Data Science

Autumn Toney

Data Research Analyst Print Bio

Autumn Toney is a Data Research Analyst at Georgetown’s Center for Security and Emerging Technology (CSET). Previously, she worked at the Naval Research Laboratory in computer security, focusing on anomaly detection. She is currently completing her Ph.D. in Computer Science at the George Washington University, where her research focuses on bias in machine learning. She earned a B.A. in Mathematics with a minor in history from the University of Florida and a Master of Science in Data Science from the George Washington University.

Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. Our first series of Snapshots will introduce CSET’s Map of Science and explore the underlying data and analytic utility of this new tool. Check in every two weeks to see our newest Snapshot, and keep an eye out for the future release of CSET’s Map of Science user interface, which will enable users to interact with the Map directly.

Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. Our first series of Snapshots will introduce CSET’s Map of Science and explore the underlying data and analytic utility of this new tool. Check in every two weeks to see our newest Snapshot, and keep an eye out for the future release of CSET’s Map of Science user interface, which will enable users to interact with the Map directly.

Data Snapshots are informative descriptions and quick analyses that dig into CSET’s unique data resources. Our first series of Snapshots will introduce CSET’s Map of Science and explore the underlying data and analytic utility of this new tool. Check in every two weeks to see our newest Snapshot, and keep an eye out for the future release of CSET’s Map of Science user interface, which will enable users to interact with the Map directly.

Using CSET’s new Map of Science to examine clusters of research publications, this data brief presents a comparative analysis of U.S. and Chinese research publication outputs. The authors find that global competition outcomes differ depending on the level of granularity when comparing research publication data. In a granular view of global scientific research, the United States and China together dominate almost two-thirds of the research publication output, with the rest of the world leading in more than one-third of publication output. In a general view of global scientific research, only China and the United States appear as leaders in research output.

The global map of research has shifted dramatically over the last 20 years. Annual global investment in research and development has tripled, and the United States’ share of both global R&D funding and total research output is diminishing. The open research system, with its expanding rates of investment and interconnectedness, has delivered tremendous benefits to many nations but also created new challenges for research integrity and security.

AI and Industry

May 2021

Artificial intelligence is said to be transforming the global economy and society in what some dub the “fourth industrial revolution.” This data brief analyzes media representations of AI and the alignments, or misalignments, with job postings that include the AI-related skills needed to make AI a practical reality. This potential distortion is important as the U.S. Congress places an increasing emphasis on AI. If government funds are shifted away from other areas of science and technology, based partly on the representations that leaders and the public are exposed to in the media, it is important to understand how those representations align with real jobs across the country.

The advantages of nations depend in part on their access to new inventions—and modern applications of artificial intelligence can help accelerate the creation of new inventions in the years ahead. This data brief is a first step toward understanding how modern AI and machine learning have begun accelerating growth across a wide array of science and engineering disciplines in recent years.

China’s surge in artificial intelligence development has been fueled, in large part, by advances in computer vision, the AI subdomain that makes powerful facial recognition technologies possible. This data brief compares U.S. and Chinese computer vision patent data to illustrate the different approaches each country takes to AI development.

Future U.S. competitiveness in artificial intelligence will require a robust AI workforce. This data brief analyzes market demand for AI-related jobs to determine the skills necessary in the field. It concerns jobs considered both “core AI” and “AI-adjacent.”

The U.S. government and industry both see artificial intelligence as a pivotal technology for future growth and competitiveness. What skills will be needed to create, integrate, and deploy AI applications? This data brief analyzes market demand for AI-related jobs to determine their educational requirements, dominant sectors, and geographic distribution.