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Welcome to Stanford Human-Centered AI Vodcast, where we don't sweep anything under the rug; where we question everything, starting with ourselves.
What does digital inclusion look like in the age of AI? Over 6,000 of the world’s 7,000-plus living languages remain digitally disadvantaged.
What does digital inclusion look like in the age of AI? Over 6,000 of the world’s 7,000-plus living languages remain digitally disadvantaged.
AI+Science: Accelerating Discovery is an interdisciplinary conference bringing together researchers across physics, mathematics, chemistry, biology, neuroscience, and more to examine how AI is reshaping scientific discovery. Experts will separate hype from reality, spotlighting where AI is already enabling genuine breakthroughs and where its limits and risks remain.
AI+Science: Accelerating Discovery is an interdisciplinary conference bringing together researchers across physics, mathematics, chemistry, biology, neuroscience, and more to examine how AI is reshaping scientific discovery. Experts will separate hype from reality, spotlighting where AI is already enabling genuine breakthroughs and where its limits and risks remain.
While Large Language Models (LLMs) show promise in many domains, relying on them for direct policy generation in games often results in illegal moves and poor strategic play.
While Large Language Models (LLMs) show promise in many domains, relying on them for direct policy generation in games often results in illegal moves and poor strategic play.
Through this series, we hope to better understand what all of you want from AI. So, all of you in the audience, be prepared to engage, discuss and share. We want to hear about your experience and perspectives.
In this episode, Ge and Vanessa discussed with Stanford Graduate School of Education Professors, Sarah Levine and Nick Haber what we (REALLY) want from AI in early childhood education.
What do we (really) want from artificial intelligence?
We live in a time when advancements in AI technology is shaping our world, while critically outpacing our understanding of this technology in various humanistic contexts (cultural, social, ethical, historical).
Look at us, we are Stanford, one of the most powerful academic institutions, located in the heart of Silicon Valley. And yet it is all too easy to be in a profound bubble. Much of the world knows and cares about AI far less than we might assume. It is all too easy, also, to be sure of ourselves, as the technology creators, while remaining out of touch with the rest of the world. We tell ourselves that more technology is the solution—for technology is what we know, and we are eager to apply our craft. Unfortunately, it is all too easy to do so with a shallow understanding of the social, cultural, historical contexts—while not even considering the possibility that problems in the world are seldom “lack-of-technology” problems, but entrenched human problems (including technology itself). But of course, we keep moving fast because that is good for business. Even when we “design tech for social good”, we too often just end up making something slightly more convenient, because slightly more convenient fits the prevailing economic narrative. This is the bubble, the technology cave we don’t know we are living in.
We need to interrogate ourselves to better understand how we as individuals and as communities would want to live with AI technology—and through our creations how we would want to live with one another. We will seek distinctions between intelligence and wisdom. (working definitions: “Intelligence—having the means to achieve what you desire. Wisdom—having the capacity to assess your desires in the first place, and to assess the means to achieve them.”) So we ask again: what do we (really) want from it all?
And above all, what does it mean to do AI with heart and compassion?
What do you (really) want from AI in the real world? —In your world?
How do we want to live with our technologies?
Through our technologies, how do we want to live with one another?
What are the foregone premises in AI that we could re-think?
