Atticus Geiger is a researcher broadly interested in causality, cognition, language, and artificial intelligence. Currently, he is the principal investigator of the Pr(Ai)^2R Group, a non-profit organization that develops the theoretical foundations of interpretability research in order to produce better methods for understanding and controlling artificial intelligence.

David Ha is the co-founder of Sakana AI and former Research Scientist at Google Brain. He is interested in studying how intelligence might have emerge from limited resource constraints. At a time when computational resources seem abundant, there is much excitement around scaling up machine learning and training increasingly larger models on bigger datasets. Intelligent life, however, has arisen not from an abundance of resources, but rather from the lack of it. Evolution naturally selects systems that are able to do more with less.

Jacob Steinhardt is an Assistant Professor at the Department of Statistics of UC Berkeley. His goal is to make the conceptual advances necessary for machine learning systems to be reliable and aligned with human values. This includes the following directions: Robustness, reward specification and reward hacking, and scalable alignment. He also consults part-time for Open Philanthropy, he worked at OpenAI, and had been a coach for the USA Computing Olympiad.

Fernanda Viégas is a Gordon McKay Professor of Computer Science at Harvard University, affiliated with the Harvard Business School, and a Sally Starling Seaver Professor at Harvard Radcliffe Institute. She co-leads the Insight and Interaction Lab and is also a Principal Scientist at Google, where she co-founded the PAIR (People + AI Research) initiative and the Big Picture team. Her work focuses on improving human/AI interaction and democratizing AI technology, while incorporating societal expectations and values into system design and evaluation. Originally from Brazil, Dr. Viégas is dedicated to increasing diversity in technological fields and to address underrepresentation of women and minorities in computer science.