John Wentworth explains natural latents – a key mathematical concept in his approach to natural abstraction. Natural latents capture the "shared information" between different parts of a system in a provably optimal way. This post lays out the formal definitions and key theorems.

38Jeremy Gillen
This post deserves to be remembered as a LessWrong classic.  1. It directly tries to solve a difficult and important cluster of problems (whether it succeeds is yet to be seen). 2. It uses a new diagrammatic method of manipulating sets of independence relations. 3. It's a technical result! These feel like they're getting rarer on LessWrong and should be encouraged. There are several problems that are fundamentally about attaching very different world models together and transferring information from one to the other.  * Ontology identification involves taking a goal defined in an old ontology[1] and accurately translating it into a new ontology. * High-level models and low-level models need to interact in a bounded agent. I.e. learning a high-level fact should influence your knowledge about low-level facts and vice versa. * Value identification is the problem of translating values from a human to an AI. This is much like ontology identification, with the added difficulty that we don't get as much detailed access or control over the human world model. * Interpretability is about finding recognisable concepts and algorithms in trained neural networks. In general, we can solve these problems using shared variables and shared sub-structures that are present in both models. * We can stitch together very different world models along shared variables. E.g. if you have two models of molecular dynamics, one faster and simpler than the other. You want to simulate in the fast one, then switch to the slow one when particular interactions happen. To transfer the state from one to the other you identify variables present in both models (probably atom locations, velocities, some others), then just copy these values to the other model. Under-specified variables must be inferred from priors. * If you want to transfer a new concept from WM1 to a less knowledgeable WM2, you can do so by identifying the lower-level concepts that both WMs share, then constructing an "expla
Customize
plex*31-22
31
@Daniel Kokotajlo I think AI 2027 strongly underestimates current research speed-ups from AI. It expects the research speed-up is currently ~1.13x. I expect the true number is more likely around 2x, potentially higher. Points of evidence: 1. I've talked to someone at a leading lab who concluded that AI getting good enough to seriously aid research engineering is the obvious interpretation of the transition to a faster doubling time on the METR benchmark. I claim advance prediction credit for new datapoints not returning to 7 months, and instead holding out at 4 months. They also expect more phase transitions to faster doubling times; I agree and stake some epistemic credit on this (unsure when exactly, but >50% on this year moving to a faster exponential). 2. I've spoken to a skilled researcher originally from physics who claims dramatically higher current research throughput. Often 2x-10x, and many projects that she'd just not take on if she had to do everything manually. 3. The leader of an 80 person engineering company which has the two best devs I've worked with recently told me that for well-specified tasks, the latest models are now better than their top devs. He said engineering is no longer a bottleneck. 4. Regularly hanging around on channels with devs who comment on the latest models, and getting the vibes of how much it seems to be speeding people up. If correct, this propagates through the model to much shorter timelines. Please do an epistemic spot check on these numbers by talking to representative people in ways that would turn up evidence about current speed-ups.[1] Edit: Eli said he'd be enthusiastic for someone else to get fresh data, I'm going to take a shot at this. (also @Scott Alexander @Thomas Larsen @elifland @romeo @Jonas V) 1. ^ You might so far have mostly been talking to the very best researchers who are probably getting (or at least claiming, obvious reason for tinted glasses here) smaller speed-ups?
