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

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[09] Kenneth Stanley - Efficient Evolution of Neural Networks through Complexification

Each episode of The Thesis Review is a conversation centered around a researcher's PhD thesis, giving insight into their history, revisiting older ideas, and providing a valuable perspective on how their research has evolved (or stayed the same) since.