When talking about why the models improve, people frequently focus on algorithmic improvements and on hardware improvements. This leaves out improvements in data quality.

DeepResearch provides a huge quantity of high quality analysis reports. GPT5 will almost certainly be trained on all the DeepResearch requests (where there are no reason to believe they are wrong like bad user feedback) of users that haven't opted out of their data being used for training. This means that when users ask GPT5 questions where no human has written an anlaysis of the question, GPT5 might still get facts right because of past DeepResearch reports.

This means that the big companies who do have a massive amount of users that produce a massive amount of DeepResearch requests will have a leg-up that's hard...

Note: This is a linkpost from my personal substack. This is on a culture war topic, which is not normally the focus of my blogging. Rationalist friends suggested that this post might be interesting and surprising to LW readers.

Summary

• People widely exclude romantic and sexual partners on the basis of race. This is claimed not to be racism by those who have these attitudes, but it is^[1].
• Certain groups, such as Black women and Asian men, are highly excluded by all other groups. These cases also exhibit within-race asymmetries in racial preferences across gender:
  • Black women exhibit the highest endophilia (i.e. they most desire to date within race), but this is strongly not reciprocated by Black men.
  • Asian women exhibit the highest endophobia (i.e. they most refuse to date within race),

Summary

To quickly transform the world, it's not enough for AI to become super smart (the "intelligence explosion"). 

AI will also have to turbocharge the physical world (the "industrial explosion"). Think robot factories building more and better robot factories, which build more and better robot factories, and so on. 

The dynamics of the industrial explosion has gotten remarkably little attention.

This post lays out how the industrial explosion could play out, and how quickly it might happen.

We think the industrial explosion will unfold in three stages:

1. AI-directed human labour, where AI-directed human labourers drive productivity gains in physical capabilities.
   1. We argue this could increase physical output by 10X within a few years.
2. Fully autonomous robot factories, where AI-directed robots (and other physical actuators) replace human physical labour.
   1. We argue that, with current physical technology and

(A response to this post.)

If you use prediction markets to make decisions, you might think they’ll generate EDT decisions: you’re asking for P(A|B), where you care about A, and B is something like “a decision … is taken”.

Okay, so say you want to use prediction markets to generate CDT decisions. You want to know P(A|do(B)).

There’s a very simple way to do that:

• Make a market on P(A|B).
• Commit to resolving the market to N/A with 99.9% chance.
• With 0.1% chance, transparently take a random decision among available decisions (the randomness is pre-set and independent of specific decisions/market data/etc.)

Now, 99.9% of the time, you can use freely use market data to make decisions, without impacting the market! You screened off everything upstream of the decision from the market. All you need...

Table of Contents

1. Lack Of Tracking Hurts Actual Everyone.
2. Not Tracking Especially Hurts Those Who Are Struggling.
3. No Child Left Behind Left Behind.
4. Read Early, Read Often.
5. Mirror, Mirror.
6. Spaced Repetition.
7. Learning Methods.
8. Interruptions.
9. Memorization.
10. Math is Hard.
11. Get to Work.
12. The Whiz Kids.
13. High School Does Not Seem To Teach Kids Much.
14. Two Kinds of Essays.

Lack Of Tracking Hurts Actual Everyone

There was what everyone agrees was a high quality critique of the timelines component of AI 2027, by the LessWrong user and Substack writer Titotal.

It is great to have thoughtful critiques like this. The way you get actual thoughtful critiques like this, of course, is to post the wrong answer (at length) on the internet, and then respond by listening to the feedback and by making your model less wrong.

This is a high-effort, highly detailed, real engagement on this section, including giving the original authors opportunity to critique the critique, and warnings to beware errors, give time to respond, shares the code used to generate the graphs, engages in detail, does a bunch of math work, and so on. That is The Way.

So, Titotal: Thank you.

I note...

If probability is in the map, then what is the territory? What are we mapping when we apply probability theory?

"Our uncertainty about the world, of course."

Uncertainty, yes. And sure, every map is, in a sense, a map of the world. But can we be more specific? Say, for a fair coin toss, what particular part of the world do we map with probability theory? Surely it's not the whole world at the same time, is it?

"It is. You map the whole world. Multiple possible worlds, in fact. In some of them the coin is Heads in the others it's Tails, and you are uncertain which one is yours."

Wouldn't that mean that I need to believe in some kind of multiverse to reason about probability? That doesn't sound...

(Note: This is NOT being posted on my Substack or Wordpress, but I do want a record of it that is timestamped and accessible for various reasons, so I'm putting this here, but do not in any way feel like you need to read it unless it sounds like fun to you.)

We all deserve some fun. This is some of mine.

I will always be a gamer and a sports fan, and especially a game designer, at heart. The best game out there, the best sport out there is Love Island USA, and I’m getting the joy of experiencing it in real time.

A Brief Ode To One Of Our Beloved Games

Make no mistake. This is a game, a competition, and the prizes are many.

The people agree. It is...