Co-authored with Jonah Sinick.
How big is the field of AI, and how big was it in the past?
This question is relevant to several issues in AGI safety strategy. To name just two examples:
- AI forecasting. Some people forecast AI progress by looking at how much has been accomplished for each calendar year of research. But as inputs to AI progress, (1) AI funding, (2) quality-adjusted researcher years (QARYs), and (3) computing power are more relevant than calendar years.1 To use these metrics to predict future AI progress, we need to know how many dollars and QARYs and computing cycles at various times in the past have been required to produce the observed progress in AI thus far.
- Leverage points. If most AI research funding comes from relatively few funders, or if most research is produced by relatively few research groups, then these may represent high-value leverage points through which one might influence the field as a whole, e.g. to be more concerned with the long-term social consequences of AI.
For these reasons and more, MIRI recently investigated the current size and past growth of the AI field. This blog post summarizes our initial findings, which are meant to provide a “quick and dirty” launchpad for future, more thorough research into the topic.
- Another important input metric is theoretical progress imported from other fields, e.g. methods from statistics. ↩