The Data Exchange Podcast: Kenneth Stanley on his work at Uber AI and starting a new group focused on open-endedness at OpenAI.
In this episode of the Data Exchange I speak with Kenneth Stanley, a Senior Research Manager at Uber AI and a Professor at UCF. Ken just announced that starting in June he is starting a new research group focused on open-endedness at OpenAI. He is a pioneer in the field of neuroevolution – a method for evolving and learning neural networks through evolutionary algorithms. Ken and his colleague, Joel Lehman, wrote one of my favorite books on AI aimed at a broad audience: Why Greatness Cannot Be Planned. In this episode we discuss his upcoming move to OpenAI, as well as his recent work on open-ended algorithms.
Our conversation covered:
- Ken’s new position at OpenAI.
- The transition from being a longtime academic researcher to founding and helping lead an industrial research team (Uber AI Labs).
- Open-ended algorithms, specifically his work on POET (Paired Open-Ended Trailblazer) and Enhanced POET.
- Generative Teaching Networks.
- Chris Nicholson: “Next-generation simulation software will incorporate deep reinforcement learning”
- “Enterprise Applications of Reinforcement Learning: Recommenders and Simulation Modeling”
- Edmon Begoli: “Hyperscaling natural language processing”
- Edo Liberty: “How deep learning is being used in search and information retrieval”
- Nir Shavit: “The combination of the right software and commodity hardware will prove capable of handling most machine learning tasks”
- Kenneth Stanley: “Effective mechanisms for searching the space of machine learning algorithms”
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Back when I was still at O’Reilly, I recruited Ken to write these two seminal posts:
- “Neuroevolution: A different kind of deep learning”
- “Open-endedness: The last grand challenge you’ve never heard of”
[Image: Abandoned Fish Processing Pier, Western End of Drakes Beach HDR 1 by Dean Wampler, used with permission.]