Previous guests on our podcasts - from Tesla, Aurora, Waymo - are building the brains of the cars and trucks of our future. This episode's guest, Professor Cathy Wu, is building the roadways of our future. She is building machine-learning to predict the ideal infrastructure for the world's future mobility, the cost of building this infrastructure, and most importantly, what's the solution that eliminates traffic jams and gridlock forever.

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? Are there infrastructure solutions or software solutions that can help ensure smooth travel and safe roadways as our mode for transportation and delivery evolve? 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." Her past and current research can be found here and you can watch her recent TedXMIT talk here.

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| Host: Pieter Abbeel | Executive Producers: Alice Patel & Henry Tobias Jones | Production: Fresh Air Production

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