trentmkelly/LessWrong-43k huggingface dataset:
Dimensionalization
by Jordan Rubin — 2025-05-29T18:18:46.763Z
Link: https://jordanmrubin.substack.com/p/dimensionalization

TL;DR

Dimensionalization is the practice of identifying and using quasi-independent axes or "dials" along which things vary. It’s the mental move behind tradeoff analysis, optimization, and design thinking. You are doing it already!

Unlike categorization or decomposition, dimensionalization preserves complexity but renders it navigable. Mastering it helps you think more clearly, decide more effectively, and communicate with greater precision.

A good decision isn’t just “good.” It’s good along the right dimensions, and good enough on the others to be worth it. You already know this; you just may not have named it. That naming, structuring mental move is dimensionalization. It is what enables us to see nuance.

You probably already dimensionalize:

Watching: familiar vs new, funny vs serious, short vs long.
Weekend: social vs solo, restful vs productive.
Shopping: quality vs price, speed vs reliability.

You don’t need to learn dimensionalization. But getting better at it makes you more precise, faster, and harder to bullshit.

So how does dimensionalization work?

Meta-Dimension 1: Fidelity

Dimensionalization is a perceptual act. It’s how you make fuzzy, complex phenomena legible. You take something like “vibe” or “quality” or “pain” and ask: what are the independent axes along which this varies?

Fidelity is how well your sliders map to reality. High-Fidelity dimensions reveal meaningful differences. They don’t collapse when you zoom out or vanish when you compare two related objects.

What makes dimensions high-Fidelity?

Validity: They track actual meaningful differences.
Stability: They hold up over time, context, or zoom level.

Examples:

High-Fidelity examples:

Quant investing: factor exposure, drawdown, alpha decay.
Software engineering: latency, modularity, throughput.
Listening to music: tempo, rhythmic complexity, texture, spatiality.
Homeowning: layout, light quality, maintenance, appreciation potential.
Making art: balance, composition, color harmony, movement.

Low-Fidelity examples:

Buzzwords without clarification ("Tech Debt", “Efficiency”).
Aesthetic terms with no shared reference point (“Vibe”, “Quality”).

When Fidelity is low, you can still win, but you’re flying blind.

Meta-Dimension 2: Leverage

Leverage is how much change you get per dial twist. A good dimension is one you can actually move—and that, when moved, actually matters.

Dimensionalization unlocks Leverage. When you kn...
Distilled Human Judgment: Reifying AI Alignment
by Devansh Mehta — 2025-05-29T18:06:39.078Z

Long time lurker but first post here, so I'll quickly introduce myself. I work at the Ethereum Foundation leading their AI x Public Goods track, which broadly encompasses coming up with and piloting new credibly neutral funding mechanisms (especially those involving the use of AI to distribute money)

The purpose of this post is two-fold;

Soliciting feedback on the idea of distilled human judgment (DHJ) and its application to AI alignment

A call to action for the rationalist community to submit models in our 2 ongoing DHJ competitions to allocate funding to core Ethereum open source repositories and identify sybil (bot) wallet addresses

Let's start with an explanation of DHJ to a 5 year old;

Your teacher needs to grade 1000 assignments
She only manages to complete 100
Many AI agents or models give a grade to all the assignments
The 100 assignments the teacher did grade selects the winning model that then gives grades to all 1000

Since people often relate new ideas with what they are already familiar with, the best way to think of DHJ is as a Kaggle competition where models are competing to accurately predict the ground truth data (human judgments).

The main difference being, we don't have ground truth data for all the answers that models give but only a subset of them. But as submitters don't know which questions we have answers to and which we don't, they have to do a good job on all of them.
DHJ <> AI Alignment

I'll start this section with a vision of a world that I don't want but which I think we're headed towards

Every company has its own monolithic AI model trained on data of their C-suite. Employees pray for beneficence from the AI overlord to get attention to their project and also for promotions, pay raises, etc. Any change to the company AI is permissioned and requires review from a core team within the company

What is a viable alternative?

