Featuring author Brian Christian
Thursday, March 31 | 4:30 pm
Watson Center, Room A51
60 Sachem Street, New Haven
Please register: https://www.eventbrite.com/e/the-alignment-problem-machine-learning-and-human-values-tickets-308843809147
Yale University’s Wu Tsai Institute and the Schmidt Program on Artificial Intelligence, Emerging Technologies, and National Power will co-host the talk, “The Alignment Problem: Machine Learning and Human Values,” by Brian Christian, an award-winning author and Science Communicator in Residence at the Simons Institute for the Theory of Computing at University of California – Berkeley.
Christian is recognized as a leading authority on artificial intelligence and the ethical challenges associated with emerging technologies. His latest book, “The Alignment Problem: Machine Learning and Human Values,” is a blend of history and on-the-ground reporting, tracing the explosive growth of machine learning and the wide range of resulting risks, opportunities, and unintended consequences. The book is a Los Angeles Times Finalist for Best Science & Technology Book of the Year, and Microsoft CEO Satya Nadella has named it one of the five books that inspired him in 2021.
Christian is the author of the acclaimed bestsellers “The Most Human Human" and "Algorithms to Live By.” His writing has appeared in The New Yorker, The Atlantic, Wired, and The Wall Street Journal, as well as peer-reviewed journals. He holds degrees in computer science, philosophy, and poetry from Brown University and the University of Washington.
The talk will be moderated by John Lafferty, John C. Malone Professor of Statistics & Data Science, and Director of the Wu Tsai Institute’s Center for Neurocomputation and Machine Intelligence at Yale.
Note: I do not include any code examples. I actually like writing code. However, were I to give you examples, (as I do in Goals.html below), I fear that I might stifle your creativity. You all know how to write computer programs. I do not want to prejudice your thoughts. I am confident that you can figure this stuff out and come out with interesting data structures and algorithms. If your code is especially clever or interesting, I may include it in future editions of this course - with attribution of course.
Use polleverywhere to provide examples of the specified goal type. https://pollev.com/slade You may also download the app to your phone. Use the "slade" poll id.
See Goals.html Agents.