Get the latest news, advances in research, policy work, and education program updates from HAI in your inbox weekly.
Sign Up For Latest News
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.
Clinical Mind AI is a Stanford-developed, international collaborative platform designed to strengthen clinical reasoning education through AI-simulated patient encounters across health professions.

Clinical Mind AI is a Stanford-developed, international collaborative platform designed to strengthen clinical reasoning education through AI-simulated patient encounters across health professions.
Much of the discourse around AI frames it as an alien and inscrutable intelligence. But in fact our success at creating "real" AI with general capabilities in the 2020s has come not only by taking many pages from computational neuroscience, but by performing large-scale modeling on human language-- the "DNA" of our collective intelligence. Intelligence does not equate with or arise from optimal performance at any particular task; neither does it arise in isolation, or against the background of a static game-like environment. Rather, it arises through the social modeling of interactions with other intelligent entities.
This talk will explore the deep (and largely positive) implications of reframing intelligence in terms of social modeling, intersubjectivity, and symbiosis. These include new perspectives on life and evolution, the relationship between humans and technology, and even the nature of free will and consciousness.