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Kandungan disediakan oleh Google DeepMind and Hannah Fry. Semua kandungan podcast termasuk episod, grafik dan perihalan podcast dimuat naik dan disediakan terus oleh Google DeepMind and Hannah Fry atau rakan kongsi platform podcast mereka. Jika anda percaya seseorang menggunakan karya berhak cipta anda tanpa kebenaran anda, anda boleh mengikuti proses yang digariskan di sini https://ms.player.fm/legal.
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Gaming, Goats & General Intelligence with Frederic Besse

40:22
 
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Manage episode 441766284 series 2532352
Kandungan disediakan oleh Google DeepMind and Hannah Fry. Semua kandungan podcast termasuk episod, grafik dan perihalan podcast dimuat naik dan disediakan terus oleh Google DeepMind and Hannah Fry atau rakan kongsi platform podcast mereka. Jika anda percaya seseorang menggunakan karya berhak cipta anda tanpa kebenaran anda, anda boleh mengikuti proses yang digariskan di sini https://ms.player.fm/legal.

Games are a very good training ground for agents. Think about it. Perfectly packaged, neatly constrained environments where agents can run wild, work out the rules for themselves, and learn how to handle autonomy. In this episode, Research Engineering Team Lead, Frederic Besse, joins Hannah as they discuss important research like SIMA (Scalable Instructable Multiworld Agent) and what we can expect from future agents that can understand and safely carry out a wide range of tasks - online and in the real world.

Further reading:

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
  • Production support: Mo Dawoud
  • Music composition: Eleni Shaw
  • Camera Director and Video Editor: Tommy Bruce
  • 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

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

32 episod

Artwork
iconKongsi
 
Manage episode 441766284 series 2532352
Kandungan disediakan oleh Google DeepMind and Hannah Fry. Semua kandungan podcast termasuk episod, grafik dan perihalan podcast dimuat naik dan disediakan terus oleh Google DeepMind and Hannah Fry atau rakan kongsi platform podcast mereka. Jika anda percaya seseorang menggunakan karya berhak cipta anda tanpa kebenaran anda, anda boleh mengikuti proses yang digariskan di sini https://ms.player.fm/legal.

Games are a very good training ground for agents. Think about it. Perfectly packaged, neatly constrained environments where agents can run wild, work out the rules for themselves, and learn how to handle autonomy. In this episode, Research Engineering Team Lead, Frederic Besse, joins Hannah as they discuss important research like SIMA (Scalable Instructable Multiworld Agent) and what we can expect from future agents that can understand and safely carry out a wide range of tasks - online and in the real world.

Further reading:

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
  • Production support: Mo Dawoud
  • Music composition: Eleni Shaw
  • Camera Director and Video Editor: Tommy Bruce
  • 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

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

32 episod

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