Artificial Intelligence has suddenly gone from the fringes of science to being everywhere. So how did we get here? And where's this all heading? In this new series of Science Friction, we're finding out.
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[09] Kenneth Stanley - Efficient Evolution of Neural Networks through Complexification
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Manage episode 302418436 series 2982803
Content provided by The Thesis Review and Sean Welleck. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by The Thesis Review and Sean Welleck or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
Kenneth Stanley is a researcher at OpenAI, where he leads the team on Open-endedness. Previously he was a Professor Computer Science at the University of Central Florida, cofounder of Geometric Intelligence, and head of Core AI research at Uber AI labs. His PhD thesis is titled "Efficient Evolution of Neural Networks through Complexification", which he completed on 2004 at the University of Texas. We talk about evolving increasingly complex structures and how this led to the NEAT algorithm that he developed during his PhD. We discuss his research directions related to open-endedness, how the field has changed over time, and how he currently views algorithms that were developed over a decade ago. Episode notes: https://cs.nyu.edu/~welleck/episode9.html Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter, and find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html Support The Thesis Review at www.buymeacoffee.com/thesisreview
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47 episodes
MP3•Episode home
Manage episode 302418436 series 2982803
Content provided by The Thesis Review and Sean Welleck. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by The Thesis Review and Sean Welleck or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
Kenneth Stanley is a researcher at OpenAI, where he leads the team on Open-endedness. Previously he was a Professor Computer Science at the University of Central Florida, cofounder of Geometric Intelligence, and head of Core AI research at Uber AI labs. His PhD thesis is titled "Efficient Evolution of Neural Networks through Complexification", which he completed on 2004 at the University of Texas. We talk about evolving increasingly complex structures and how this led to the NEAT algorithm that he developed during his PhD. We discuss his research directions related to open-endedness, how the field has changed over time, and how he currently views algorithms that were developed over a decade ago. Episode notes: https://cs.nyu.edu/~welleck/episode9.html Follow the Thesis Review (@thesisreview) and Sean Welleck (@wellecks) on Twitter, and find out more info about the show at https://cs.nyu.edu/~welleck/podcast.html Support The Thesis Review at www.buymeacoffee.com/thesisreview
…
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47 episodes
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