Today, I’m joined by Kenneth Stanley, Professor in the Department of Computer Science at the University of Central Florida and senior research scientist at Uber AI Labs.
Subscribe: iTunes / Google Play / Spotify / RSS
Kenneth studied under TWIML Talk #47 guest Risto Miikkulainen at UT Austin, and joined Uber AI Labs after Geometric Intelligence , the company he co-founded with Gary Marcus and others, was acquired in late 2016. Kenneth’s research focus is what he calls Neuroevolution, applies the idea of genetic algorithms to the challenge of evolving neural network architectures. In this conversation, we discuss the Neuroevolution of Augmenting Topologies (or NEAT) paper that Kenneth authored along with Risto, which won the 2017 International Society for Artificial Life’s Award for Outstanding Paper of the Decade 2002 – 2012. We also cover some of the extensions to that approach he’s created since, including, HyperNEAT, which can efficiently evolve very large networks with connectivity patterns that look more like those of the human and that are generally much larger than what prior approaches to neural learning could produce, and novelty search, an approach which unlike most evolutionary algorithms has no defined objective, but rather simply searches for novel behaviors. We also cover concepts like “Complexification” and “Deception”, biology vs computation including differences and similarities, and some of his other work including his book, and NERO, a video game complete with Real-time Neuroevolution. This is a meaty “Nerd Alert” interview that I think you’ll really enjoy.
Thanks to everyone who took the time to enter our #TWIML1MIL listener giveaway! We sent out an email to entrants a few days ago, so please be on the lookout for that. If you haven’t heard from us yet, please reach out to us at email@example.com so that we can get you your swag!
TWIML Online Meetup
The details for our January Meetup are set! Tuesday, January 16, we will be joined by former TWIML guest and Microsoft Researcher Timnit Gebru. Timnit joined us a few weeks ago to discuss her recently released, and much acclaimed paper, “Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States”, and I’m excited that she’s be joining us to discuss the paper, and the pipeline she used to identify 22 million cards in 50 million Google Street View images, in more detail. I’m anticipating a lively discussion segment, in which we’ll be exploring your AI resolutions & predictions for 2018. For links to the paper, or to register for the meetup, or to check out previous meetups, visit twimlai.com/meetup.