All Work Published on Machine Learning
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.
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.
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.
Fareed Zakaria speaks with “Godmother of AI” Fei-Fei Li about her journey as a computer scientist and how it influenced the discovery of modern AI.
Fareed Zakaria speaks with “Godmother of AI” Fei-Fei Li about her journey as a computer scientist and how it influenced the discovery of modern AI.
Six interdisciplinary research teams received a total of $3 million to pursue groundbreaking ideas in the field of AI.
Six interdisciplinary research teams received a total of $3 million to pursue groundbreaking ideas in the field of AI.
With the release of Meta's Llama 3.1, Director of CRFM and Senior Fellow at Stanford HAI Percy Liang comments on the potential audience shifts that could occur from other commercial AI tools to Llama 3.1.
With the release of Meta's Llama 3.1, Director of CRFM and Senior Fellow at Stanford HAI Percy Liang comments on the potential audience shifts that could occur from other commercial AI tools to Llama 3.1.
Scholars develop a new framework that optimizes compound AI systems by backpropagating large language model feedback.
Scholars develop a new framework that optimizes compound AI systems by backpropagating large language model feedback.