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A Center for Neural Data Science Seminar Series
A Center for Neural Data Science Seminar Series
Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.
Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.
An AI Measurement Science (AIMS) Seminar Series
An AI Measurement Science (AIMS) Seminar Series
Phil Lewis from the National Center for O*NET Development will talk about data infrastructure the US Department of Labor provides, and how AI is used to update it. A panel with Diyi Yang from the Stanford Computer Science Department and DEL’s Erik Brynjolfsson will discuss the opportunities of a new partnership between Stanford and the National Center for O*NET Development.
Phil Lewis from the National Center for O*NET Development will talk about data infrastructure the US Department of Labor provides, and how AI is used to update it. A panel with Diyi Yang from the Stanford Computer Science Department and DEL’s Erik Brynjolfsson will discuss the opportunities of a new partnership between Stanford and the National Center for O*NET Development.
In the last year, AI models have rapidly improved on public benchmarks measuring their ability to carry out research tasks autonomously. Such performance suggests that models may be capable of automating parts of the AI R&D process itself, thereby accelerating the pace of AI progress.
In the last year, AI models have rapidly improved on public benchmarks measuring their ability to carry out research tasks autonomously. Such performance suggests that models may be capable of automating parts of the AI R&D process itself, thereby accelerating the pace of AI progress.
This talk will cover key insights from the third AI100 report, AI Goes Mainstream: The Scaling Era and the Rise of Generative AI, set for release in November 2026. The report was commissioned by the AI100 standing committee and led by study panel chair Michael Wooldridge.
This talk will cover key insights from the third AI100 report, AI Goes Mainstream: The Scaling Era and the Rise of Generative AI, set for release in November 2026. The report was commissioned by the AI100 standing committee and led by study panel chair Michael Wooldridge.
Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.
Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.
Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.
Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.
Please join us for an illuminating investigation of the complex work of social media content creators—their lives, conflicts, and controversies.
Please join us for an illuminating investigation of the complex work of social media content creators—their lives, conflicts, and controversies.
A Center for Neural Data Science Seminar Series
A Center for Neural Data Science Seminar Series
Hear from speakers and attendees at recent Stanford HAI events across research, policy, and education.
This talk develops the proposal that a central – and neglected – ethical challenge for the field of AI is demystification of the techniques and technologies that constitute it
This talk develops the proposal that a central – and neglected – ethical challenge for the field of AI is demystification of the techniques and technologies that constitute it
Join California Supreme Court Justice Cuéllar, who teaches the popular “Regulating AI” course at Stanford, Dan Ho, Associate Director of HAI and professor at the law school and political science, and Terah Lyons, the Founding Executive Director of the Partnership on AI, for a conversation on the law, regulation, and governance of AI! The three will provide a range of perspectives on the promise, challenges, and directions for AI governance.
Join California Supreme Court Justice Cuéllar, who teaches the popular “Regulating AI” course at Stanford, Dan Ho, Associate Director of HAI and professor at the law school and political science, and Terah Lyons, the Founding Executive Director of the Partnership on AI, for a conversation on the law, regulation, and governance of AI! The three will provide a range of perspectives on the promise, challenges, and directions for AI governance.
AI for Healthcare session will feature Marzyeh Ghassemi who targets “Healthy ML” focusing on creating and applying machine learning to understand and improve health. Improving health requires targeting and evidence. Marzyeh tackles part of this puzzle with machine learning. This session will cover some of the novel technical opportunities for machine learning in health challenges and the important progress to be made with a careful application to domain. She will also walk through the danger of applying methods without a robust understanding of the domain, and potential downstream uses.
AI for Healthcare session will feature Marzyeh Ghassemi who targets “Healthy ML” focusing on creating and applying machine learning to understand and improve health. Improving health requires targeting and evidence. Marzyeh tackles part of this puzzle with machine learning. This session will cover some of the novel technical opportunities for machine learning in health challenges and the important progress to be made with a careful application to domain. She will also walk through the danger of applying methods without a robust understanding of the domain, and potential downstream uses.
Recent advances of artificial intelligence and deep learning have been undoubtedly driven by a large amount of data amassed over the years, helping firms, researchers, and practitioners achieve many amazing feats, most notably in recognition tasks often surpassing human ability in several benchmarks.
Recent advances of artificial intelligence and deep learning have been undoubtedly driven by a large amount of data amassed over the years, helping firms, researchers, and practitioners achieve many amazing feats, most notably in recognition tasks often surpassing human ability in several benchmarks.
AI promises to transform how government agencies work. Where will it have the biggest impact? What are some challenges around transparency, privacy, bias, and accountability? This talk will go beyond the headlines and share highlights of a just-completed report on AI in the US Government.
AI promises to transform how government agencies work. Where will it have the biggest impact? What are some challenges around transparency, privacy, bias, and accountability? This talk will go beyond the headlines and share highlights of a just-completed report on AI in the US Government.
How can AI and machine learning be leveraged to mitigate the impact of human activities on earth’s natural systems? Learn about data science tools and strategies being used to safeguard our water supply, feed the worldwide human population, and promote greater biodiversity and global sustainability.
How can AI and machine learning be leveraged to mitigate the impact of human activities on earth’s natural systems? Learn about data science tools and strategies being used to safeguard our water supply, feed the worldwide human population, and promote greater biodiversity and global sustainability.
Bias in government automated decision systems, the future of farmwork, digital literacy, algorithms in bail decisions, and more.
Bias in government automated decision systems, the future of farmwork, digital literacy, algorithms in bail decisions, and more.
The content shared on social media is among the largest data sets on human behavior in history. I leverage this data to address questions in the psychological sciences. Specifically, I apply natural language processing and machine learning to characterize and measure psychological phenomena with a focus on mental and physical health.
The content shared on social media is among the largest data sets on human behavior in history. I leverage this data to address questions in the psychological sciences. Specifically, I apply natural language processing and machine learning to characterize and measure psychological phenomena with a focus on mental and physical health.