5 Mar 2024 20:05 UTC
5 points
0
on: Alex_Altair’s Shortform
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.

Alex_Altair 28 Feb 2024 17:48 UTC
7 points
0
on: Voting Results for the 2022 Review
Just noticing that every post has at least one negative vote, which feels interesting for some reason.

Alex_Altair 23 Feb 2024 1:49 UTC
2 points
0
on: Dual Wielding Kindle Scribes
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).

Alex_Altair 20 Feb 2024 16:33 UTC
3 points
in reply to: Thomas Kwa’s comment on: Fixing The Good Regulator Theorem
Here’s the arxiv version of the paper, with a bunch more content in appendices.

Alex_Altair 14 Feb 2024 14:57 UTC
4 points
4
on: Where is the Town Square?

And, since I can’t do everything: what popular platforms shouldn’t I prioritize?

I think cross-posting between twitter, mastodon and bluesky would be pretty easy. And it would let you gather your own data on which platforms are worth continuing.

Alex_Altair 8 Feb 2024 5:58 UTC
6 points
0
on: Choosing a book on causality
I looked at these several months ago and unfortunately recommend neither. Pearl’s Causality is very dense, and not really a good introduction. The Primer is really egregiously riddled with errors; there seems to have been some problem with the publisher. And on top of that, I just found it not very well written.
I don’t have a specific recommendation, but I believe that at this point there are a bunch of statistics textbooks that competently discuss the essential content of causal modelling; maybe check the reviews for some of those on amazon.

Alex_Altair 14 Jan 2024 2:03 UTC
11 points
in reply to: Garrett Baker’s comment on: D0TheMath’s Shortform
One way that the analogy with code doesn’t carry over is that in math, you often can’t even being to use a theorem if you don’t know a lot of detail about what the objects in the theorem mean, and often knowing what they mean is pretty close to knowing why the theorem’s you’re building on are true. Being handed a theorem is less like being handed an API and more like being handed a sentence in a foreign language. I can’t begin to make use of the information content in the sentence until I learn what every symbol means and how the grammar works, and at that point I could have written the sentence myself.

Alex_Altair 11 Jan 2024 21:30 UTC
4 points
0
in reply to: Yuxi_Liu’s comment on: The Perceptron Controversy
I’d recommend porting it over as a sequence instead of one big post (or maybe just port the first chunk as an intro post?). LW doesn’t have a citation format, but you can use footnotes for it (and you can use the same footnote number in multiple places).

Alex_Altair 10 Jan 2024 22:23 UTC
10 points
8
on: A model of research skill
I had a side project to get better at research in 2023. I found very little resources that were actually helpful to me. But here are some that I liked.
- A few posts by Holden Karnofsky on Cold Takes, especially Useful Vices for Wicked Problems and Learning By Writing.
- Diving into deliberate practice. Most easily read is the popsci book Peak. This book emphasizes “mental representations”, which I find the most useful part of the method, though I think it’s also the least supported by the science.
- The popsci book Grit.
- The book Ultralearning. Extremely skimmable, large collection of heuristics that I find essential for the “lean” style of research.
- Reading a scattering of historical accounts of how researchers did their research, and how it came to be useful. (E.g. Newton, Einstein, Erdős, Shannon, Kolmogorov, and a long tail of less big names.)
(Many resources were not helpful for me for reasons that might not apply to others; I was already doing what they advised, or they were about how to succeed inside academia, or they were about emotional problems like lack of confidence or burnout. But, I think mostly I failed to find good resources because no one knows how to do good research.)
What links here?
- Raemon's comment on My research agenda in agent foundations by Alex_Altair (25 Jan 2024 2:07 UTC; 6 points)

Alex_Altair 2 Jan 2024 22:47 UTC
2 points
0
on: New report: “Scheming AIs: Will AIs fake alignment during training in order to get power?”
Finally, I want to note an aspect of the discussion in the report that makes me quite uncomfortable: namely, it seems plausible to me that in addition to potentially posing existential risks to humanity, the sorts of AIs discussed in the report might well be moral patients in their own right.
I strongly appreciate this paragraph for stating this concern so emphatically. I think this possibility is strongly under-represented in the AI safety discussion as a whole.

Alex_Altair

Towards a for­mal­iza­tion of the agent struc­ture problem

Towards a formalization of the agent structure problem