In this post, I proclaim/endorse forum participation (aka commenting) as a productive research strategy that I've managed to stumble upon, and recommend it to others (at least to try). Note that this is different from saying that forum/blog posts are a good way for a research community to communicate. It's about individually doing better as researchers.

yanni1d3453
3
I like the fact that despite not being (relatively) young when they died, the LW banner states that Kahneman & Vinge have died "FAR TOO YOUNG", pointing to the fact that death is always bad and/or it is bad when people die when they were still making positive contributions to the world (Kahneman published "Noise" in 2021!).
A strange effect: I'm using a GPU in Russia right now, which doesn't have access to copilot, and so when I'm on vscode I sometimes pause expecting copilot to write stuff for me, and then when it doesn't I feel a brief amount of the same kind of sadness I feel when a close friend is far away & I miss them.
Dictionary/SAE learning on model activations is bad as anomaly detection because you need to train the dictionary on a dataset, which means you needed the anomaly to be in the training set. How to do dictionary learning without a dataset? One possibility is to use uncertainty-estimation-like techniques to detect when the model "thinks its on-distribution" for randomly sampled activations.
Novel Science is Inherently Illegible Legibility, transparency, and open science are generally considered positive attributes, while opacity, elitism, and obscurantism are viewed as negative. However, increased legibility in science is not always beneficial and can often be detrimental. Scientific management, with some exceptions, likely underperforms compared to simpler heuristics such as giving money to smart people or implementing grant lotteries. Scientific legibility suffers from the classic "Seeing like a State" problems. It constrains endeavors to the least informed stakeholder, hinders exploration, inevitably biases research to be simple and myopic, and exposes researchers to constant political tug-of-war between different interest groups poisoning objectivity.  I think the above would be considered relatively uncontroversial in EA circles.  But I posit there is something deeper going on:  Novel research is inherently illegible. If it were legible, someone else would have already pursued it. As science advances her concepts become increasingly counterintuitive and further from common sense. Most of the legible low-hanging fruit has already been picked, and novel research requires venturing higher into the tree, pursuing illegible paths with indirect and hard-to-foresee impacts.
habryka5d5120
10
A thing that I've been thinking about for a while has been to somehow make LessWrong into something that could give rise to more personal-wikis and wiki-like content. Gwern's writing has a very different structure and quality to it than the posts on LW, with the key components being that they get updated regularly and serve as more stable references for some concept, as opposed to a post which is usually anchored in a specific point in time.  We have a pretty good wiki system for our tags, but never really allowed people to just make their personal wiki pages, mostly because there isn't really any place to find them. We could list the wiki pages you created on your profile, but that doesn't really seem like it would allocate attention to them successfully. I was thinking about this more recently as Arbital is going through another round of slowly rotting away (its search currently being broken and this being very hard to fix due to annoying Google Apps Engine restrictions) and thinking about importing all the Arbital content into LessWrong. That might be a natural time to do a final push to enable people to write more wiki-like content on the site.

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7Charlie Steiner16h
Dictionary/SAE learning on model activations is bad as anomaly detection because you need to train the dictionary on a dataset, which means you needed the anomaly to be in the training set. How to do dictionary learning without a dataset? One possibility is to use uncertainty-estimation-like techniques to detect when the model "thinks its on-distribution" for randomly sampled activations.

You may be able to notice data points where the SAE performs unusually badly at reconstruction? (Which is what you'd see if there's a crucial missing feature)

4Erik Jenner14h
I think this is an important point, but IMO there are at least two types of candidates for using SAEs for anomaly detection (in addition to techniques that make sense for normal, non-sparse autoencoders): 1. Sometimes, you may have a bunch of "untrusted" data, some of which contains anomalies. You just don't know which data points have anomalies on this untrusted data. (In addition, you have some "trusted" data that is guaranteed not to have anomalies.) Then you could train an SAE on all data (including untrusted) and figure out what "normal" SAE features look like based on the trusted data. 2. Even for an SAE that's been trained only on normal data, it seems plausible that some correlations between features would be different for anomalous data, and that this might work better than looking for correlations in the dense basis. As an extreme version of this, you could look for circuits in the SAE basis and use those for anomaly detection. Overall, I think that if SAEs end up being very useful for mech interp, there's a decent chance they'll also be useful for (mechanistic) anomaly detection (a lot of my uncertainty about SAEs applies to both possible applications). Definitely uncertain though, e.g. I could imagine SAEs that are useful for discovering interesting stuff about a network manually, but whose features aren't the right computational units for actually detecting anomalies. I think that would make SAEs less than maximally useful for mech interp too, but probably non-zero useful.
4Charlie Steiner10h
Yeah, this seems somewhat plausible. If automated circuit-finding works it would certainly detect some anomalies, though I'm uncertain if it's going to be weak against adversarial anomalies relative to regular ol' random anomalies.