Every company has a marketplace of competing AI models that any team (or outside contributors) can submit. All these models are queried for answers to decisions that the C-suite needs to make, such as funding to different departments, who to lay off or promote, etc. Executives then see which models align most closely with the subset of decisions that they do make manually to make decisions across the board.

A good analogy for thinking about these 2 visions is whether we want AI to be similar to a monopoly ...
Summer AI Safety Intro Fellowships in Boston and Online (Policy & Technical) – Apply by June 6!
by jandrade112 — 2025-05-29T18:02:12.829Z

TL;DR: Apply to AISST’s AI Policy Fellowship and Technical Fellowship by Friday, June 6. Refer others here. AISST (a group of undergrad and grad students at Harvard) is excited to be running two introductory reading groups this summer: a 7-week Intro Policy Fellowship and an 8-week Intro Technical Fellowship.

Our research-oriented Technical Fellowship covers topics like neural network interpretability, learning from human feedback, goal misgeneralization in reinforcement learning agents, and eliciting latent knowledge. See here for more details (curriculum, application, FAQ). Students with machine learning experience are especially encouraged to apply.

Our Policy Fellowship discusses topics such as the pace of progress in AI, potential threats from AI misuse and misalignment, AI audits and evaluations, and semiconductor policy. More details here (curriculum, application, FAQ).

The fellowships are open to current and incoming undergraduate and graduate students, as well as recent graduates, from Boston-area universities, and working professionals who will be in Boston during the upcoming academic year. We’ll meet weekly, in small cohorts facilitated by a grad student or upperclassman with experience in AI safety research or AI policy.

Both reading groups will have an in-person option in Cambridge as well as a virtual option. In-person cohort meetings will be 2 hours long, with lunch or dinner provided. Readings will be completed during the meetings (so no outside reading is required). Online cohort meetings will be 1 hour each, and participants will be expected to complete the readings prior to each meeting (~1 hour of reading per meeting).

We think reducing catastrophic risks from powerful artificial intelligence is one of the most important problems of our time. If you are interested, we encourage you to apply.

And please pass along the opportunity to anyone in your network who may be interested! You can refer others through this form.

The deadline for both applications is Friday, June 6, at 11:59 pm.

Apply here for the Technical Fellowship
Apply here for the Policy Fellowship
Digital sentience funding opportunities: Support for applied work and research
by aog — 2025-05-29T15:22:43.197Z

Written by Zach Freitas-Groff and posted at his request.

Summary

I’m excited to announce a

“Digital Sentience Consortium”

hosted by

Longview Philanthropy

, in collaboration with

The Navigation Fund

and

Macroscopic Ventures

, to support research and applied projects focused on the potential consciousness, sentience, moral status, and experiences of artificial intelligence systems. The opportunities include

research fellowships

,

career transition fellowships

, and a broad

request for proposals

for applied work on these topics.

For years, I’ve thought this area was seriously overlooked. It now has growing interest. Twenty-two out of 123 pages of

Claude 4’s model card

are about its potential moral patienthood.

Scientific experts increasingly say

that near-term AI sentience is a real possibility; even the skeptical neuroscientist Anil Seth says, “it is unwise to dismiss the possibility altogether.” We’re hoping to bring new people and projects into the field to increase the chance that society deals with the possibility of digital sentience reasonably, and with concern for all involved.

Apply to Research Fellowship
Apply to Career Transition Fellowship
Apply to Request for Proposals

Motivation & Focus

For about as long as I’ve been reading about transformative AI, I’ve wondered whether society would face critical decisions involving AI sentience. Until recently, I thought there was not much to be done here besides perhaps more philosophy of mind and perhaps some ethics—and I was not sure these approaches would make much progress.

Now, I think there are live areas where people can contribute:

Technically informed research on which AI systems are sentient, like

this paper

applying existing theories of consciousness to a few AI architectures.
Innovative approaches to investigate sentience, potentially in a way that avoids having to take a stand on a particular theory of consciousness, like

work on

AI introspection

.
Political philosophy and policy research on the proper role of AI in society.
Work to educate the public about the issue and improve the reasonableness of public discussion.
Advice and applied work to make it more likely that AI models, if sentient, experience wellbeing.

