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Google DeepMind and Hannah Fry에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Google DeepMind and Hannah Fry 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.
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Gemini 2.0 and the evolution of agentic AI with Oriol Vinyals

49:25
 
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Manage episode 455162396 series 2532352
Google DeepMind and Hannah Fry에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Google DeepMind and Hannah Fry 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.

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 all of the full length demos, including unedited versions, and other videos related to Gemini 2.0 head to YouTube.

Future reading/watching:

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 YouTube channel

Find us on X

Follow us on Instagram

Add us on Linkedin

Please like and subscribe on your preferred podcast platform. Want to share feedback? Or have a suggestion for a guest that we should have on next? Leave us a comment on YouTube and stay tuned for future episodes.

  continue reading

31 에피소드

Artwork
icon공유
 
Manage episode 455162396 series 2532352
Google DeepMind and Hannah Fry에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Google DeepMind and Hannah Fry 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.

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 all of the full length demos, including unedited versions, and other videos related to Gemini 2.0 head to YouTube.

Future reading/watching:

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 YouTube channel

Find us on X

Follow us on Instagram

Add us on Linkedin

Please like and subscribe on your preferred podcast platform. Want to share feedback? Or have a suggestion for a guest that we should have on next? Leave us a comment on YouTube and stay tuned for future episodes.

  continue reading

31 에피소드

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