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

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Gemini 2.0 and the evolution of agentic AI with Oriol Vinyals

Google DeepMind - YouTube

Gemini 2.0 and the evolution of agentic AI with Oriol Vinyals