Modeling Progress in AI

TL;DR

This paper reviews and proposes methods for rigorously modeling AI progress by linking hardware, algorithms, and human inputs, aiming to better understand technological growth and unemployment impacts.

Contribution

It introduces a framework for quantitatively modeling AI progress, integrating hardware, algorithms, and human factors, and applies it to analyze technological unemployment.

Findings

Proposes models linking hardware improvements and algorithmic progress.

Highlights the role of human inputs in AI development.

Suggests tailored models for assessing unemployment impacts.

Abstract

Participants in recent discussions of AI-related issues ranging from intelligence explosion to technological unemployment have made diverse claims about the nature, pace, and drivers of progress in AI. However, these theories are rarely specified in enough detail to enable systematic evaluation of their assumptions or to extrapolate progress quantitatively, as is often done with some success in other technological domains. After reviewing relevant literatures and justifying the need for more rigorous modeling of AI progress, this paper contributes to that research program by suggesting ways to account for the relationship between hardware speed increases and algorithmic improvements in AI, the role of human inputs in enabling AI capabilities, and the relationships between different sub-fields of AI. It then outlines ways of tailoring AI progress models to generate insights on the specific issue of technological unemployment, and outlines future directions for research on AI progress.