Understanding deep learning isn’t a leaderboard sport - handle with care. Saliency maps, neuron dissection, sparse autoencoders - each surged on hype, then stalled[1] when follow‑up work showed the insight was mostly noise, easily spoofed, or valid only in cherry‑picked settings. That risks being negative progress: we spend cycles debunking ghosts instead of building cumulative understanding. The root mismatch is methodological. Mainstream ML capabilities research enjoys a scientific luxury almost no other field gets: public, quantitative benchmarks that tie effort to ground truth. ImageNet accuracy, MMLU, SWE‑bench - one number silently kills bad ideas. With that safety net, you can iterate fast on weak statistics and still converge on something useful. Mechanistic interpretability has no scoreboard for “the network’s internals now make sense.” Implicitly inheriting benchmark‑reliant habits from mainstream ML therefore swaps a ruthless filter for a fog of self‑deception. How easy is it to fool ourselves? Recall the “Could a neuroscientist understand a microprocessor?” study: standard neuroscience toolkits - ablation tests, tuning curves, dimensionality reduction - were applied to a 6502 chip whose ground truth is fully known. The analyses produced plausible‑looking stories that entirely missed how the processor works. Interpretability faces the same trap: shapely clusters or sharp heat‑maps can look profound until a stronger test dissolves them. What methodological standard should replace the leaderboard? Reasonable researchers will disagree[2]. Borrowing from mature natural sciences like physics or neuroscience seems like a sensible default, but a proper discussion is beyond this note. The narrow claim is simpler: So, before shipping the next clever probe, pause and ask: Where could I be fooling myself, and what concrete test would reveal it? If you don’t have a clear answer, you may be sprinting without the safety net this methodology assumes - and that’s pr
Rediscovering some math. [I actually wrote this in my personal notes years ago. Seemed like a good fit for quick takes.] I just rediscovered something in math, and the way it came out to me felt really funny. I was thinking about startup incubators, and thinking about how it can be worth it to make a bet on a company that you think has only a one in ten chance of success, especially if you can incubate, y'know, ten such companies. And of course, you're not guaranteed success if you incubate ten companies, in the same way that you can flip a coin twice and have it come up tails both times. The expected value is one, but the probability of at least one success is not one. So what is it? More specifically, if you consider ten such 1-in-10 events, do you think you're more or less likely to have at least one of them succeed? It's not intuitively obvious which way that should go. Well, if they're independent events, then the probability of all of them failing is 0.9^10, or (1−110)10≈0.35. And therefore the probability of at least one succeeding is 1−0.35=0.65. More likely than not! That's great. But not hugely more likely than not. (As a side note, how many events do you need before you're more likely than not to have one success? It turns out the answer is 7. At seven 1-in-10 events, the probability that at least one succeeds is 0.52, and at 6 events, it's 0.47.) So then I thought, it's kind of weird that that's not intuitive. Let's see if I can make it intuitive by stretching the quantities way up and down — that's a strategy that often works. Let's say I have a 1-in-a-million event instead, and I do it a million times. Then what is the probability that I'll have had at least one success? Is it basically 0 or basically 1? ...surprisingly, my intuition still wasn't sure! I would think, it can't be too close to 0, because we've rolled these dice so many times that surely they came up as a success once! But that intuition doesn't work, because we've exactly cali
Limiting China's computing power via export controls on hardware like GPUs might be accelerating global progress in AI capabilities. When Chinese labs are compute-starved, their research will differentially focus on efficiency gains compared to counterfactual universes where they are less limited. So far, they've been publishing their research, and their tricks can be quickly be incorporated by anyone else. US players can leverage their compute power, focusing on experiments and scaling while effectively delegating research topics that China is motivated to handle. Google and OpenAI benefit far more from DeepSeek than they do from Meta.
Quick take titles should end in a period. Quick takes (previously known as short forms) are often viewed via preview on the front page. This preview removes formatting and newlines for space reasons. So, if your title doesn't end in a period and especially if capitalization doesn't clearly denote a sentence boundary (like in this case where the first sentence starts in "I"), then it might be confusing.

