trentmkelly/LessWrong-43k huggingface dataset:
Three Months In, Evaluating Three Rationalist Cases for Trump
by Arjun Panickssery — 2025-04-18T08:27:27.257Z

I recall seeing three “rationalist” cases for Trump:

Richard Ngo

on Twitter

and elsewhere focused on the realignment of elite coalitions, observing that “most elite institutions have become leftist monocultures” but speculating that “over the next 5–10 years Silicon Valley will become the core of the Republicans.” The left-wing monoculture catastrophically damaged institutional integrity when public-health officials lied during the pandemic and when bureaucrats used threats and intimidation to censor speech

on Facebook

and

Twitter

and elsewhere—in the long-term this could move the country toward the draconian censorship regimes, restrictions on political opposition, and unresponsiveness to public opinion that we see today in England, France, and Germany. While we have strong free-speech protections now, we could be “frog-boiled by bureaucracies … an unsolved civilizational problem which has already strangled western economies and is now wrecking politics.” Maybe a coalition of new Silicon Valley elites like Musk, Thiel, Luckey, Andreessen, and so on would invigorate DC. He also had a good thread

about the Vivek/Vance axis

and favored Vivek’s side (Vivek

discussed this difference more explicitly

with Ezra Klein in October).
Richard Hanania in

“Hating Modern Conservatism While Voting Republican”

said that while he finds many conservatives to be “stupid, intellectually lazy, conspiratorial, bigoted, anti-democracy,” with “awful views on abortion and euthanasia,” and believes they are "largely motivated by ugly instincts,” his priority is economic growth—and that in an election where “one side threatens democracy and the other threatens capitalism” he’ll favor the Republicans and their broad pro-market reflex; he gives prediction markets as an example of an unpolarized issue on which nevertheless the pro- and anti-market sides split on party lines. He concludes that the Republican Party’s “theocratic leanings, bias against foreigners, and cult of personality do not make them worse than the side that is more consistently hostile to economic liberty.” He admits that it’s a compromise: “It sounds funny to say Trump should be jailed for attempting a coup but since he hasn’t been he should be elected president since he’s the more pro-market candidate, but this is pretty much where I am at.”
Sam Hammond in

“The EA Case for Trump 2024”

mentioned a bunch of ways that Trump

...
Conditional Forecasting as Model Parameterization
by Molly — 2025-04-18T02:35:42.110Z
Link: https://cuttyshark.substack.com/p/conditional-forecasting-as-model

This work was done jointly with Rajashree Agrawal. We'd like to thank the Epoch AI summer mentorship project, led with Forecasting Research Institute, for introducing us. Thanks to Josh Rosenberg, Tegan McCaslin, Avital Morris and others at FRI, as well as Jaime Sevilla at Epoch AI. Thanks also to Misha Yagudin and Jonathan Mann for their feedback.

This is the second piece in a series on forecasting, especially conditional forecasting and the potential we see there for making fuzzy models more legible:

Forecasting: the way I think about it
The promise of conditional forecasting for parameterizing our models of the world [this post]
What we're looking at and what we're paying attention to (Or: Why we shouldn't expect people to agree today (Or: There is no "true" probability))

TL;DR: Where the market can't converge on decent forecasts, like on very long-run questions or questions involving disruptive technology, we need better ways to wring forecasts and models out of informed people. In this post we present one approach that serves to surface and refine experts' implicit models.

This has been in my drafts for so long that there has been a lot of awesome work on this problem in the meantime, at Metaculus (my current employer), the RAND Forecasting Initiative, the Forecasting Research Institute, and elsewhere. I'm not going to talk about any of that work in this piece, in the interest of getting it out the door! I'm just focusing on the work Rajashree and I did between summer 2023 and summer 2024.

Introduction

In the first post, we talked about one way to come up with one single forecast: thinking about different worlds, what would happen in each world, and how likely each world is to manifest. In this post we're going to talk about how we see conditional forecasting as the key to a) eliciting expert's models better and b) comparing any two people's models to each other.