The goal of our Digital Sentience Consortium is to spur more work in these areas and more.

This is an area with a lot...
When to Be Nice vs Kind
by Declan Molony — 2025-05-29T15:06:41.227Z

While often used synonymously, there is a subtle yet meaningful distinction between being nice and being kind. Understanding when to prioritize kindness over niceness—and vice versa—helps me better navigate social situations with both authenticity and appropriate boundaries.

Definitions

Someone who is being nice is pleasant to be around, but internally their pleasantness may or may not come from a genuine place of warmth or love.

Someone who is being kind genuinely cares about another’s well-being, even if in the short-term they don’t necessarily make the other person feel good.

The distinction becomes apparent when someone is one, but not the other.

Being nice, but not kind

When baristas, for example, smile and ask me how my day is going, they’re being nice. They don’t actually care how I’m doing, yet I still appreciate them because they’re adding pleasantness to what would otherwise be just another mundane economic transaction.

Being kind, but not nice

A good mother is kind to her children, but not always nice. When she refuses to indulge her kid’s every request for ice cream, she’s not being nice in the short-term, but she’s being kind in the long-term (because she’s saving her kid from ill health, as

1 in 5 youths now have prediabetes

in the US).

Being neither nice nor kind

Living in a big city, I don’t acknowledge every stranger I pass on the sidewalk. I’m not adding pleasantness, nor am I genuinely caring for them—I’m being indifferent.

On the more extreme side, someone being an asshole is neither nice nor kind.

Being both nice and kind

Mister Rogers

exemplifies niceness when he waves hello to a little girl in his neighborhood, and he also expresses genuine kindness when he finds out her pet bunny is sick and he consoles her.

In fleeting interactions (like with baristas, waiters, coworkers), it’s okay to simply just be nice, without expressing genuine kindness. Asking a busy waiter how his day is really going can actually be considered rude because he has a lot of customers to serve.

And with my friends and family who I spend the most time with, I would prefer they be genuine with me rather than just nice or polite. (Though a little bit of both never hurt anyone🙂).

In my experience, getting this balance wrong—being merely polite with loved ones or trying to 'fix' casual acquaintances—can leave interactions feeling either superficia...
AI #118: Claude Ascendant
by Zvi — 2025-05-29T14:10:04.603Z

The big news of this week was of course the release of Claude 4 Opus. I offered two review posts: One on safety and alignment, and one on mundane utility, and a bonus fun post on Google’s Veo 3.

I am once again defaulting to Claude for most of my LLM needs, although I often will also check o3 and perhaps Gemini 2.5 Pro.

On the safety and alignment front, Anthropic did extensive testing, and reported that testing in an exhaustive model card. A lot of people got very upset to learn that Opus could, if pushed too hard in the wrong situations engineered for these results, do things like report your highly unethical actions to authorities or try to blackmail developers into not being shut down or replaced. It is good that we now know about these things, and it was quickly observed that similar behaviors can be induced in similar ways from ChatGPT (in particular o3), Gemini and Grok.

Last night DeepSeek gave us R1-0528, but it’s too early to know what we have there.

Lots of other stuff, as always, happened as well.

This weekend I will be at LessOnline at Lighthaven in Berkeley. Come say hello.

Table of Contents

Language Models Offer Mundane Utility. People are using them more all the time.

Now With Extra Glaze. Claude has some sycophancy issues. ChatGPT is worse.

Get My Agent On The Line. Suggestions for using Jules.

Language Models Don’t Offer Mundane Utility. Okay, not shocked.

Huh, Upgrades. Claude gets a voice, DeepSeek gives us R1-0528.

On Your Marks. The age of benchmarks is in serious trouble. Opus good at code.

Choose Your Fighter. Where is o3 still curiously strong?

Deepfaketown and Botpocalypse Soon. Bot infestations are getting worse.

