Tag Archive: Data

Identifying AI Research

Christian Schoeberl Autumn Toney James Dunham
| July 2023

The choice of method for surfacing AI-relevant publications impacts the ultimate research findings. This report provides a quantitative analysis of various methods available to researchers for identifying AI-relevant research within CSET’s merged corpus, and showcases the research implications of each method.

Brian Love is a Software Engineer at the Center for Security and Emerging Technology, where he works on the Emerging Technology Observatory initiative.

Introducing the Emerging Technology Observatory

Emerging Technology Observatory
| October 19, 2022

Making sense of the often overwhelming world of emerging tech with data-driven tools and resources.

Katherine Quinn is a Data Scientist at CSET.

Christian Schoeberl is a Data Research Analyst at CSET.

CSET’s Map of Science reveals that Germany leads the world in robotics for automotive engineering.

Ronnie Kinoshita is a Deputy Director of Data Science and Research at Georgetown’s Center for Security and Emerging Technology (CSET).

It is common for observers to compare machine intelligence with individual human intelligence, but this tendency can narrow and distort understanding. Rather, this paper suggests that machines, bureaucracies and markets can usefully be regarded as a set of artificial intelligences that have been invented to complement the limited abilities of individual human minds to discern patterns in large amounts of data. This approach opens an array of possibilities for insight and future investigation.

Trends in AI Research for the Visual Surveillance of Populations

Ashwin Acharya Max Langenkamp James Dunham
| January 2022

Progress in artificial intelligence has led to growing concern about the capabilities of AI-powered surveillance systems. This data brief uses bibliometric analysis to chart recent trends in visual surveillance research — what share of overall computer vision research it comprises, which countries are leading the way, and how things have varied over time.

Drawing from their report "Small Data's Big AI Potential," CSET's Helen Toner and Husanjot Chahal discuss why smaller data approaches to AI can be helpful and how this approach can be applied within Europe.