BIDS Machine Learning and Science Forum
Date: Monday, January 24, 2022
Time: 11:00 AM - 12:00 PM Pacific Time
Location: Participate remotely using this Zoom link
AV2.0: Learning a Globally Scalable Driving Intelligence
Speaker: Alex Kendall, Co-founder and CEO, Wayve
Abstract: In this talk, I'll discuss a new approach to autonomous driving -- moving away from an expensive array of sensors, HD maps and rules-based control strategies -- to leveraging end-to-end deep learning. I'll show our pioneering work at Wayve, from the first ever reinforcement learning agent on an autonomous vehicle in 2018, to a generalised driving intelligence able to drive across multiple cities in 2021. Autonomous driving has unique challenges compared to other big-data problems, and I'll finish by discussing some grand challenges to motivate future work.
The BIDS Machine Learning and Science Forum meets biweekly to discuss current applications across a wide variety of research domains in the physical sciences and beyond. Hosted by BIDS Affiliates Uroš Seljak (professor of Physics at UC Berkeley) and Ben Nachman (physicist at Lawrence Berkeley National Laboratory), these active sessions bring together domain scientists, statisticians, and computer scientists who are either developing state-of-the-art methods or are interested in applying these methods in their research. To receive email notifications about upcoming meetings, or to request more information, please contact the organizers at firstname.lastname@example.org. All interested members of the UC Berkeley and Berkeley Lab communities are welcome and encouraged to attend.
Alex Kendall is the CEO of Wayve, a start-up pioneering AV2.0: the next-generation approach to autonomous driving. Alex has raised over $258m and was named on the 2020 Forbes 30 Under 30 list for contributions to technology entrepreneurship. Wayve was the first team to deploy autonomous vehicles on public roads with end-to-end deep learning. Alex was a Research Fellow at Trinity College at the University of Cambridge, where he completed his PhD as a Woolf Fisher Scholar.