Previous guests on our podcasts - from Tesla, Aurora, to Waymo - are building the brains of the cars and trucks of our future. This episode's guest, Professor Cathy Wu, is helping build the roadways of our future with machine-learning to predict the ideal infrastructure for the world's future mobility, the cost of building this infrastructure, and the age-old question, "How do we eliminate traffic jams?"
Currently at MIT's Institute for Data, Systems, and Society (IDSS), Professor Cathy Wu (and previous student of Pieter Abbeel's) gives listeners an overview of the type of potential scenarios being modeled with machine-learning such as scenarios in which the road is filled with mixed-autonomy vehicles. What emergent behaviors might happen? As our mode for transportation and delivery evolve, what are the infrastructure or software solutions that can help ensure smooth travel and safe roadways? What are the policy considerations?
Throughout the talk, Wu cites building reinforcement learning for her work and why it's the right fit her research, "Reinforcement learning is essentially this paradigm at the intersection of machine learning and also control, and it is essentially about how agents learn from experience and in particular through trial and error."
What's in this episode:
00:00:00 - Introduction
00:02:09 - Her undergrad path to where she is now
00:06:30 - Why studying next-generation transportation models is important
00:10:03 - What does autonomy mean for traffic jams?
00:18:30 - Her motivation for her work
00:23:00 - Why use reinforcement learning?
00:29:27 - The future of our world's highways
00:34:21 - Advice for students
Links:
MIT's IDSS: idss.mit.edu/
Website: wucathy.com/
Recent interview: news.mit.edu/2021/qa-cathy-wu...
TEDxMIT talk: • How Cities Can Reshape...
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Host: Pieter Abbeel
Executive Producers: Alice Patel & Henry Tobias Jones
Production: Fresh Air Production