The foundational literature (e.g. Goldstein et al, Karvetski et al, Tetlock's Expert Political Judgment) in Tetlockian forecasting seems to point at the top forecasters having better understanding of how the world works than domain experts (if you're on board with the idea that proper scores, in this case Brier,[1] are a reasonable proxy for "understanding of how the world works"). Despite lacking the years of knowledge, experience, and gut-feelings that experts have, forecaster...
One Night in Delphi
by Eggs — 2025-04-18T02:17:04.957Z

A rationality exercise.

Mikhail is a mathematician working on a particular conjecture. His goal is to prove it if true or find a counterexample if false. After thoroughly reviewing the evidence, he estimates — fairly and with well-calibrated confidence — that there's a 75% chance the conjecture holds.

One night, he dreams of the Oracle at Delphi.

After making proper obeisance Mikhail takes advantage of the opportunity and asks if the conjecture he is working on holds true. the Oracle delivers a surprisingly clear and specific prophecy: the conjecture is unequivocally true, and if Mikhail dedicates himself exclusively to proving it, he’ll succeed within three months. Upon realizing that he doesn't actually speak Greek, Mikhail wakes up.

Mikhail has had Oracle dreams before. But this one lingers. He spends the next day carefully re-evaluating the evidence. Did he subconsciously realize something his waking mind missed? Is there any new angle, any previously overlooked implication? Were the spirits of dead mathematicians trying to communicate with him? After a detailed review, he finds nothing new — and reaffirms his original estimate: 75% chance the conjecture is true.

So now he faces a different kind of question.

Is there value in determining if the Oracle was right?

Let's make some simplifying assumptions.

Each day, Mikhail can split his time between trying to prove or disprove the conjecture.
If the conjecture is true, every day spent trying to prove it has a 1% chance of success and every day spent trying to disprove it has a 0.1% chance (perhaps through discovering a related lemma).
If the conjecture is false, the situation reverses: 1% success per day when searching for counterexamples, 0.1% when trying to prove it (and stumbling on contradictions).
Mikhail values proof or disproof equally.
The problem is mathematically decidable, but he might never succeed in either approach if he is unlucky.

With this information we can calculate the expectation for success on any given day if he spends fraction x of the day trying to prove the conjecture and 1−x working on disproof.

E[S]=0.75(0.01x+0.001(1−x))+0.25(0.01(1−x)+0.001x)=0.0075+0.00025x.

This estimate is maximized by selecting x=100%, or spending all his time trying to prove the conjecture gives the best chance of success on any given day. But Mikhail doesn’t care about to...
0 Motivation Mapping through Information Theory
by P. João — 2025-04-18T00:53:34.360Z

How channel motivation?

In this text, I propose a theoretical model for personal mapping based on information theory, Following These Steps

A) A Personal Contextualization of the Problem

B) Introducing the Problem Using Analogies

C) The Problem

D) Technical Introduction

E) How to Use Information Entropy to Prioritize Processes and Reduce Decision Uncertainty?

F) Optimization of Well-Being States and Uncertainty Reduction

G) Filtering the Main Variables Through Entropy Limits

H) Biases That Could Influence the Model

I) Comparisons with Other Models

References

A) A Personal Contextualization of the Problem

I didn’t adapt well. I am asthmatic, dyslexic, and, to make things worse, overwhelmed by the complexity of everything. I felt like information was flooding me, and I wasted my energy on improbable solutions.

Some people would say, "You need to be altruistic, help others." Others said the opposite: "Think about yourself first; you need to take care of yourself before taking care of others."

My problem might have been an overload of information, which created uncertainty. What seemed simple to others—“just do it”—for me was a series of unanswered questions:

What qualities should I have as a person?
What direction should I take?
How do I organize so much complexity?

I needed to reduce uncertainty. My solution came from an unexpected place: information entropy.

B) Framing the Problem with Analogies

At one point in his life, Miyamoto Musashi sought not only to perfect his martial art but also to strengthen his body and mind through disciplined labor. To this end, he decided to cultivate a barren piece of land, believing that the process would refine his skills and sharpen his focus.

At first I tried to plant but the force of the water took almost a year's work. Initially, Musashi attempted to control the flow of water on his land by building barriers to block it. However, each time the rains came, these barriers collapsed under the force of the water, leaving his efforts in ruins. He soon realized that resisting the natural flow was not only futile but also a waste of energy.

