  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.
