Tom Cunningham

Note I wrote this around 2020, when working at Facebook, trying to reconcile practical decision-making with how economists talk about identification.

This page surveys definitions related to recursive self-improvement.

Thanks to Nate Rush, Thomas Kwa, Beth Barnes, Eli Lifland, Chris Ong, Basil Halperin, Tom Houlden, Parker Whitfill, Phil Trammell, & Andy Haupt for comments.

Suppose you overtake 10 times as many runners as overtake you.

Thanks to Zoë Hitzig & Parker Whitfill, among others, for helpful comments.

A prediction: people will move towards producing documents that are machine-verified.

Validation Checks Overall: ⚠️ Warning ✅ [43/43] Cited sources exist in posts/ai.bib (programmatic) ✅ [33/33] Table rows have required fields (programmatic) ✅ [33/33] QMD quotes match posts/ai.bib (programmatic) ✅ [33/33] QMD growth values match posts/ai.b…

Thanks to comments from Daniel Björkegren, Andreas Haupt, Philip Trammel, Brent Cohn, Nick Otis, Andrey Fradkin, Jon de Quidt, Joel Becker.

I’ve spent a lot of time trying to prove things.

Here are two nice pieces of Bayesian logic observations that help explain everyday intuitions: When an outcome is the sum of two components then your belief about the contribution of the thinner-tailed component will be first increasing then decreasing in…

When formalizing tradeoffs our decisions typically get worse before they get better.

In short:1 1 First version January 23 2023.

This note contains some ideas about hierarchical structure in perception, judgment and decision-making that I haven’t seen explained clearly elsewhere. 1 Summary A model of encapsulated inference (“bloodhound”).

Thanks to many comments, esp.

This document describes five technologies for automated text moderation, each roughly correspond to an historical phase.

This material was first presented at MIT CODE 2021.

Introduction I give a simple Bayesian way of thinking about experiments, and implications for interpretation and extrapolation.

Thanks to comments from many, especially Giorgio Martini, Grady Ward, Rob Donnelly, Inés Moreno de Barreda, and Colin Fraser.

A model of internet media: the platform chooses the composition, the user chooses the quantity.

TL;DR: The experiments run by Meta during the 2020 elections were not big enough to test the theory that social media has made a substantial contribution to polarization in the US.