Computing Up

Michael Levin (🔗, 🔗, 🔗) is the director of the Allen Discovery Center at Tufts University, and Distinguished Professor of Biology and Vannevar Bush Chair, among several other roles. In this episode he talks with Michael and Dave about computing writ very large indeed, with topics ranging from the meaning of life and agency to the problems of computability theory to the ways Levin's TAME model - Technological Approach to Mind Everywhere (🔗) - envisions a reality full of adaptive machines made of adaptive parts adapting to each other with everything they've got.

Cynthia Rudin, the Earl D. McLean, Jr. Professor of Computer Science, Electrical and Computer Engineering, Statistical Science, Mathematics,and Biostatistics & Bioinformatics at Duke University (🔗, 🔗, 🔗), joins Michael and Dave for a fast and feisty conversation about how to make machines we can understand and control, with high-stakes examples like predicting power failures in New York City.

Vukosi Marivate, Associate Professor of Computer Science and ABSA UP Chair of Data Science at the University of Pretoria (🔗, 🔗, 🔗), joins Michael and Dave for a discussion of AI and machine learning research across Africa and around the world, and the challenges of centralization and efficiency versus diversification at the edge, and what each can learn from the other. [Title card based on image courtesy of Vukosi Marivate]

Andrew Davison is Professor of Robot Vision (🔗) at Imperial College London, and leads the Dyson Robotics Laboratory (🔗). Andrew invented the SLAM algorithm for robot mapping and navigation, and as this fast conversation makes clear, Dave and Michael are both big fans. [Thumbnail based on image courtesy of Andrew Davison]

Reclusive New York Times best-selling author John Twelve Hawks (🔗, 🔗 , 🔗) joins Michael and Dave to discuss problems of the world today and possibilities of the world tomorrow -- including AI risks, technological centralization, machines acting like people and people acting like machines, sex drives for sexbots, and the question of unintended consequences.

Peter Norvig 🔗, who literally (co)wrote the book 🔗 on Artificial Intelligence in the 1990s, talks with Michael and Dave about how the field has changed over the years, AI fairness and ethics, what is a symbol, and much more. [Cover image based on "Peter Norvig in 2019 at the Interval"  🔗 , licensed CC BY-SA 4.0 by Christopher Michel (Cmichel67 🔗 on Wikipedia)]

Oriel FeldmanHall, Brown University assistant professor and director of the Social and Affective Neuroscience Lab (🔗, 🔗), joins Michael and Dave in a wide-ranging discussion starting with what reinforcement learning does and doesn't mean -- and she turns the tables to ask what computer scientists do and don't get wrong about mind and brain and learning in general.

Michael and Dave tackle the big questions and settle two of them: Is Agency A Zero Sum Game? Why Do (Internet of) Things Suck? How Can We Turn Computation Away From Centralization? [Image of ancient Philips Hue Controller operating without internet access, used by permission of Dave the owner]

James Tompkin 🔗, assistant Professor of Computer Science at Brown University 🔗, joins Michael and Dave to talk about visual computing research writ large, with topics ranging from the relevance of traditional computer graphics in the era of machine learning, to differentiable rendering and neural radiance fields, to DALL-E 2 and remixing Hitchcock's "Rear Window" at the Museum of the Moving Image.

Ellie Pavlick, Assistant Professor of Computer Science at Brown University (🔗) and Research Scientist at Google AI (🔗), joins Michael and Dave in a quick discussion of the remarkable new large AI language models. Topics range from what is and isn't known about the models, and by them, to if or how scared should we be of them, to what 'traditional' sciences like linguistics bring to artificial intelligence research and engineering. [Image courtesy of Ellie Pavlick]