trentmkelly/LessWrong-43k huggingface dataset:
Stone Age Herbalist's notes on ant warfare and slavery
by trevor — 2024-11-09T02:40:01.128Z
Link: https://x.com/Paracelsus1092/status/1854849456983683425

A fun piece on ants and instrumental convergence:

Each month millions of Argentine ants die along battlefronts that extend for miles around San Diego, where clashes occur with three other colonies in wars that may have been going on since the species arrived in the state a century ago.

Some notes on ant warfare

Many are aware that a world war between Argentine ant supercolonies is currently underway, across multiple continents, and against multiple ant 'nations'.

Ant conflict differs from species to species and from scenario to scenario. Some use sheer numbers in tight phalanx-like organisations to swamp the enemy, which may include ants many times their individual size.

The species of the aggressor matters - when researchers placed a single dead slave-making ant into a slave-host colony for 5 mins, the response was extreme aggression against almost all neighbours for three days, most especially against slave-taking ants.

Treating known neighbours or strangers with more hostility is a strategic choice that ant colonies must make, and these choices often determine how aggressive a colony will be.

Ants that cannot afford huge losses with each battle might opt for ranged weapons such as chemical attacks or dropping stones onto the enemy's heads and nest entrances.

Slave making ants are usually outnumbered when they raid other nests. In order to successfully capture the brood to raise in their own nest they must pacify the defenders, which is often done using pheromones to sow confusion and discord amongst the ranks.

!!!!!!

Certain mathematical models derived from human conflicts can explain the success of swamping the enemy with large numbers of disposable soldiers.

That said, some species prefer stronger individuals, and may even use personal champion style duels whilst fighting. Leaving the battlefield for the night and returning in the daylight has also been documented during large scale conflicts.

I may add some more notes as I read. Some of the papers go into marvellous detail on the tactics and change in fighting styles over many days of warfare.

I hadn't heard about ant slavery, apparently it's a well-studied phenomena.

From the Wikipedia page:

The slave-making ants are specialized to parasitize a single species or a group of related species, and they are often close relatives to their hosts, which is typical for social parasites.

The slave-makers may eithe

...
LLMs Look Increasingly Like General Reasoners
by eggsyntax — 2024-11-08T23:47:28.886Z

Summary

Four months after my post 'LLM Generality is a Timeline Crux', new research on o1-preview should update us significantly toward LLMs being capable of general reasoning, and hence of scaling straight to AGI, and shorten our timeline estimates.

Update

Since writing this post, my continuing interest in this question has led to empirical research on the topic: this post describes both a completed pilot project and the plan for a now-underway larger project, expected to finish in mid-2025.

Summary of previous post

In June of 2024, I wrote a post, 'LLM Generality is a Timeline Crux', in which I argue that

LLMs seem on their face to be improving rapidly at reasoning.
But there are some interesting exceptions where they still fail much more badly than one would expect given the rest of their capabilities, having to do with general reasoning. Some argue based on these exceptions that much of their apparent reasoning capability is much shallower than it appears, and that we're being fooled by having trouble internalizing just how vast their training data is.
If in fact this is the case, we should be much more skeptical of the sort of scale-straight-to-AGI argument made by authors like Leopold Aschenbrenner and the short timeline that implies, because substantial additional breakthroughs will be needed first.

Reasons to update

In the original post, I gave the three main pieces of evidence against LLMs doing general reasoning that I found most compelling: blocksworld, planning/scheduling, and ARC-AGI (see original for details). All three of those seem importantly weakened in light of recent research.

