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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.
Despite widespread speculation about artificial intelligence’s impact on the future of work, we lack systematic empirical evidence about how these systems are actually being used for different tasks. Here, we present a novel framework for measuring AI usage patterns across the economy.
Despite widespread speculation about artificial intelligence’s impact on the future of work, we lack systematic empirical evidence about how these systems are actually being used for different tasks. Here, we present a novel framework for measuring AI usage patterns across the economy.
Join HAI Senior Fellow Susan Athey for a conversation with European Commission Executive Vice-President Henna Virkkunen on her leadership shaping Europe’s digital agenda.
Join HAI Senior Fellow Susan Athey for a conversation with European Commission Executive Vice-President Henna Virkkunen on her leadership shaping Europe’s digital agenda.
IBM Synthetic Data Sets (SDS) have been created for use cases in the financial industry.
IBM Synthetic Data Sets (SDS) have been created for use cases in the financial industry.
Does job task automation augment or diminish the value of labor in the tasks that remain? We argue that the answer depends on whether removing tasks raises or reduces the expertise required to perform the bundle of tasks that remain. Because the tasks that are expert in one occupation may serve primarily as supporting tasks in another, automating any given task may simultaneously replace experts in some occupations and augment expertise in others.
Does job task automation augment or diminish the value of labor in the tasks that remain? We argue that the answer depends on whether removing tasks raises or reduces the expertise required to perform the bundle of tasks that remain. Because the tasks that are expert in one occupation may serve primarily as supporting tasks in another, automating any given task may simultaneously replace experts in some occupations and augment expertise in others.
The AI Index, currently in its eighth year, tracks, collates, distills, and visualizes data relating to artificial intelligence.
The AI Index, currently in its eighth year, tracks, collates, distills, and visualizes data relating to artificial intelligence.
Advances in artificial intelligence are rapidly transforming the scientific enterprise, offering powerful new methods for generating hypotheses, designing experiments, and interpreting data. I will discuss the promise of AI to accelerate scientific discovery, sharing examples that illustrate emerging patterns of innovation driven by the rapid scaling of cycles of generation and testing.
Advances in artificial intelligence are rapidly transforming the scientific enterprise, offering powerful new methods for generating hypotheses, designing experiments, and interpreting data. I will discuss the promise of AI to accelerate scientific discovery, sharing examples that illustrate emerging patterns of innovation driven by the rapid scaling of cycles of generation and testing.