OffScript with Ge Wang | What Do We (Really) Want from AI in the Research World?
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 humanproblems (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?
We will be looking forward to discussion with the live audience to address:
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?
Watch Event Recording
Related Events
Ashesh Rambachan | From Next-Token Prediction to Automatic Induction of Automata
Sequence data is ubiquitous in economics — job histories in labor economics, diagnosis and treatment sequences in health economics, strategic interactions in game theory. Generative sequence models can learn to predict these sequences well, but their complexity makes it hard to extract interpretable economic insights from their predictions.
.png&w=1920&q=100)
Sequence data is ubiquitous in economics — job histories in labor economics, diagnosis and treatment sequences in health economics, strategic interactions in game theory. Generative sequence models can learn to predict these sequences well, but their complexity makes it hard to extract interpretable economic insights from their predictions.
Caroline Meinhardt, Thomas Mullaney, Juan N. Pava, and Diyi Yang | How Can AI Support Language Digitization and Digital Inclusion?
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.
Matt Beane | Precision Proactivity: Measuring Cognitive Load in Real-World AI-Assisted Work
Systems like ChatGPT and Claude assist billions through proactive dialogue—offering unsolicited, task-relevant information. Drawing on Cognitive Load Theory, we study how cognitive load shapes performance in AI assisted knowledge work.
.png&w=1920&q=100)
Systems like ChatGPT and Claude assist billions through proactive dialogue—offering unsolicited, task-relevant information. Drawing on Cognitive Load Theory, we study how cognitive load shapes performance in AI assisted knowledge work.