Most dramatically, a new paper on blocksworld has recently been published by some of the same highly LLM-skeptical researchers (Valmeekam et al, led by Subbarao Kambhampati[1]: 'LLMs Still Can’t Plan; Can LRMs? A Preliminary Evaluation of OpenAI’s o1 on Planbench'. Where the best previous success rate on non-obfuscated blocksworld was 57.6%, o1-preview essentially saturates the benchmark with 97.8%. On obfuscated blocksworld, where previous LLMs had proved almost entirely incapable (0.8% zero-shot, 4.3% one-shot), o1-preview jumps all the way to a 52.8% success rate. In my view, this jump in particular should update us significantly toward the LLM architecture being capable of general reasoning[2].

o1-preview also does much better on ARC-AGI than gpt-4o, jumping from 9% t...
overengineered air filter shelving
by bhauth — 2024-11-08T22:04:39.987Z
Link: https://bhauth.com/blog/industrial%20design/filter%20shelving.html

Let's consider air purifier design a bit.

preface

This post is about a potential new category of air purifier, but existing air purifiers are already worth using. You should probably have at least one at your home. For larger rooms, the Levoit Vital 200S-P is a reasonable option; that's an Amazon affiliate link. See also: /r/AirPurifiers and review sites such as HouseFresh.

Someone once told me that they didn't want an air purifier because it would make their incense and scented candles ineffective. I'm afraid I can't help with that issue.

design goals

What do we want from an air purifier?

low noise

We want a quiet and efficient fan. For that:

Fan blades should be good airfoils, not whatever's cheapest to injection-mold.

Fan blades should be in a duct, with low clearance between the blades and duct walls.

A single big fan is better than many small fans.

We want low pressure through the filters, so a large filter area.

Bearings should be quiet. Steel ball bearings wear out and start making noise.

We also want a quiet motor:

The power supply shouldn't hum or buzz. (Use toroidal inductors, etc.)

It should use quiet gears or (preferably) a direct drive.

You might think that fans meeting those criteria should be easy to find. People buy a lot of fans, and surely some people want quiet ones that aren't too expensive, right? But no. There are lots of floor fans, but compared to computer fans they're almost all poorly-designed, even the expensive ones. Maybe people actually want some noise so it feels like the fan is working?

filters

We want standardized filters that are easy to replace. They should be either cylindrical or (preferably) rectangular.

To get a long time between replacement and low pressure, we want a large filter area.

If we're using carbon filters, granular carbon is more effective than carbon foam.

Washable fabric pre-filters seem worthwhile.

overall configuration

Some goals for the overall configuration:

Particles tend to go down towards the floor, so we prefer an intake near the floor and exhaust that goes upwards.

We don't want to repeatedly filter the same air, so we want to avoid exhaust recirculating to the intake.

For these reasons, air purifiers are often circular or box-shaped, sit on the floor, and have upward exhaust. In some cases, people want an exhaust that points directly at them, from the side.

We want the air purifier ...
Bigger Livers?
by sarahconstantin — 2024-11-08T21:50:09.814Z

My husband, Andrew Rettek, has a blog you should read. As he’s gotten into fitness, he’s started following exercise science, which is the (very new!) field of running small controlled experiments on diet and exercise on athletes who do exactly what you tell them to, under strict observation.

This is in contrast to fields like nutrition science, which study the effect of the “intervention” of telling people (usually non-athletes) what diets and exercise programs to follow. Exercise science tends to have much smaller sample sizes, but it can come to more unambiguous conclusions because it’s testing the intervention itself, not people’s ability or willingness to follow it. Contra the “nobody knows anything about diet or exercise” conventional wisdom, we do know some things! It’s just…not very many things. And only about college athletes.

One surprising thing Andrew noticed is that organ size, especially liver size, has a lot to do with overall metabolism.

Larger Athletes’ Higher RMR is Partly From Bigger Livers

Athletes have higher resting metabolic rates than non-athletes; their bodies use more energy, even when they’re not exercising. That means they can eat more without getting fat.

Some part of this effect is due to higher muscle mass, which “costs” energy to maintain.

But muscle isn’t the only tissue athletes grow; bigger athletes also have bigger livers.1

In fact, muscle is a lot less metabolically expensive than other organs. Muscle consumes only 13 kcal/kg/day, while liver, brain, heart, and kidney consume 200, 240, 440, and 400 kg/kg/day respectively. In fact, 60-70% of resting energy expenditure2 in adults comes from these four organs, even though they’re only 6% of body weight.

