Gemini 2.0 and the evolution of agentic AI | Oriol Vinyals

In this episode, Hannah is joined by Oriol Vinyals, VP of Drastic Research and Gemini co-lead. They discuss the evolution of agents from single-task models to more general-purpose models capable of broader applications, like Gemini. Vinyals guides Hannah through the two-step process behind multi modal models: pre-training (imitation learning) and post-training (reinforcement learning). They discuss the complexities of scaling and the importance of innovation in architecture and training processes. They close on a quick whirlwind tour of some of the new agentic capabilities recently released by Google DeepMind. Note: To see the full demos, unedited versions, and other videos related to Gemini 2.0 head to our Gemini playlist: https://www.youtube.com/playlist?list=PLqYmG7hTraZD8qyQmEfXrJMpGsQKk-LCY Timecodes 00:00 Intro 02:30 Games and early AI agents 04:28 Weights 09:27 Architectures and the digital brain 10:24 Agentic behaviour 13:31 Digital body 14:09 Scaling 19:02 Data 20:59 Complex understanding and knowledge 25:14 Post training challenges 30:43 Reasoning 33:11 Planning 34:19 Systems 2 37:00 Memory 40:54 Gemini and agentic capabilities Additional learning: https://deepmind.google/ https://www.youtube.com/watch?v=lH74gNeryhQ& https://youtu.be/64pndvbbokA?si=O9Ep7fD5eF5YUNYe Thanks to everyone who made this possible, including but not limited to: Presenter: Professor Hannah Fry Series Producer: Dan Hardoon Editor: Rami Tzabar, TellTale Studios Commissioner & Producer: Emma Yousif Music composition: Eleni Shaw Camera Director and Video Editor: Bernardo Resende Audio Engineer: Perry Rogantin Video Studio Production: Nicholas Duke Video Editor: Bilal Merhi Video Production Design: James Barton Visual Identity and Design: Eleanor Tomlinson Commissioned by Google DeepMind — Subscribe to our channel https://www.youtube.com/@UCP7jMXSY2xbc3KCAE0MHQ-A Find us on X https://twitter.com/GoogleDeepMind Follow us on Instagram https://instagram.com/googlede