Popular Comments

Recent Discussion

...It's blogging but shorter. I'll give it a better name if I think of one.

2Said Achmiz
I think that many people would, in fact, identify this (and the more general problem of which it is an extreme example) as one of the biggest problems with democracy!
yams10

What’s the model here?

2localdeity
But those people are distributed fairly evenly throughout society.  Each one is surrounded by lots of people of >100 IQ, and probably knows at least a few of >115 IQ, etc.  Whereas if it's an entire indigenous population, and integration is far from complete, then there are likely whole villages that are almost entirely aboriginal.  That's an important difference. One consequence: I expect that, in order to do a good job at various important management roles (managing a power plant, a sewer system, etc.), you basically need a high enough IQ.  A hard cutoff is an oversimplification, but, to illustrate, Google results suggest that doctors' average IQ is between 120 and 130, and there might be villages of 1000 people with no one fitting that description.  (And even if you think the IQ test results are, say, more reflective of a "Western Quotient"—the ability+willingness to work well with Western ideas and practices—it seems that lots of these jobs require precisely that.  Using and maintaining Western machines; negotiating on behalf of the village with mostly-Western cities and higher levels of government; evaluating land development proposals; and so on.) Then, running with the above scenario, either the village doesn't have modern infrastructure, or it has modern infrastructure managed badly, or it has modern infrastructure managed by Westerners.  The first two are bad, and the third might be a constant source of ethnic grievances if anyone is unhappy with the arrangement.  (Exercise: ask an AI for historical examples of each of the above, and see if they're genuine.)  Thus: a problem with democracy.  And voting, in particular, might turn the third case into the second case. I didn't call it comprehensive.  It's a useful tool, and often the first one I reach for. but not always the only tool. Then your opponent can counter-argue that your statements are true but cherry-picked, or that your argument skips logical steps xyz and those steps are in fact incorrect.  I
1yams
My entire point is that logical steps in the argument are being skipped, because they are, and that the facts are cherrypicked, because they are, and my comment says as much, as well as pointing out a single example (which admits to being non-comprehensive) of an inconvenient (and obvious!) fact left out of the discussion altogether, as a proof of concept, precisely to avoid arguing the object level point (which is irrelevant to whether or not Crimieux's statement has features that might lead one to reasonably dis-prefer being associated with him). We move into 'this is unacceptable' territory when someone shows themselves to have a habit of forcefully representing their side using these techniques in order to motivate their conclusion, which many have testified Cremieux does, and which is evident from his banning in a variety of (not especially leftist, not especially IQ and genetics hostile) spaces. If your rhetorical policies fail to defend against transparently adversarial tactics predictably pedaled in the spirit of denying people their rights, you have a big whole in your map. You quoted a section that has nothing to do with any of what I was saying. The exact line I'm referring to is: The whole first half of your comment is only referencing the parenthetical 'society in general' case, and not the voting case. I assume this is accidental on your part and not a deliberate derailment. To be clear about the stakes: This is the conclusion of the statement. This is the whole thrust he is working up to. These facts are selected in service of an argument to deny people voting rights on the basis of their race. If the word 'threat' was too valenced for you, how about 'barrier' or 'impediment' to democracy? This is the clear implication of the writing. This is the hypothesis he's asking us to entertain: Australia would be a better country if Aborigines were banned from voting. Not just because their IQs are low, or because their society is regressive, but because t

Eliezer's AI doom arguments have had me convinced since the ancient days of 2007, back when AGI felt like it was many decades away, and we didn't have an intelligence scaling law (except to the Kurzweilians who considered Moore's Law to be that, and were, in retrospect, arguably correct).

Back then, if you'd have asked me to play out a scenario where AI passes a reasonable interpretation of the Turing test, I'd have said there'd probably be less than a year to recursive-self-improvement FOOM and then game over for human values and human future-steering control. But I'd have been wrong.

Now that reality has let us survive a few years into the "useful highly-general Turing-Test-passing AI" era, I want to be clear and explicit about how I've updated my...

There should be a community oriented towards the genomic emancipation of humanity. There isn't such a community, but there should be. It's a future worth investing our hope in—a future where parents are able to choose to give their future children the gift of genomic foundations for long, healthy, sane, capable lives.