If you look at “small”, “medium”, and “large” athletes3 (male, mean weights 147, 170, and 200 lbs), obviously the larger athletes have more REE than the smaller ones. What’s surprising is that most of this is due to non-muscle fat-free mass differences — i.e. organs, mostly liver. The difference between “small” and “medium” athletes’ REE is 73% due to this “residual mass”. The difference between “medium” and “large” athletes’ REE is 56% due to “residual mass”.

Same deal for female athletes4 — 76% of the REE difference between “medium” and “small”, and 36% of the REE difference between “large” and “small”, is due to increases in “residual mass”.

If you compare sumo wrestlers to untra...
New UChicago Rationality Group
by Noah Birnbaum — 2024-11-08T21:20:34.485Z

Hey y'all!

I just started a rationality group on the UChicago campus and wanted to post it here to advertise it to UChicago-affiliated LessWrong readers. We've had a couple meetings so far which have been great, and I'm excited for more! A few more things:

(1) You can join the email list by submitting this form to be updated about meeting times, hear about events, and more!

(2) While we do not yet have university RSO (Registered Student Organization) status, I plan on applying for fall of next year. This would allow us to promote the club at the annual club fair, put up posters around campus (for events, fellowships, or other opportunities), and get some funding from the university.

(3) In general, the purpose of this group is threefold: (1) to create an in person community for those who would consider themselves rationalists (in the LW/ other rationalist blogosphere sense), (2) to work on the martial art of rationality (for example, I plan to run a forecasting fellowship at some point), and (3) to counterfactually introduce UChicago students and faculty to rationalist ideas and the rationality community (both to raise the sanity waterline and increase the number of Effective Altruists on campus).

Any comments, questions, or suggestions would be greatly appreciated. If you want to reach out without using the comment section, you can also email me at noahdbirnbaum@gmail.com.
Active Recall and Spaced Repetition are Different Things
by Saul Munn — 2024-11-08T20:14:56.092Z
Link: https://www.brasstacks.blog/ar-srs-different/

Epistemic status: splitting hairs. Originally published as a shortform; thanks @Arjun Panickssery for telling me to publish this as a full post.

There’s been a lot of recent work on memory. This is great, but popular communication of that progress consistently mixes up active recall and spaced repetition. That consistently bugged me — hence this piece.

If you already have a good understanding of active recall and spaced repetition, skim sections I and II, then skip to section III.

Note: this piece doesn’t meticulously cite sources, and will probably be slightly out of date in a few years. I link to some great posts that have far more technical substance at the end, if you’re interested in learning more & actually reading the literature.

I. Active Recall

When you want to learn some new topic, or review something you’ve previously learned, you have different strategies at your disposal. Some examples:

Watch a YouTube video on the topic.
Do practice problems.
Review notes you’d previously taken.
Try to explain the topic to a friend.
etc

Some of these boil down to “stuff the information into your head” (YouTube video, reviewing notes) and others boil down to “do stuff that requires you to use/remember the information” (doing practice problems, explaining to a friend). Broadly speaking, the second category — doing stuff that requires you to actively recall the information — is way, way more effective.

That’s called “active recall.”

II. (Efficiently) Spaced Repetition

After you learn something, you’re likely to forget it pretty quickly:

Fortunately, reviewing the thing you learned pushes you back up to 100% retention, and this happens each time you “repeat” a review:

That’s a lot better!

…but that’s also a lot of work. You have to review the thing you learned in intervals, which takes time/effort. So, how can you do the least the number of repetitions to keep your retention as high as possible? In other words — what should be the size of the intervals? Should you space them out every day? Every week? Should you change the size of the spaces between repetitions? How?

