Data, algorithms and models

Forecasting Potential Misuses of Language Models for Disinformation Campaigns—and How to Reduce Risk

Josh A. Goldstein Girish Sastry Micah Musser Renée DiResta Matthew Gentzel Katerina Sedova
| January 2023

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 provide a framework for assessing potential mitigation strategies.

Forecasting Potential Misuses of Language Models for Disinformation Campaigns—and How to Reduce Risk

Josh A. Goldstein Girish Sastry Micah Musser Renée DiResta Matthew Gentzel Katerina Sedova
| January 2023

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 provide a framework for assessing potential mitigation strategies.

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.

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

Will AI Make Cyber Swords or Shields?

Andrew Lohn Krystal Jackson
| August 2022

Funding and priorities for technology development today determine the terrain for digital battles tomorrow, and they provide the arsenals for both attackers and defenders. Unfortunately, researchers and strategists disagree on which technologies will ultimately be most beneficial and which cause more harm than good. This report provides three examples showing that, while the future of technology is impossible to predict with certainty, there is enough empirical data and mathematical theory to have these debates with more rigor.

China’s Advanced AI Research

William Hannas Huey-Meei Chang Daniel Chou Brian Fleeger
| July 2022

China is following a national strategy to lead the world in artificial intelligence by 2030, including by pursuing “general AI” that can act autonomously in novel circumstances. Open-source research identifies 30 Chinese institutions engaged in one or more of this project‘s aspects, including machine learning, brain-inspired AI, and brain-computer interfaces. This report previews a CSET pilot program that will track China’s progress and provide timely alerts.

China’s Industrial Clusters

Anna Puglisi Daniel Chou
| June 2022

China is banking on applying AI to biotechnology research in order to transform itself into a “biotech superpower.” In pursuit of that goal, it has emphasized bringing together different aspects of the development cycle to foster multidisciplinary research. This data brief examines the emerging trend of co-location of AI and biotechnology researchers and explores the potential impact it will have on this growing field.

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

Exploring Clusters of Research in Three Areas of AI Safety

Helen Toner Ashwin Acharya
| February 2022

Problems of AI safety are the subject of increasing interest for engineers and policymakers alike. This brief uses the CSET Map of Science to investigate how research into three areas of AI safety — robustness, interpretability and reward learning — is progressing. It identifies eight research clusters that contain a significant amount of research relating to these three areas and describes trends and key papers for each of them.

AI and Compute

Andrew Lohn Micah Musser
| January 2022

Between 2012 and 2018, the amount of computing power used by record-breaking artificial intelligence models doubled every 3.4 months. Even with money pouring into the AI field, this trendline is unsustainable. Because of cost, hardware availability and engineering difficulties, the next decade of AI can't rely exclusively on applying more and more computing power to drive further progress.