What banner?

3yanni12h
I have heard rumours that an AI Safety documentary is being made. Separate to this, a good friend of mine is also seriously considering making one, but he isn't "in" AI Safety. If you know who this first group is and can put me in touch with them, it might be worth getting across each others plans.
1Neil 12h
This reminds me of when Charlie Munger died at 99, and many said of him "he was just a child". Less of a nod to transhumanist aspirations, and more to how he retained his sparkling energy and curiosity up until death. There are quite a few good reasons to write "dead far too young". 

Cross-posted on the EA Forum. This article is the fourth in a series of ~10 posts comprising a 2024 State of the AI Regulatory Landscape Review, conducted by the Governance Recommendations Research Program at Convergence Analysis. Each post will cover a specific domain of AI governance (e.g. incident reportingsafety evals, model registries, etc.). We’ll provide an overview of existing regulations, focusing on the US, EU, and China as the leading governmental bodies currently developing AI legislation. Additionally, we’ll discuss the relevant context behind each domain and conduct a short analysis.

This series is intended to be a primer for policymakers, researchers, and individuals seeking to develop a high-level overview of the current AI governance space. We’ll publish individual posts on our website and release a comprehensive report at the end of this...

[This is part of a series I’m writing on how to convince a person that AI risk is worth paying attention to.] 

tl;dr: People’s default reaction to politics is not taking them seriously. They could center their entire personality on their political beliefs, and still not take them seriously. To get them to take you seriously, the quickest way is to make your words as unpolitical-seeming as possible. 

I’m a high school student in France. Politics in France are interesting because they’re in a confusing superposition. One second, you'll have bourgeois intellectuals sipping red wine from their Paris apartment writing essays with dubious sexual innuendos on the deep-running dynamics of power. The next, 400 farmers will vaguely agree with the sentiment and dump 20 tons of horse manure in downtown...

5Shankar Sivarajan8h
Yes, every four years, if the good guys don't win the next (US) presidential election. Or if people don't switch to/away from nuclear power. Or they're killed by immigrants/cops. Or they die of a fentanyl overdose. Or in a school shooting. Or if the Iraqis/Russians/Chinese invade. Or if taxes are lowered/raised. Perhaps telling people they or their children are going to die imminently isn't a standard tactic of "mere politics" where you are; you did say you're not American.

Concept creep is a bastard. >:(

6Neil 12h
More French stories: So, at some point, the French decided what kind of political climate they wanted. What actions would reflect on their cause well? Dumping manure onto the city center using tractors? Sure! Lining up a hundred stationary taxi cabs in every main artery of the city? You bet! What about burning down the city hall's door, which is a work of art older than the United States? Mais évidemment! "Politics" evokes all that in the mind of your average Frenchman. No, not sensible strategies that get your goals done, but the first shiny thing the protesters thought about. It'd be more entertaining to me, except for the fact that I had to skip class at some point because I accidentally biked headfirst into a burgeoning cloud of tear gas (which the cops had detonated in an attempt to ward off the tractors). There are flagpoles in front of the government building those tractors dumped the manure on. They weren't entirely clean, and you can still see the manure level, about 10 meters high. 

An entry-level characterization of some types of guy in decision theory, and in real life, interspersed with short stories about them

A concave function bends down. A convex function bends up. A linear function does neither.

A utility function is just a function that says how good different outcomes are. They describe an agent's preferences. Different agents have different utility functions.

Usually, a utility function assigns scores to outcomes or histories, but in article we'll define a sort of utility function that takes the quantity of resources that the agent has control over, and the utility function says how good an outcome the agent could attain using that quantity of resources.

In that sense, a concave agent values resources less the more that it has, eventually barely wanting more resources at...

5Donald Hobson9h
The convex agent can be traded with a bit more than you think.  A 1 in 10^50 chance of us standing back  and giving it free reign of the universe is better than us going down fighting and destroying 1kg as we do. The concave agents are less cooperative than you think, maybe. I suspect that to some AI's, killing all humans now is more reliable than letting them live.  If the humans are left alive, who knows what they might do. They might make the vacuum bomb. Whereas the AI can Very reliably kill them now. 
2mako yass8h
Alternate phrasing, "Oh, you could steal the townhouse at a 1/8billion probability? How about we make a deal instead. If the rng rolls a number lower than 1/7billion, I give you the townhouse, otherwise, you deactivate and give us back the world." The convex agent finds that to be a much better deal, accepts, then deactivates. I guess perhaps it was the holdout who was being unreasonable, in the previous telling.