As it turns out, efficiently spacing out repetitions of reviews is a pretty well-studied problem. The answer is “riiiight before you’re about to forget it:”

Generally speaking, you should do a review right before it crosses some threshold for retention. What that threshold actually is depends on some fiddly details, ...
Boring & straightforward trauma explanation
by lemonhope — 2024-11-08T09:45:19.486Z

I thought of this a couple years ago and figured it was so obvious that it wasn't worth posting about, but people are still discussing trauma endlessly, and I have not seen an explanation written anywhere, so here's this.

"Trauma" is a bad experience deemed anomalous. It means "the world is not usually like that". We do not call any behavior or emotional pattern "trauma" if it is obviously adaptive.

If you are in a war and you lie down and panic when you hear loud bangs, nobody will call you traumatized. When the war is over and you panic at fireworks, people will say you are traumatized. If then a bomb lands nearby and you survive because you took cover, they'll say you were smart and acted fast.

If 10% of your country got murdered a generation or two ago for not going with the political majority, then you are completely sane for shutting off the thinky brain in politics. (The USSR, China, Korea, Vietnam, Nigeria, Sudan, Afghanistan, Cambodia, Ethiopia, Lebanon...) If that won't happen where you live now, it's "intergenerational trauma".

If you got raped (and half your friends did too) and you are anxious and distrustful, then maybe you are just correctly calibrated about your own social sphere. If it was in a different country 10 years ago, then it's trauma.

If you got ostracized and called creepy in school, you might have a good idea of what actually happens when you ask girls out in public. If your classmates were jerks and you don't have acne anymore, it's trauma.

If you got emotionally beat up by all your exes, then being less open might be a good idea. If they were all cocaine addicts from the same town, it's trauma.

If driving a car makes you want to throw up since your last accident, you might be a bad driver. If a helicopter landed on you, it's trauma. (I have heard it said that if you're worried about your driving then you're probably a safe driver. The last person I met who was seriously worried about their driving totaled her car a week later. Everybody, including me, was telling her she was fine and shouldn't worry.)

It often takes years to get over trauma because that's how long it takes to get enough evidence. Even a terrible driver will only crash every few years. And you can't trust your friends (or therapist) to make a very accurate assessment of the risk, because nobody has good data.
Curriculum of Ascension
by andrew sauer — 2024-11-07T23:54:18.983Z

Not sure whether this belongs here or not, but there are plenty of fiction posts here. This is sort of halfway between a story and a worldbuilding document, based on many ideas I've learned from here and from adjacent spaces. Hopefully it will be interesting or useful to somebody.

==============================================================

The following is a series of excerpts from the textbooks of various required courses for baseline citizens wishing to eventually Ascend. They are mainly taken from introduction sections, which customarily summarize at a high level the material which the rest of the course goes more in-depth on. They are offered here as a quick summary of our beliefs and culture, to anyone from elsewhere in the vast Multiverse, even and especially in places outside of the Overseer’s control, who wishes to understand.

History of Computation

What is computation? It is a question which is at once easy and difficult to answer. Easy, because there are plenty of equivalent mathematical definitions which are easy to understand at a basic level, which all boil down informally to the same thing. Alan Turing published the first definition which would today be considered a Model of Computation back in 1936 PHW with his Turing Machine, and in the centuries (and untold googolplexes of subjective years) since then, all practically usable Models of Computation have been equivalent to his or weaker. Computation is just a kind of process which can find the output data of some kind of function or program, given the input data. All you need to be able to compute any function, is somewhere to store a list of instructions, known as a “program”, some instructions which can store and retrieve data from somewhere, known as “memory”, and some instructions which can check spots in memory and choose which instruction to execute next based on this, known as “control flow”. A computer language which has all of these elements, if unrestricted (i.e. infinite memory, unrestricted control flow) is said to be “Turing-Complete” because it is as powerful as a Turing Machine, and therefore is capable of computing all functions that any known Model of Computation can.