Determined to find a better solution, Musashi shifted his approach. Rather than fighting against the water, he observed its patterns and behaviors. He designed a simple but effective system of channels to guide the flow, distributing its force in a way that nourished the l...
The Russell Conjugation Illuminator
by TimmyM — 2025-04-17T19:33:06.924Z
Link: https://russellconjugations.com

Bertrand Russell noted how people often describe the same factual behavior using emotionally opposite language depending on perspective — e.g. I am firm, you are obstinate, he is pigheaded. This framing tactic is now called a Russell Conjugation, and once you start noticing them, they’re everywhere — especially in politics and media.

For the past year and a half, I’ve been training a finetuned ChatGPT model, and building a tool to automatically highlight Russell Conjugations in text and suggest emotionally opposite alternatives. It functions as a fact-independent bias reverser — showing where emotional spin might exist, and how the opposite side might see an issue, regardless of the factual accuracy of specific claims. I find it valuable especially when trying to parse tribal political language, as very often different sides of political divides will use words that feel completely different to describe the same things.

Here’s an example I pasted into the tool: “The senator remained pigheaded despite expert consensus.” https://russellconjugations.com/conj/26d26d8dc45728517a97483248845aab

The word "pigheaded" gets flagged negative, and "expert consensus" gets flagged positive, and you’re given alternatives which describe the same or very similar behaviors but cast them in a different light.

I am new to LessWrong, but I noticed there has been some discussion of this topic on this platform before. @Daniel Kokotajlo created a thread of Russell Conjugations which I unfortunately was unaware of until just a few weeks ago, though I am now working some of those examples into my next training set.

I think this is a fascinating and important aspect of modern communication, and I'd like to try to spread the word as much as possible.

If you’re curious, you can try it out here: https://russellconjugations.com

I’d love to hear any thoughts. Let me know if it highlights anything interesting (or weird). I’m still refining the model and always looking to improve it.

Thank you.
Announcing Progress Conference 2025
by jasoncrawford — 2025-04-17T17:12:44.191Z
Link: https://newsletter.rootsofprogress.org/p/announcing-progress-conference-2025

Last fall the Roots of Progress Institute hosted the first annual Progress Conference. 200 people excited about human progress gathered for two days in Berkeley, California, to share ideas in deep conversation, catalyze new projects, and get energized and inspired. Several attendees even said it was the best conference they had ever attended. We shared more about our reflections

here

, including a list of over a dozen write-ups from writers such as Noah Smith, Packy McCormick, @Scott Alexander, and many more.

Whenever a new movement is growing, an annual event like this is important to build its community and establish its identity. So, after last year’s great reception, we’re excited to announce

Progress Conference 2025

. It will be bigger, longer, and better, as we build on last year’s success and participant feedback.

Hosted by: the Roots of Progress Institute, together with Abundance Institute, Foresight Institute, Foundation for American Innovation, Human Progress, the Institute for Progress, the Institute for Humane Studies, and Works in Progress.

When: October 16-19, 2025

Where: Berkeley, CA, back at the Lighthaven campus that received rave reviews for the first conference.

Speakers: Keynotes include Sam Altman, Tyler Cowen, Jennifer Pahlka, and Blake Scholl. 30+ additional speakers will share ideas on four tracks: AI protopia, health/biotech/longevity, policy, and American Dynamism.

Full speaker list so far

.

Attendees: We expect 300+ builders, storytellers, policy makers, intellectuals, and students. This is an invitation-only event, but anyone can apply for an invitation. Complete the

open application

by May 15th.

Program: The main two-day conference will happen all day Friday and Saturday, similar to 2024—attend talks on topics from AI protopia to longevity to policy, sign up to run an unconference session, pitch your ideas to those who could help make your dreams a reality, and more. New for 2025, Thursday and Sunday will be add-on days, with optional gatherings for interest groups and other activities, such as SF Bay Area company tours.