Or the sides can't make that deal because one side or both wouldn't hold up their end of the bargain. Or they would, but they can't prove it. Once the coin lands, the losing side has no reason to follow it other than TDT. And TDT only works if the other side can reliably predict their actions.

Summary: The post describes a method that allows us to use an untrustworthy optimizer to find satisficing outputs.

Acknowledgements: Thanks to Benjamin Kolb (@benjaminko), Jobst Heitzig (@Jobst Heitzig) and Thomas Kehrenberg (@Thomas Kehrenberg)  for many helpful comments.

Introduction

Imagine you have black-box access to a powerful but untrustworthy optimizing system, the Oracle. What do I mean by "powerful but untrustworthy"? I mean that, when you give an objective function  as input to the Oracle, it will output an element  that has an impressively low[1] value of . But sadly, you don't have any guarantee that it will output the optimal element and e.g. not one that's also chosen for a different purpose (which might be dangerous for many reasons, e.g. instrumental convergence).

What questions can you safely ask the Oracle? Can you use it to...

3Donald Hobson11h
I think that, if you are wanting a formally verified proof of some maths theorem out of the oracle, then this is getting towards actually likely to not kill you.  You can start with m huge, and slowly turn it down, so you get a long list of "no results", followed by a proof. (Where the optimizer only had a couple of bits of free optimization in choosing which proof.)  Depending on exactly how chaos theory and quantum randomness work, even 1 bit of malicious super optimization could substantially increase the chance of doom.  And of course, side channel attacks. Hacking out of the computer. And, producing formal proofs isn't pivotal. 
1Simon Fischer2h
Yes, I believe that's within reach using this technique. This is quite dangerous though if the Oracle is deceptively withholding answers; I commented on this in the last paragraph of this section.

If the oracle is deceptively withholding answers, give up on using it. I had taken the description to imply that the oracle wasn't doing that. 

2EGI11h
"...under the assumption that the subset of dangerous satisficing outputs D is much smaller than the set of all satisficing outputs S, and that we are able to choose a number m such that |D|≪m<|S|." I highly doubt that  D≪S is true for anything close to a pivotal act since most pivotal acts at some point involve deploying technology that can trivially take over the world. For anything less ambitious the proposed technique looks very useful. Strict cyber- and physical security will of course be necessary to prevent the scenario Gwern mentions.
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There's a particular kind of widespread human behavior that is kind on the surface, but upon closer inspection reveals quite the opposite. This post is about four such patterns.

 

Computational Kindness

One of the most useful ideas I got out of Algorithms to Live By is that of computational kindness. I was quite surprised to only find a single mention of the term on lesswrong. So now there's two.

Computational kindness is the antidote to a common situation: imagine a friend from a different country is visiting and will stay with you for a while. You're exchanging some text messages beforehand in order to figure out how to spend your time together. You want to show your friend the city, and you want to be very accommodating and make sure...

Just came to my mind that these are things I tend to think of under the heading "considerateness" rather than kindness

Guess I'd agree. Maybe I was anchored a bit here by the existing term of computational kindness. :)

2Mary Chernyshenko4h
What you say doesn't matter as much as what the other person hears. If I were the other person, I would probably wonder why you would add epicycles, and kindness would be just one possible explanation.
1silentbob2h
Fair point. Maybe if I knew you personally I would take you to be the kind of person that doesn't need such careful communication, and hence I would not act in that way. But even besides that, one could make the point that your wondering about my communication style is still a better outcome than somebody else being put into an uncomfortable situation against their will. I should also note I generally have less confidence in my proposed mitigation strategies than in the phenomena themselves. 
Alexander Gietelink Oldenziel

Can you post the superforecaster report that has the 0.12% P(Doom) number. I have not actually read anything of course and might be talking out of my behind.

In any case, there have been several cases where OpenPhil or somebody or other has brought in 'experts' of various ilk to debate the P(Doom), probability of existential risk. [usually in the context of AI]

Many of these experts give very low percentages. One percentage I remember was 0.12 %

In the latest case these were Superforecasters, Tetlock's anointed. Having 'skin in the game' they outperformed the fakexperts in various prediction markets. 

So we should defer to them (partially) on the big questions of x-risk also. Since they give very low percentages that is good. So the argument goes.

Alex thinks these

...

As of two years ago, the evidence for this was sparse. Looked like parity overall, though the pool of "supers" has improved over the last decade as more people got sampled.

There are other reasons to be down on XPT in particular.

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