Fun With Media Generation. Reasons AI video might not do much for a while.

Playing The Training Data Game. Meta now using European posts to train AI.

They Took Our Jobs. That is indeed what Dario means by bloodbath.

The Art of Learning. Books as a way to force you to think. Do you need that?

The Art of the Jailbreak. Pliny did the work once, now anyone can use it. Hmm.

Unprompted Attention. Very long system prompts are bad signs for scaling.

Get Involved. Softma, Pliny versus robots, OpenPhil, RAND.

Introducing. Google’s Lyria RealTime for music, Pliny has a website.

In Other AI News. Scale matters.

Show Me the Money. AI versus advertising revenue, UAE versus democracy.

Nvidia Sells Out. Also, they can’t meet deman

...
Social Capital - Does it Matter?
by Momcilo — 2025-05-29T12:26:08.640Z

Within the Walls

There's plenty of funny business. My dad was enthusiastically arguing that we should invest in this little known bank offering a high return. Your money doubles every month! Who told you that, I asked? The dentist. He now lives in a big house in Dedinje and has a huge painting of Petar Lubarda on the wall.

The next day he was fixing the VW Beetle - not his. It belonged to a friend who lived in Germany and occasionally needed to be chauffeured from and to the airport. The thing was rotten in places, but the engine started every time and kept up with other traffic by constantly being revved out of its wits. Now it was lying in bits on the street, oil in slicks and drops and dad's feet protruding from under the body.

My mum loved socialising. She took pride in her taste in the arts and sought company of writers, painters and actors. Her old school friends would often come for drinks in the evening and most of them looked like they came out of 70s French movies.

My great grandmother was visiting us again. She spent days sitting on a chair, narrating same old stories. This time It was about wisdom - how she systematically refused to follow doctor's advice and evaded the surgical knife. "He warned me, I've got 6 months max if I didn't have it removed. He died of a stroke 15 years ago. What a pity, he was a young man..."

Uncle and aunt lived with us - 7 people altogether. Me and my sister in one room, parents in the adjacent one, grandma in the living room and uncle and aunt in the second living room. People walked in bathrobes, queued in front of the only bathroom, smoked and chatted. There was always something cooking. When the phone rang - it was a fight who'd get to it first.

The dining room was the meeting place. My dad never felt comfortable joining in, he didn't smoke or drink coffee. Being socially awkward - he preferred staying in his room and reading motoring magazines that he was subscribed to. They'd arrive every month from England and he'd exercise delayed gratification by not opening the envelope straight away. All the chores that he normally postponed would suddenly be prioritised. That's what kept him motivated.

Uncle and aunt worked on films. She was an assistant director and he designed and...
Cross-posting to Substack
by jefftk — 2025-05-29T11:10:01.913Z

Historically people kept up with blogs via RSS, but it's been on its
way out for over a decade. These days receiving posts via email is
popular, and I should probably make my posts available this way. I
considered implementing my own emailing system, but bulk email is (or
at least was, I'm not up to date here) a pain. Instead I decided to
start cross-posting to Substack:

jefftkaufman.substack.com.
This will be yet another way to read my posts, similar to the

LessWrong mirror
and my

text-only FB
cross-posts.

I have a full RSS feed of all my posts, and Substack imported it fine.
It doesn't look like there's an option to do ongoing RSS-based
imports, but copy-paste seems to work well enough; I did this post and
the previous
one that way. At some point I'll look into automatic
cross-posting, though right now it looks like Substack doesn't support
anything good. And if I'm going to reverse engineer something I'll
start with their comments implementation, since I always want to
rehost comments.