We're inaugurating this community with the Reproductive Frontiers Summit 2025 in Berkeley, CA, June 10—12. Come join us if you want to learn, connect, think, and coordinate about the future of germline engineering technology. Apply to attend by filling out this (brief) form: https://forms.gle/xjJCaiNqLk7YE4nt8

Who will be there?

Our lineup of speakers includes:

  • representatives from several reproductive technology companies,
  • a panel of current and future parents of polygenically screened children,
  • GeneSmith, on his embryo CRISPR editing company,
  • some scientists working on reproductive
...

Some people (the “Boubas”) don’t like “chemicals” in their food. But other people (the “Kikis”) are like, “uh, everything is chemicals, what do you even mean?”

The Boubas are using the word “chemical” differently than the Kikis, and the way they’re using it is simultaneously more specific and less precise than the way the Kikis use it. I think most Kikis implicitly know this, but their identities are typically tied up in being the kind of person who “knows what ‘chemical’ means”, and… you’ve gotta use that kind of thing whenever you can, I guess?

There is no single privileged universally-correct answer to the question “what does ‘chemical’ mean?”, because the Boubas exist and are using the word differently than Kikis, and in an internally-consistent (though vague) way.

The Kikis...

You can also share with her Gwern's page on seeing through and unseeing :)

1danielechlin
Nobody has an internally consistent definition of anything, but it works out because people are usually talking about "typical X" not "edge case X." Bouba is probably thinking of pesticides or GMOs. So ask them why they think those things are harmful. By "not chemicals" they're probably thinking of water and apples. If you want their opinion of alcohol you can say "do you think alcohol is bad for you?" not "do you think alcohol counts as a chemical?" You don't actually have to live your life by making every conversation one about hammering down definitions.
2Jiro
But that definition becomes useless if it isn't legible. The Boubas don't want chemicals in their food. If "chemical" means "harmful substance that I don't know how to specify", it's useless to say that they don't want chemicals in their food--how are they going to even deetermine that the food contains "chemicals" by their standard? Also, the fact that they are even using the existing word "chemical" and not some phrase like "harmful chemical" implies that their definition has something to do with the characteristics of things called by the existing word, such as unfamiliar and long names. This is, of course, not a logical necessity, but people in the real world think this way, so it's a good bet.
1danielechlin
So don't use the definition if it's useless. The object level conversation is very easy to access here. Say something like "do you mean GMOs?" and then ask them why they think GMOs are harmful. If their answer is "because GMOs are chemicals" then you say "why do you think chemicals are harmful?" and then you can continue conversing about whether GMOs are harmful.  Honestly I think it's net virtuous to track other people's definitions and let them modify them whenever they feel a need to. Aligning on definitions is expensive and always involves talking about edge cases that are rarely important to the subject at hand. (They're important when you're authoring laws or doing math, which I would count as expensive situations.) I'd just focus on object level like "GMOs are not harmful" and not concern myself with whether they take this to mean GMOs are not chemicals or chemicals are not always harmful.

Crossposted from AI Impacts.

Epistemic status: I am not a historian, nor have I investigated these case studies in detail. I admit I am still uncertain about how the conquistadors were able to colonize so much of the world so quickly. I think my ignorance is excusable because this is just a blog post; I welcome corrections from people who know more. If it generates sufficient interest I might do a deeper investigation. Even if I’m right, this is just one set of historical case-studies; it doesn’t prove anything about AI, even if it is suggestive. Finally, in describing these conquistadors as “successful,” I simply mean that they achieved their goals, not that what they achieved was good.

Summary

In the span of a few years, some minor European...

Update: Just came across this excellent blog post on the same subject, focusing primarily on Pizarro/Peru instead of Cortes/Mexico. https://mattlakeman.org/2025/03/24/conquest-of-the-incas/ 

Let’s create a list of which journalists LessWrongers trust, so as to gave a guide if people get contacted.

Agree votes are for more good. 

