The Importance of Adaptability in AI for Robotics

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The suction of vacuum has become a useful technology in warehouses, but different tasks require different robot hands. AI for robotics needs to be adaptable to different physical bodies and scenarios. This leads to the concept of a covariant brain, a foundation model for robots that can learn across multiple tasks and hardware. By collecting all the necessary data together, this model can solve the data problem and accelerate progress in robotics. This creates a proprietary advantage for companies like Amazon over competitors like Target and Walmart.

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Today’s show:

Covariant CEO Peter Chen joins Jason to discuss the future of AI in robotics (1:33), the key concepts of reinforcement learning (13:50), and much more!

Time stamps:

(0:00) Covariant CEO Peter Chen joins Jason

(1:33) AI’s role in robotics and its value in e-commerce warehouses

(7:38) Lemon.io - Get 15% off your first 4 weeks of developer time at https://Lemon.io/twist

(8:59) Reinforcement learning today and the AlphaGo moment

(13:50) The 2 key concepts of reinforcement learning

(17:35) Approaches to accessing data for AI in robotics

(20:35) Robotics hardware

(22:50) Vanta - Get $1000 off your SOC 2 at https://vanta.com/twist

(23:57) Covariant's hardware and software in use

(27:32) The importance of adaptability

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