Media Mentions

HAI Executive Director Russell Wald speaks about the importance of passing the CREATE AI Act, born from Stanford HAI's National AI Research Resource. 

Ray Perrault, Co-Director of the AI Index Steering Committee, explains the widening gap between the U.S. and China in rankings from the AI Global Vibrancy Tool. 

AI Index Steering Committee Co-Director Ray Perrault comments on the AI Global Vibrancy Tool rankings. 

Stanford HAI co-director Fei-Fei Li says the next frontier in AI lies in advancing spatial intelligence. In this op-ed, she explains how enabling machines to perceive and interact with the world in 3D can unlock human-centered AI applications for robotics, healthcare, education, and beyond.

This year, Stanford HAI celebrated the fifth year since its founding. Looking forward, the institute aims to continue fostering a better understanding of AI's impacts on society.

Rishi Bommasani, Society Lead at HAI's CRFM, discusses where AI is proving most dangerous, why openness is important, and how regulators are thinking about the open-close divide. 

Stanford's RegLab, directed by HAI Senior Fellow Daniel E. Ho, developed an AI model that helped Santa Clara accelerate the process of flagging and mapping restrictive covenants. 

Dan Ho, HAI Senior Fellow and director of the Stanford RegLab, discusses RegLab's AI model that analyzes decades of property records, helping to identify illegal racially restrictive language in housing documents.

Despite huge advancements in machine learning and neural networks, AI systems still depend on human direction. This article references HAI's 2022 conference where attendees were encouraged to rethink AI systems with a “human in the loop” and consider a future where people remain at the center of decision making.

AI expert Gary Marcus references HAI's study showing that LLM responses to medical questions highly vary and are often inaccurate. 

Peter Norvig, Distinguished Education Fellow at the Stanford HAI, comments on how limiting the budget at an AI agent’s disposal as well as transaction times and capabilities can help AI agents “operate safely within defined boundaries."

A team of researchers from Stanford HAI, MIT, and Princeton created the Foundation Model Transparency Index, which rated the transparency of 10 AI companies; each one received a failing grade.