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Nathan Labenz talks to Google Robotics researcher Keerthana Gopalakrishnan (@keerthanpg) about how they train robots at Google, and how robots contextualize their environment and complete tasks through supervised learning. They go deep on integrating language models for commands and how to approach translating them, as well as the importance of robotics safety. Keerthana also shares how we’re using robots to better understand ourselves, personal experiences using AI, and her hopes and fears about AI's impact on society.

This episode provides valuable insights into the current state and potential future of robotics and AI, with Keerthana's expertise offering unique perspectives on these fascinating topics.

(0:00) Preview to episode
(0:51) Sponsor
(1:09) Intro
(5:20) GPT analogy for robotics
(6:51) Domestic robots
(10:53) Description of Google robotics
(15:17) Controlling robots and supervised learning
(18:50) How robots complete tasks
(26:17) Using data to train robots
(30:45) Data operability
(36:02) Integrating language models for commands
(38:58) How to approach translating commands
(42:49) How robots contextualize their environment
(47:02) Approach to robotics safety
(51:02) Tasks that robots can’t/shouldn’t do
(54:08) The hard bounds of robotics
(59:17) Frequency of inference
(1:03:50) Form factor in robotics
(1:08:38) Using robots to better understand ourselves
(1:12:23) Having robot friends and spouses
(1:17:44) Inference and latency
(1:19:17) The Google Robotics team
(1:21:07) AI tools Keerthana uses in her personal life
(1:22:44) Would you get a Neuralink?
(1:24:12) Keerthana's biggest hopes and fears about AI

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Twitter:
@CogRev_Podcast
@keerthanpg (Keerthana)
@labenz (Nathan)

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cognitivervolution.ai
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https://research.google/research-areas/robotics/
https://research.google/teams/brain/