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.

Machine learning advances have powered the development of new and more powerful generative language models. These systems are increasingly able to write text at near human levels. In a new report, authors at CSET, OpenAI, and the Stanford Internet Observatory explore how language models could be misused for influence operations in the future, and they provide a framework for assessing potential mitigation strategies.

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

CSET's Catherine Aiken testified before the National Artificial Intelligence Advisory Committee on measuring progress in U.S. AI research and development.

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

In an opinion piece for The Diplomat, Ellen Lu and CSET's Ryan Fedasiuk examine whether China's new data regulations will hamper AI ambitions.

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.

In their op-ed for Scientific American, Husanjot Chahal and Helen Toner argue how small data can assist AI breakthroughs.

The transfer of national security relevant technology—to peer competitors especially—is a well-documented problem and must be balanced with the benefits of free exchange. The following propositions covering six facets of the transfer issue reflect CSET’s current recommendations on the matter.

CSET's Ryan Fedasiuk discusses China's use of technology companies to strengthen the government's authority over personal data under the Personal Information Protection Law.