# How to Build AI for the Physical World

Page: https://stenobird.com/podcast/shift-6601531/how-to-build-ai-for-the-physical-world
Text version: https://stenobird.com/podcast/shift-6601531/how-to-build-ai-for-the-physical-world.md
Podcast: [SHIFT](https://stenobird.com/podcast/shift-6601531)
Published: 2026-05-27T04:30:00+00:00
Episode link: https://play.prx.org/listen?ge=prx_3360_a26ca39e-39f8-4241-a78c-4503e80330f6&uf=https%3A%2F%2Fpublicfeeds.net%2Ff%2F3360%2Ffeed-rss.xml
Audio file: https://dts.podtrac.com/redirect.mp3/dovetail.prxu.org/3360/a26ca39e-39f8-4241-a78c-4503e80330f6/SHIFT_E104_Daniela_Rus_SEG_A.mp3
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JSON: https://stenobird.com/v1/public/podcasts/shift-6601531/episodes/how-to-build-ai-for-the-physical-world
Duration seconds: 1512

## Resource

For as far as we’ve come with AI and robotics, there’s still a huge gap when it comes to combining the two. AI excels in the digital space, and in the physical world, robots are often pre-programmed. That’s where physical AI comes in. It’s critical for things that can’t tolerate the kinds of mistakes that are common in today’s statistics based AI, like self driving cars or managing the power grid. In the latest installment of our oral history project, we meet a central figure in these efforts, MIT's Daniela Rus. We Meet: Daniela Rus is the Director of MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Andrew and Erna Viterbi Professor in the Department of Electrical Engineering and Computer Science. Credits: This episode of SHIFT was produced by Jennifer Strong with help from Emma Cillekens. It was mixed by Garret Lang, with original music from him and Jacob Gorski. Art by Meg Marco.

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## Transcript

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