  Over the past decade, AI research has focused heavily on building ever-larger
deep learning models. This approach has simultaneously unlocked incredible
achievements in science and technology, and hindered AI from overcoming
long-standing limitations with respect to explainability, ethical harms, and
environmental efficiency. Drawing on qualitative interviews and computational
analyses, our three-part history of AI research traces the creation of this
"epistemic monoculture" back to a radical reconceptualization of scientific
progress that began in the late 1980s. In the first era of AI research
(1950s-late 1980s), researchers and patrons approached AI as a "basic" science
that would advance through autonomous exploration and organic assessments of
progress (e.g., peer-review, theoretical consensus). The failure of this
approach led to a retrenchment of funding in the 1980s. Amid this "AI Winter,"
an intervention by the U.S. government reoriented the field towards measurable
progress on tasks of military and commercial interest. A new evaluation system
called "benchmarking" provided an objective way to quantify progress on tasks
by focusing exclusively on increasing predictive accuracy on example datasets.
Distilling science down to verifiable metrics clarified the roles of
scientists, allowed the field to rapidly integrate talent, and provided clear
signals of significance and progress. But history has also revealed a tradeoff
to this streamlined approach to science: the consolidation around external
interests and inherent conservatism of benchmarking has disincentivized
exploration beyond scaling monoculture. In the discussion, we explain how AI's
monoculture offers a compelling challenge to the belief that basic,
exploration-driven research is needed for scientific progress. Implications for
the spread of AI monoculture to other sciences in the era of generative AI are
also discussed.