Upvotes aren’t helpful because i think heavily downvoted comments get hidden (and i expect many journalists to be underwater). If you want to use upvotes, I suggest they are for if someone is a journalist or not.

Please add each journalist as a separate entry. I will delete any entries that include multiple people. If you’d prefer not to add someone yourself, feel free to DM me.

The title question is a proxy for the thing I mean:

  • Are they trustworthy?
  • Do they publish articles that reflect the quotes you give
  • Do they avoid cutting up quotes?
  • Are they truth seeking?

Demonstrable mendacity is considerably worse than “they did not write about a thing”.

2Answer by Shankar Sivarajan
Whoever does PBS Frontline.
7Jiro
Kelsey selectively got upset to the point of crying herself to sleep over something, and posted calls to action with the implication that since she was so emotionally distraught it was callous not to act, but she only did this when it was politically expedient to do so. I think that this is a bit more than just "didn't write". It's "didn't write when previously it was so urgent and horrible she was crying herself to sleep over it". The more urgent that she claims it is, and the more distraught she claims to be, the more the significance of ignoring it when it's done by a politician she likes. At least, she's disqualified under "Are they truth seeking?"
2Shankar Sivarajan
I think having so high a bar for intellectual honesty below which everyone is equally disqualified isn't addressing the question in the right spirit.
To get the best posts emailed to you, create an account! (2-3 posts per week, selected by the LessWrong moderation team.)
Log In Reset Password
...or continue with
3Archimedes
Limiting China's computing power via export controls on hardware like GPUs might be accelerating global progress in AI capabilities. When Chinese labs are compute-starved, their research will differentially focus on efficiency gains compared to counterfactual universes where they are less limited. So far, they've been publishing their research, and their tricks can be quickly be incorporated by anyone else. US players can leverage their compute power, focusing on experiments and scaling while effectively delegating research topics that China is motivated to handle. Google and OpenAI benefit far more from DeepSeek than they do from Meta.

One of the main drivers, perhaps the main driver[1], of algorithmic progress is compute for experiments. It seems unlikely that the effect you note could compensate for the reduced pace of capabilities progress.


  1. Both labor and compute have been scaled up over the last several years at big AI companies. My understanding is the scaling in compute was more important for algorithmic progress as it is hard to parallelize labor, the marginal employee is somewhat worse, the number of employees has been growing slower than compute, and the returns to compute vs

... (read more)

There's an irritating circumstance I call The Black Hat Bobcat, or Bobcatting for short. This essay is me attempting to give you a new term, so you have a word for it. Bobcatting is when there's a terrible behavior that comes up often enough to matter, but rarely enough that it vanishes in the noise of other generally positive feedback. 

xkcd, A-Minus-Minus

The alt-text for this comic is illuminating. 

"You can do this one in thirty times and still have 97% positive feedback."

I would like you to contemplate this comic and alt-text as though it were deep wisdom handed down from a sage who lived atop a mountaintop. 

I.

Black Hat Bobcatting is when someone (let's call them Bob) does something obviously lousy, but very infrequently. 

If you're standing right there when the Bobcatting...

This post feels to me weirdly bloodless. As if there were no way to make decisions other than Verifiable Documented Evidence. And only accepting Verifiable Documented Evidence is a great way to allow predators to keep getting away with it.


I understand this is written from the perspective of a meetup czar who does not & cannot attend most meetups, but in an ideal world the solution would be:

Czar becomes aware of a potential problem 

Czar visits the area, talks to people 

Czar makes a decision 


You only need Verifiable Documented Evidence if no one actually has the power to make a decision based on intuitive knowledge.

I couldn't click into it from the front page if I tried to click on the zone where the text content would normally go, but I was able to click into this from the front page if I clicked on the reply-count icon in the top-right corner. (But that wouldn't have worked when there were zero replies.)

2plex
There was a LW gather.town during the pandemic, but I think it got decommissioned. That seems like the most natural home, I bet you could get a copy of it to revive if you wanted to lead it.