One aspect that's a bit eerie is the URLs: both Substack and my blog
would use /p/post-name-in-title-case as the url for a
post titled "Post Name In Title Case". I've been doing this since 2013-10-28
and Substack got started in 2017 so I know I
didn't copy them ;)

Comment via: substack
Reflections on AI Wisdom, plus announcing Wise AI Wednesdays
by Chris_Leong — 2025-05-29T07:13:39.508Z

I recently finished leading an

AI Safety Camp

project on Wise AI Advisors[1] (my team included Chris Cooper, Matt Hampton, and Richard Kroon). Since we want to share our work in an orderly fashion, I’m launching Wise AI Wednesdays. Each Wednesday[2], I (or one of my teammates) will be sharing a post, initially drawn from our AI Safety Camp outputs, but later including shifting to include future work and outputs summaries or commentary on related research. I’m hoping that a regular posting schedule will help cultivate Wise AI/Wise AI Advisors as a subfield of AI Safety.

This inaugural post provides an update on how my views on AI and wisdom have changed since I won 3rd prize in the

Automation of Wisdom and Philosophy Competition

. My views have evolved considerably since then, so I thought it was worth listing a number of updates I've made:

Broadening My Focus: I had originally labelled my research direction as

Wise AI Advisors via Imitation Learning

. While I still see this as a particularly promising approach, I’m now interested in

Wise AI Advisors

more broadly. Many different reasonable-sounding paths exist; it would be arrogant to believe I’ve found the One True Path without much more investigation. I’m now more inclined to follow a 'let a thousand flowers bloom' path.
More Favourable Funding Landscape: There seems to be more desire to fund work adjacent to this space than I had anticipated. For example:

Cosmos x FIRE Truth-seeking Round

and

Fellowship on AI for Human Reasoning

. I initially thought significant effort would be needed to convince funders, but it now seems quite possible to secure funding for Wise AI projects, even though you might have to choose a project that intersects with a funder's interest level.
Increased Emphasis on Field-Building: Consequently, my focus has shifted from solely direct research progress to a combination of research and field-building. While I'm still defining what this entails, activities like establishing the case for Wise AI, exploring theories of impact, and identifying concrete, high-priority projects seem important right now.
Expanded View of Possible Contributions: My ideas about what kind of work might be useful have broadened significantly thanks to AI Safety Camp and conversations with others. I now see a much wider range of ways people can contribute (see my

draft post with

...
What was so great about Move 37?
by Caleb Biddulph — 2025-05-29T07:00:45.061Z

I frequently use "Move 37" as a shorthand for "AI that comes up with creative, highly effective ideas that no human would ever consider." Often the implication is that reinforcement learning (as used in AlphaGo) has some "secret sauce" that could never be replicated by imitation learning.

But I realize that I don't know the details of Move 37 very well, other than secondhand accounts from Go experts of how "groundbreaking" it was. I've never played Go, and I have basically no knowledge of the rules or strategies beyond the most basic descriptions. Considering how influential Move 37 is on my views about AI, it seems like I'd better try to understand what was so special about it.

I'd be interested in an explanation that builds up the necessary understanding from the ground up. This could look like: "Read this tutorial on the rules of Go, study these wiki pages about specific concepts and strategies, look at these example games, and finally read my explanation of Move 37 which uses everything you've learned."

Extremely ambitiously, after reading this explanation, I'd be able to look at a series of superficially similar Go boards, distinguish whether it might be a good idea to do a Move-37-like play, identify where exactly to move if so, and explain my answer. That may be unrealistic to achieve in a short time, but I'd be interested in getting as close as possible. An easier version of that challenge would use heavily-annotated Go boards that abstract away some parts of the necessary cognition, with notes like "this section of the board is very important to control" or "this piece has property A" or "these pieces are in formation B."[1]

If part of the explanation is "when you do an extensive Monte Carlo Tree Search from this board state guided by XYZ heuristics, Move 37 turns out to be the best move," that seems like a pretty good explanation to me—as long as the search tree is small enough that it plausibly could have been explored by AlphaGo during its match with Lee Sedol. I'm mainly interested in trying to understand the intuition behind Move 37 in the way AlphaGo might have "understood" it. If the move couldn't be found by a human without using brute force search, that would be valuable to know.

I'm particularly interested in an explanation of Move 37 because I want to know whether such an explanation is even possible. When we have superintelligent AI solving r...