It is, however, quite difficult in another sense to pin down what exactly we think of as “computation”, particularly when referring to it in an informal, rather than mathematical sense. For example...
Analyzing how SAE features evolve across a forward pass
by bensenberner — 2024-11-07T22:07:02.827Z
Link: https://arxiv.org/abs/2410.08869

This research was completed for the Supervised Program for Alignment Research (SPAR) summer 2024 iteration. The team was supervised by @Stefan Heimersheim (Apollo Research). Find out more about the program and upcoming iterations

here

.

TL,DR: We look for related SAE features, purely based on statistical correlations. We consider this a cheap method to estimate e.g. how many new features there are in a layer and how many features are passed through from previous layers (similar to the feature lifecycle in Anthropic’s

Crosscoders

). We find communities of related features, and features that appear to be quasi-boolean combinations of previous features.

Here’s a

web interface

showcasing our feature graphs.

Communities of sparse features through a forward pass. Nodes represent residual stream SAE features that were active in the residual stream for a specific prompt of text. The rows of the graph correspond to layers in GPT-2 (the bottom row is an earlier layer). The edges represent the Jaccard similarity of the activations of features across many other prompts. Colors represent different subgraphs discovered by a community-finding algorithm. Features within a community typically capture similar concepts. More graphs can be viewed in the

feature browser

.

We ran many prompts through GPT-2 residual stream SAEs and measured which features fired together, and then created connected graphs of frequently co-firing features (“communities”) that spanned multiple layers (inspired by work from

Marks 2024

). As expected, features within communities fire on similar input tokens. In some circumstances features appeared to “specialize” in later layers.

Feature evolution: a layer 2 SAE feature (bottom) which detects “evidence” in many different contexts specializes into later layer features which detect “evidence” in mutually exclusive contexts. These relationships were discovered by counting, of all the times that a feature activated on a set of inputs, which previous-layer feature activated most frequently as well.

You can read the paper on

arXiv

, or find us at the

ATTRIB

workshop at NeurIPS!
Markets Are Information - Beating the Sportsbooks at Their Own Game
by JJXW — 2024-11-07T20:58:43.389Z
Link: https://thehobbyist.substack.com/p/markets-are-information-beating-the

TL;DR Sports betting markets are weird because there are many market makers and they unilaterally set their own prices. This combined with the fact that some sports books are better than others presents an opportunity for sharp bettors to execute a statistical arbitrage strategy across books by using information from more skilled books to take advantage of mispricings at less skilled books. The linkpost covers this sharp[1] market based arbitrage sports betting strategy in more detail as well as how I've fared executing this strategy across ~1,500 bets over the past 11 months or so.

I do have some additional thoughts that I think would be relevant to the folks here on LW that I've been mulling over since doing the initial write up.

Sharp Sports Betting as a Calibration Tool

Sharp sports betting as a hobby seems to be a reasonable way to help calibrate your mind on how likely an event "feels" like it should happen. This is especially true with +EV long odds bets. Outside of an artificial setting (e.g. sequence of coin flips), it is not easy to regularly come across events that have ~5% or a ~10% chance of occurring, but these can be very frequent in sports betting. I think I've placed a few hundred bets that have <5% of paying out and I think it has improved my gut feeling on what a 5% event should feel like.

In the past I have tried out forecasting sites and something along the lines of Katja's calibration exercises, but they mostly failed to keep my attention which is possibly a revealed preference that calibration doesn't matter as much as I thought to me. The skin-in-the-game element and the ability to get in a decent about of volume due to how quickly events resolve (most bets resolve within 24 hours of placing them) make it a more effective calibration technique than other forms of forecasting for me.

The disadvantages to this are, of course, that you have to risk money and it takes effort to find +EV wagers. For me, the main utility from engaging in sharp betting is the money with calibration being a side effect. I imagine you could get some calibration benefit out of placing very small wagers if that is your goal.

There is also possibly an interesting empirical question here to test people's calibration before and after engaging in sharp betting to see if it actually works.

Utility of Sharp Sports Betting

Unlike prediction markets, pr...