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BI 045 Raia Hadsell: Robotics and Deep RL

BI 045 Raia Hadsell: Robotics and Deep RL

FromBrain Inspired


BI 045 Raia Hadsell: Robotics and Deep RL

FromBrain Inspired

ratings:
Length:
77 minutes
Released:
Aug 28, 2019
Format:
Podcast episode

Description

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Show notes:



Raia and I discuss her work at DeepMind figuring out how to build robots using deep reinforcement learning to do things like navigate cities and generalize intelligent behaviors across different tasks. We also talk about challenges specific for embodied AI (robots), how much of it takes inspiration from neuroscience, and lots more.



Raia’s website. Follow her on Twitter: @RaiaHadsell Papers relevant to our discussion: Learning to Navigate in Cities without a Map. Overcoming catastrophic forgetting in neural networks. Progressive neural networks. A few Talks: Deep reinforcement learning in complex environments. Progressive Nets & Transfer. The new Neuro-AI conference she's starting with Tony Zador and Blake Richards:From Neuroscience to Artificially Intelligent Systems (NAISys) 
Released:
Aug 28, 2019
Format:
Podcast episode

Titles in the series (100)

Neuroscience and artificial intelligence work better together. Brain inspired is a celebration and exploration of the ideas driving our progress to understand intelligence. I interview experts about their work at the interface of neuroscience, artificial intelligence, cognitive science, philosophy, psychology, and more: the symbiosis of these overlapping fields, how they inform each other, where they differ, what the past brought us, and what the future brings. Topics include computational neuroscience, supervised machine learning, unsupervised learning, reinforcement learning, deep learning, convolutional and recurrent neural networks, decision-making science, AI agents, backpropagation, credit assignment, neuroengineering, neuromorphics, emergence, philosophy of mind, consciousness, general AI, spiking neural networks, data science, and a lot more. The podcast is not produced for a general audience. Instead, it aims to educate, challenge, inspire, and hopefully entertain those interested in learning more about neuroscience and AI.