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Imagine an alternate version of the Effective Altruism movement, whose early influences came from socialist intellectual communities such as the Fabian Society, as opposed to the rationalist diaspora. Let’s name this hypothetical movement the Effective Samaritans.

Like the EA movement of today, they believe in doing as much good as possible, whatever this means. They began by evaluating existing charities, reading every RCT to find the very best ways of helping.

But many effective samaritans were starting to wonder. Is this randomista approach really the most prudent? After all, Scandinavia didn’t become wealthy and equitable through marginal charity. Societal transformation comes from uprooting oppressive power structures.

The Scandinavian societal model which lifted the working class, brought weekends, universal suffrage, maternity leave, education, and universal healthcare can be traced back all the...

1. If you find that you’re reluctant to permanently give up on to-do list items, “deprioritize” them instead

I hate the idea of deciding that something on my to-do list isn’t that important, and then deleting it off my to-do list without actually doing it. Because once it’s off my to-do list, then quite possibly I’ll never think about it again. And what if it’s actually worth doing? Or what if my priorities will change such that it will be worth doing at some point in the future? Gahh!

On the other hand, if I never delete anything off my to-do list, it will grow to infinity.

The solution I’ve settled on is a priority-categorized to-do list, using a kanban-style online tool (e.g. Trello). The left couple columns (“lists”) are very active—i.e., to-do list...

For the last month, @RobertM and I have been exploring the possible use of recommender systems on LessWrong. Today we launched our first site-wide experiment in that direction. 

(In the course of our efforts, we also hit upon a frontpage refactor that we reckon is pretty good: tabs instead of a clutter of different sections. For now, only for logged-in users. Logged-out users see the "Latest" tab, which is the same-as-usual list of posts.)

Why algorithmic recommendations?

A core value of LessWrong is to be timeless and not news-driven. However, the central algorithm by which attention allocation happens on the site is the Hacker News algorithm^[1], which basically only shows you things that were posted recently, and creates a strong incentive for discussion to always be...

This is the eighth post in my series on Anthropics. The previous one is Lessons from Failed Attempts to Model Sleeping Beauty Problem. The next one is Beauty and the Bets.

Introduction

Suppose we take the insights from the previous post, and directly try to construct a model for the Sleeping Beauty problem based on them.

We expect a halfer model, so

$P\left(Heads\&Monday\right)=P\left(Heads\right)=1/2$

On the other hand, in order not repeat Lewis' Model's mistakes:

$P\left(Heads|Monday\right)=1/2$

But both of these statements can only be true if 

$P\left(Monday\right)=1$

And, therefore, apparently, $P\left(Tuesday\right)$ has to be zero, which sounds obviously wrong. Surely the Beauty can be awaken on Tuesday! 

At this point, I think, you wouldn't be surprised, if I tell you that there are philosophers who are eager to bite this bullet and claim that the Beauty should, indeed, reason as...

EDIT 1/27: This post neglects the entire sub-field of estimating uncertainty of learned representations, as in https://openreview.net/pdf?id=e9n4JjkmXZ. I might give that a separate follow-up post.

Introduction

Suppose you've built some AI model of human values. You input a situation, and it spits out a goodness rating. You might want to ask: "What are the error bars on this goodness rating?" In addition to it just being nice to know error bars, an uncertainty estimate can also be useful inside the AI: guiding active learning^[1], correcting for the optimizer's curse^[2], or doing out-of-distribution detection^[3].

I recently got into the uncertainty estimation literature for neural networks (NNs) for a pet reason: I think it would be useful for alignment to quantify the domain of validity of an AI's latent features. If we...

Epistemic status: pretty confident. Based on several years of meditation experience combined with various pieces of Buddhist theory as popularized in various sources, including but not limited to books like The Mind Illuminated, Mastering the Core Teachings of the Buddha, and The Seeing That Frees; also discussions with other people who have practiced meditation, and scatterings of cognitive psychology papers that relate to the topic. The part that I’m the least confident of is the long-term nature of enlightenment; I’m speculating on what comes next based on what I’ve experienced, but have not actually had a full enlightenment. I also suspect that different kinds of traditions and practices may produce different kinds of enlightenment states.

While I liked Valentine’s recent post on kensho and its follow-ups a lot,...

It was all quiet. Then it wasn’t.

Note the timestamps on both of these.

Dwarkesh Patel did a podcast with Mark Zuckerberg on the 18th. It was timed to coincide with the release of much of Llama-3, very much the approach of telling your story directly. Dwarkesh is now the true tech media. A meteoric rise, and well earned.

This is two related posts in one. First I cover the podcast, then I cover Llama-3 itself.

My notes are edited to incorporate context from later explorations of Llama-3, as I judged that the readability benefits exceeded the purity costs.

Podcast Notes: Llama-3 Capabilities

1. (1:00) They start with Llama 3 and the new L3-powered version of Meta AI. Zuckerberg says “With Llama 3, we think now that Meta AI is the most intelligent, freely-available

What’s Twitter for you?

That's a long-lasting trend I often see on my feed when people praise the blue bird for getting them a job, introducing them to new people, investors, and all this and that.

What about me? I just wanted to get into dribbble — the then invite-only designer's social network which was at its peak at the time. When I realized invites were given away on Twitter, I set up an account and went on a hunt. Soon, the mission was accomplished.

For the next few years, I went on a radio silence. Like many others, I was lurking most of the time. Even today I don't tweet excessively. But Twitter has always been a town square of mine. Suited best for my interests, it’s been a...