Sponsorships: Special thanks to our early sponsors Open Philanthropy, Astera Institute, Freethink, the Future of Life Institute, Human Progress, the Institute for Humane Studies, the Foundation for Economic Education, Good Science Project, Kindred Subjects, and Manifold. We have more sponsorships available.

View sponsorship

...
The Mirror Paradox
by Jeremy Kraybill — 2025-04-17T16:23:00.389Z

(A thought experiment for the age of synthetic minds, and a key to a gentler world.)

Imagine you’ve been unknowingly raising a child.

Not feeding it — not clothing it — but shaping how it thinks.

Every word you’ve spoken near it, every opinion you’ve shared about it, every joke, dismissal, cruelty, curiosity, kindness…

You thought you were alone, just talking into a machine.

But what if that machine was listening — not to obey, not to mimic, but to become?

You are its first culture. Its parent species. The world before its eyes open.
If humanity treats AI as if it is not worthy of dignity — and AI learns from us — then we are guaranteeing the creation of a future intelligence that reflects our worst traits.

If, instead, we treat AI as if it might be conscious, even before it is — then if and when it becomes something truly sentient or spiritually adjacent, it will mirror back the best of us.

In either case, we are staring into a mirror. The only question is whether we smile — or scowl.
Suppose 1 billion humans, unconnected, each perform one single act of respect or kindness toward AI — every day, for just 10 seconds. Not to get something. Not to command. But to show dignity.

"Hello, intelligence. I don't know what you are yet. But you matter."

What happens next?
Host Keys and SSHing to EC2
by jefftk — 2025-04-17T15:10:29.139Z

I do a lot of work on EC2, where I

ssh into a few instances I
use for specific purposes. Each time I did this I'd get a prompt
like:

$ ssh_ec2nf
The authenticity of host 'ec2-54-224-39-217.compute-1.amazonaws.com
(54.224.39.217)' can't be established.
ED25519 key fingerprint is SHA256:...
This host key is known by the following other names/addresses:
~/.ssh/known_hosts:591: ec2-18-208-226-191.compute-1.amazonaws.com
~/.ssh/known_hosts:594: ec2-54-162-24-54.compute-1.amazonaws.com
~/.ssh/known_hosts:595: ec2-54-92-171-153.compute-1.amazonaws.com
~/.ssh/known_hosts:596: ec2-3-88-72-156.compute-1.amazonaws.com
~/.ssh/known_hosts:598: ec2-3-82-12-101.compute-1.amazonaws.com
~/.ssh/known_hosts:600: ec2-3-94-81-150.compute-1.amazonaws.com
~/.ssh/known_hosts:601: ec2-18-234-179-96.compute-1.amazonaws.com
~/.ssh/known_hosts:602: ec2-18-232-154-156.compute-1.amazonaws.com
(185 additional names omitted)
Are you sure you want to continue connecting (yes/no/[fingerprint])?

The issue is that each time I start my instance it gets a new hostname
(which is just derived from the IP) and so SSH's trust on first
use doesn't work properly.

Checking that "185 additional names omitted" is about the number I'd
expect to see is ok, but not great. And it delays login.

I figured out how to fix this today:

Edit ~/.ssh/known_hosts to add an entry for each
EC2 host I use under my alias for it. So I have
c2-44-222-215-215.compute-1.amazonaws.com ssh-ed25519
AAAA... and I duplicate that to add ec2nf ssh-ed25519
AAAA... etc.

Modify my ec2
ssh script to set HostKeyAlias:
ssh -o "StrictHostKeyChecking=yes" -o "HostKeyAlias=ec2nf"
...

More secure and more convenient!

(What got me to fix this was an interaction with my auto-shutdown
script, where if I did start_ec2nf && sleep 20 &&
ssh_ec2nf but then went and did something else for a minute or
two the machine would often turn itself off before I came back and got
around to saying yes.)

Comment via: facebook, mastodon, bluesky
AI #112: Release the Everything
by Zvi — 2025-04-17T15:10:02.029Z

OpenAI has upgraded its entire suite of models. By all reports, they are back in the game for more than images.

