CSET’s unique data-driven approach is enabled by our data team. The team includes data scientists, data research analysts, software engineers, survey and translation specialists, and more. We maintain CSET’s vast data holdings, which include nearly 60 analysis-ready datasets, offering unprecedented coverage of the emerging technology ecosystem. The team develops and deploys the latest methods in data science and machine learning to clean, link, classify, and otherwise enhance data for analytic use, as well as support the curation and annotation of original datasets - from surveys to scraped online information. Resulting research and tools are presented in CSET Data Briefs and Data Snapshots, public repositories, academic conferences and publications, and interactive tools.
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Making sense of the often overwhelming world of emerging tech with data-driven tools and resources.
As technology competition intensifies between the United States and China, governments and policy researchers are looking for metrics to assess each country’s relative strengths and weaknesses. One measure of technology innovation increasingly used by the policy community is research output. Drawing on CSET’s experiences over the last four years, this post shares our best practices for using research output to study national technological competition and inform public policy.
CSET has received a lot of questions about LLMs and their implications. But questions and discussions tend to miss some basics about LLMs and how they work. In this blog post, we ask CSET’s NLP Engineer, James Dunham, to help us explain LLMs in plain English.
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