Building a Universal Physical AI Model for Diverse Sensor Data

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The chapter delves into the AI company's model construction, emphasizing the training data sources like cameras, audio, time series, and RF sensors. It highlights the approach of training the model with partner feedback to address specific use cases, focusing on self-driving tech and adapting to different sensor types. The conversation expands on the broad application of AI beyond self-driving cars to industries like manufacturing, discussing the significance of large datasets and contextual information for achieving full automation capabilities.

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Todays show:

Ivan Poupyrev of Archetype AI joins Jason to demo Archetype AI’s product “Newton” and discuss the application of sensors in various industries (2:55). The two also dive into anticipatory interfaces (44:14), the role of sensor technology in AI (31:09), and the potential for robots in various industries (28:12).

Timestamps:

(0:00) Ivan Poupyrev of Archetype AI joins Jason.

(2:55) Demo of Archetype AI's product, Newton, which interprets and processes motion data

(9:22) .Tech Domains - Apply for the Jam Session with JCal contest today at https://jamwithjcal.tech

(10:48) Exploration of potential business model and use cases for Archetype AI

(22:15) OpenPhone - Get 20% off your first six months at https://www.openphone.com/twist⁠

(23:30) Use cases in aviation, military, and automotive industry

(28:12) The connection between robots and artificial intelligence

(30:17) Vanta - Get $1000 off your SOC 2 at http://www.vanta.com/twist

(31:09) The evolution of sensor technology and its role in AI

(44:14) The pace of technological innovation and the future of anticipatory interfaces

(49:00) Future plans for Archetype AI, hiring and partnership opportunities