Flash Forward is a show about possible (and not so possible) future scenarios. What would the warranty on a sex robot look like? How would diplomacy work if we couldn’t lie? Could there ever be a fecal transplant black market? (Complicated, it wouldn’t, and yes, respectively, in case you’re curious.) Hosted and produced by award winning science journalist Rose Eveleth, each episode combines audio drama and journalism to go deep on potential tomorrows, and uncovers what those futures might re ...
…
continue reading
Inhoud geleverd door The Thesis Review and Sean Welleck. Alle podcastinhoud, inclusief afleveringen, afbeeldingen en podcastbeschrijvingen, wordt rechtstreeks geüpload en geleverd door The Thesis Review and Sean Welleck of hun podcastplatformpartner. Als u denkt dat iemand uw auteursrechtelijk beschermde werk zonder uw toestemming gebruikt, kunt u het hier beschreven proces https://nl.player.fm/legal volgen.
Player FM - Podcast-app
Ga offline met de app Player FM !
Ga offline met de app Player FM !
[09] Kenneth Stanley - Efficient Evolution of Neural Networks through Complexification
MP3•Thuis aflevering
Manage episode 302418436 series 2982803
Inhoud geleverd door The Thesis Review and Sean Welleck. Alle podcastinhoud, inclusief afleveringen, afbeeldingen en podcastbeschrijvingen, wordt rechtstreeks geüpload en geleverd door The Thesis Review and Sean Welleck of hun podcastplatformpartner. Als u denkt dat iemand uw auteursrechtelijk beschermde werk zonder uw toestemming gebruikt, kunt u het hier beschreven proces https://nl.player.fm/legal volgen.
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
…
continue reading
47 afleveringen
MP3•Thuis aflevering
Manage episode 302418436 series 2982803
Inhoud geleverd door The Thesis Review and Sean Welleck. Alle podcastinhoud, inclusief afleveringen, afbeeldingen en podcastbeschrijvingen, wordt rechtstreeks geüpload en geleverd door The Thesis Review and Sean Welleck of hun podcastplatformpartner. Als u denkt dat iemand uw auteursrechtelijk beschermde werk zonder uw toestemming gebruikt, kunt u het hier beschreven proces https://nl.player.fm/legal volgen.
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
…
continue reading
47 afleveringen
Alle afleveringen
×Welkom op Player FM!
Player FM scant het web op podcasts van hoge kwaliteit waarvan u nu kunt genieten. Het is de beste podcast-app en werkt op Android, iPhone en internet. Aanmelden om abonnementen op verschillende apparaten te synchroniseren.