Holistic Evaluation of Large Language Models for Medical Applications
Medical and AI experts build a benchmark for evaluation of LLMs grounded in real-world healthcare needs.
Stanford HAI joined global leaders to discuss the balance between AI innovation and safety and explore future policy paths.
Trained on a dataset that includes all known living species – and a few extinct ones – Evo 2 can predict the form and function of proteins in the DNA of all domains of life and run experiments in a fraction of the time it would take a traditional lab.
How researchers are working to ensure AI accelerates medical breakthroughs without unintended harm.
The computer scientist will invest in SAIL’s vibrant research community as it builds the future of technical AI.
Stanford researchers uncover the key factors behind successful AI development in the workplace.
Stanford HAI researchers develop a new benchmark suite aimed to test difference awareness in AI models.
Stanford HAI researchers develop a new benchmark suite aimed to test difference awareness in AI models.
Advancing Responsible Healthcare AI with Longitudinal EHR Datasets
Current evaluations of AI models in healthcare rely on limited datasets like MIMIC, lacking complete patient trajectories. New benchmark datasets offer an alternative.
Current evaluations of AI models in healthcare rely on limited datasets like MIMIC, lacking complete patient trajectories. New benchmark datasets offer an alternative.
AI Pioneer Fei-Fei Li Says AI Policy Must Be Based On ‘Science, Not Science Fiction’
Fei-Fei Li, Co-Director of Stanford HAI, outlines “three fundamental principles for the future of AI policymaking” ahead of the AI Action Summit in Paris.
Fei-Fei Li, Co-Director of Stanford HAI, outlines “three fundamental principles for the future of AI policymaking” ahead of the AI Action Summit in Paris.
Fei Fei Li, Co-Director of Stanford HAI, stresses the importance of governance for AI technologies.
Fei Fei Li, Co-Director of Stanford HAI, stresses the importance of governance for AI technologies.
AI’s Fairness Problem: When Treating Everyone the Same is the Wrong Approach
Current generative AI models struggle to recognize when demographic distinctions matter—leading to inaccurate, misleading, and sometimes harmful outcomes.
Current generative AI models struggle to recognize when demographic distinctions matter—leading to inaccurate, misleading, and sometimes harmful outcomes.
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.
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.
The Stanford Institute for Human-Centered AI Welcomes Itaú Unibanco as a New Industry Affiliate
Thirty-two interdisciplinary teams will receive $2.37 million in Seed Research Grants to work toward initial results on ambitious proposals.
Thirty-two interdisciplinary teams will receive $2.37 million in Seed Research Grants to work toward initial results on ambitious proposals.
Stanford HAI faculty joined world leaders and policymakers to discuss AI trends, shifting U.S. policy, the future of diversity efforts, and more.
Stanford HAI faculty joined world leaders and policymakers to discuss AI trends, shifting U.S. policy, the future of diversity efforts, and more.
Erik Brynjolfsson, HAI Senior Fellow and Director of the Stanford Digital Economy Lab, comments on the problems with AI spending.
Erik Brynjolfsson, HAI Senior Fellow and Director of the Stanford Digital Economy Lab, comments on the problems with AI spending.
