CSET's Director of Strategy and Foundational Research Grants Helen Toner delivered a talk at TED2024 on the importance of developing smart AI policy, even in the face of uncertainty.

Large language models (LLMs), the technology that powers generative artificial intelligence (AI) products like ChatGPT or Google Gemini, are often thought of as chatbots that predict the next word. But that isn't the full story of what LLMs are and how they work. This is the third blog post in a three-part series explaining some key elements of how LLMs function. This blog post explains how AI developers are finding ways to use LLMs for much more than just generating text.

Large language models (LLMs), the technology that powers generative artificial intelligence (AI) products like ChatGPT or Google Gemini, are often thought of as chatbots that predict the next word. But that isn't the full story of what LLMs are and how they work. This is the second blog post in a three-part series explaining some key elements of how LLMs function. This blog post explores fine-tuning—a set of techniques used to change the types of output that pre-trained models produce.

This guide provides a run-down of CSET’s research since 2019 for first-time visitors and long-term fans alike. Quickly get up to speed on our “must-read” research and learn about how we organize our work.

This blog post by CSET’s Executive Director Dewey Murdick explores two different metaphorical lenses for governing the frontier of AI. The "Space Exploration Approach" likens AI models to spacecrafts venturing into unexplored territories, requiring detailed planning and regular updates. The "Snake-Filled Garden Approach" views AI as a garden with both harmless and dangerous 'snakes,' necessitating rigorous testing and risk assessment. In the post, Dewey examines these metaphors and the different ways they can inform approaches to AI governance strategy that balances innovation with safety, all while emphasizing the importance of ongoing learning and adaptability.

AI capabilities are evolving quickly and pose novel—and likely significant—risks. In these rapidly changing conditions, how can policymakers effectively anticipate and manage risks from the most advanced and capable AI systems at the frontier of the field? This Roundtable Report summarizes some of the key themes and conclusions of a July 2023 workshop on this topic jointly hosted by CSET and Google DeepMind.

How can we measure the reliability of machine learning systems? And do these measures really help us predict real world performance? A recent study by the Stanford Intelligent Systems Laboratory, supported by CSET funding, provides new evidence that models may perform well on certain reliability metrics while still being unreliable in other ways. This blog post summarizes the study’s results, which suggest that policymakers and regulators should not think of “reliability” or “robustness” as a single, easy-to-measure property of an AI system. Instead, AI reliability requirements will need to consider which facets of reliability matter most for any given use case, and how those facets can be evaluated.

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