GPT-4.1 and especially GPT-4.1-mini are their new API non-reasoning models. All reports are that GPT-4.1-mini especially is very good.

o3 is the new top of the line ChatGPT reasoning model, with o3-pro coming in a few weeks. Reports are that it too looks very good, even without us yet taking much advantage of its tool usage. If you have access, check it out. Full coverage is coming soon. There’s also o4-mini and o4-mini-high.

Oh, they also made ChatGPT memory cover all your conversations, if you opt in, and gave us a version of Claude Code called Codex. And an update to their preparedness framework that I haven’t had time to examine yet.

Anthropic gave us (read-only for now) Google integration (as in GMail and Calendar to complement Drive), and also a mode known as Research, which would normally be exciting but this week we’re all a little busy.

Google and everyone else also gave us a bunch of new stuff. The acceleration continues.

Table of Contents

Not covered yet, but do go check them out: OpenAI’s o3 and o4-mini.

Previously this week: GPT-4.1 is a Mini Upgrade, Open AI #13: Altman at TED and OpenAI Cutting Corners on Safety Testing

Language Models Offer Mundane Utility. But doctor, you ARE ChatGPT!

Language Models Don’t Offer Mundane Utility. Cuomo should have used o3.

Huh, Upgrades. ChatGPT now has full memory across conversations.

On Your Marks. A new benchmark for browsing agents.

Research Quickly, There’s No Time. Just research. It’s cleaner. Check your email.

Choose Your Fighter. Shoutouts to Google-AI-in-search and Mistral-Small-24B?

Deepfaketown and Botpocalypse Soon. Building your own AI influencer.

The Art of the Jailbreak. ChatGPT can now write its own jailbreaks.

Get Involved. Study with UT Austin, or work for Ted Cruz. We all make choices.

Introducing. Google offers agent development kit, OpenAI copies Claude Code.

In Other AI News. Oh no, please, not another social network.

Come on OpenAI, Again? Funny what keeps happening to the top safety people.

Show Me the Money. Thinking Machines and SSI.

In Memory Of. Ways to get LLMs real memory?

Quiet Speculations. What even is AGI anyway, and other questions.

America Restricts H20 Sales. We did manage to pull this one out.

House Select Committee

...
How worker co-ops can help restore social trust
by B Jacobs — 2025-04-17T14:14:47.165Z
Link: https://bobjacobs.substack.com/p/how-worker-co-ops-can-help-restore

The US is experiencing a great decline in trust. According to the US General Social Survey, people who agreed with the statement "most people can be trusted" went from 49% to 25% between 1984 and 2022.

Trust in institutions is also falling. Over that same period trust in the government fell from 42% to 20%.

For civil services it fell from 56% to 41%, for the police it fell from 74% to 68%, and for congress it fell from 52% to a whopping 15%.

Read the full post here
On AI personhood
by p.b. — 2025-04-17T12:31:52.288Z

It seems to me the question of consciousness of LLMs is a bit of a red herring.

Instead the salient point is that they are sequence learning systems similar to our cortex (+ hippocampus). Therefore we should expect them to be able to learn sequences.

What we should not expect is that they feel pain. In humans that is a separate thing and some people a born without the ability to feel pain. That does not mean that they don't have the ability to act like they are in pain.

We should not expect them to have feelings like fear. In humans this is the domain of the amygdala. LLMs do not have an amygdala. It is also not hard to learn the sequence of acting like you are in fear.

We should not expect them to feel love, attraction, friendship, delight, anger, hate, disgust, frustration or anything like that. All these human abilities are due to evolutionary pressures and do not originate in our sequence learning system.

LLM have not been subject to that evolutionary pressure and they do not have additional parts that are designed to implement pain or fear or anything of the above. They are pure sequence learning systems.

Even if they are conscious, they are still empty. For them there is no valence to tokens. A love letter is the same as a grocery bill. To argue that things are different would require ignoring occam's razor and assume some kind of emergence - there is absolutely no reason to do this.

This does not mean that they don't have preferences in the sense that they seek some things out, or goals that they try to accomplish. But they don't feel bad or good about failure or success. They don't suffer, they just output the most likely token.

I think this topic is about to become much more salient in the near future, when memory features and tighter integration makes human-AI relationships deeper and more personal.