David Eagleman + Pedro Domingos

Chaos is coming to Westworld, so saddle up. Rubin Museum Future Fellow David Eagleman is neuroscientific advisor to the HBO hit series. The fictional (for now) commentary on Silicon Valley’s embrace of AI technology returns Sunday night, April 22. To help prepare us for the start of Season 2—and for imminent futures led by self-programmable machines—Eagleman gets together with machine learning specialist Pedro Domingos (The Master Algorithm). This Brainwave brunch session comes with the option to include a mimosa to help you find your inner Dolores, and a Westworld-themed tour after the program to prepare you for reality.

For a Buddhist-infused take on Westworld, read Rubin curator Elena Pakhoutova’s article in our “Future Is Fluid” issue of Spiral magazine here, or pick up a copy on your next visit to the Museum.

Brainwave is supported in part by public funds from the New York City Department of Cultural Affairs in partnership with the City Council.

About the Speakers

David Eagleman is a neuroscientist and the New York Times bestselling author of Incognito: The Secret Lives of the Brain and Sum: Forty Tales from the Afterlives. He is the writer and host of the Emmy-nominated PBS television series The Brain. Eagleman is an adjunct professor at Stanford University, a Guggenheim fellow, and the director of the Center for Science and Law. He has written for the New York Times, Discover Magazine, The Atlantic, Slate, Wired and many others, and he appears regularly on National Public Radio and BBC.

Pedro Domingos is a professor of computer science at the University of Washington and the author of “The Master Algorithm,” a bestselling introduction to machine learning for a broad audience. He is a winner of the SIGKDD Innovation Award, the highest honor in data science. He is a Fellow of the Association for the Advancement of Artificial Intelligence, and has received a Fulbright Scholarship, a Sloan Fellowship, the National Science Foundation’s CAREER Award, and numerous best paper awards. His research spans a wide variety of topics in machine learning, artificial intelligence, and data science, including scaling learning algorithms to big data, maximizing word of mouth in social networks, unifying logic and probability, and deep learning.