A globally competitive AI workforce hinges on the education, development, and sustainment of the best and brightest AI talent. This issue brief compares efforts to integrate AI education in China and the United States, and what advantages and disadvantages this entails. The authors consider key differences in system design and oversight, as well as strategic planning. They then explore implications for the U.S. national security community.

A globally competitive AI workforce hinges on the education, development, and sustainment of the best and brightest AI talent. This issue brief provides an overview of the education systems in China and the United States, lending context to better understand the accompanying main report, “AI Education in China and the United States: A Comparative Assessment.”

A CSET original translation of 2021 PRC "Opinions" outlining new policies for Pudong New District in Shanghai, long a trendsetter in Chinese economic reform.

See our original translation of a 2016 PRC Ministry of Science and Technology press conference announcing the creation of China's overarching "National Key R&D Program."

See our original translation of a 2021 draft PRC government plan for the development of China's cybersecurity industry.

See our original translation of a 2012 PRC regulation governing the establishment, operation, and evaluation of state key laboratories housed in corporations.

See our original translation of a 2017 Wuhan City government document outlining policies to encourage the development of Wuhan's National Cybersecurity Talent and Innovation Base.

See our original translation of a 2020 PRC provincial government plan to boost the semiconductor manufacturing industry.

Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. Our first series of Snapshots introduced CSET’s Map of Science and explored the underlying data and analytic utility of this new tool, which enables users to interact with the Map directly.

Conventional wisdom suggests that cutting-edge artificial intelligence is dependent on large volumes of data. An overemphasis on “big data” ignores the existence—and underestimates the potential—of several AI approaches that do not require massive labeled datasets. This issue brief is a primer on “small data” approaches to AI. It presents exploratory findings on the current and projected progress in scientific research across these approaches, which country leads, and the major sources of funding for this research.