Skip to Content

Magic Leap’s Latest Surprise: It’s Working on Robots

In a court filing, the secretive augmented-reality startup mentions a plan involving deep-learning for robotics.

Could secretive augmented reality startup Magic Leap be working on artificial intelligence for robotics, too?

A brief mention in a lawsuit the company filed this week against two former employees indicates as much. In the suit, filed Thursday in U.S. District Court for the Northern District of California, Magic Leap alleges that Gary Bradski—who had been Magic Leap’s senior vice president of advanced perception and intelligence and has long worked in robotics and AI—worked on proprietary technologies for the company and “was aware of and involved in projects and plans that involved deep-learning techniques for robotics.”

Magic Leap has raised over a billion dollars thus far for its technology, which mixes sharp-looking digital imagery with the real world around you, and has said it will be building this technology into a headset (it hasn’t yet released any details about when this might be available or how much it will cost, but Wired recently got to try out such a headset—see “Magic Leap Has a Headset but Its Technology Is Still Mysterious”). The company has said it’s creating a new optical chip that relies on silicon photonics in order to make its augmented-reality images work, so the idea that it’s also poking around in robotics, or at least robotics-related AI, would fit in with its efforts to explore a range of technologies.

Magic Leap spokeswoman Julia Gaynor had no comment on what the company may be doing in the AI robotics space.

The suit alleges that Bradski and another Magic Leap employee, Adrian Kaehler, started working on a new company while they were employed at Magic Leap that is based on confidential information and proprietary technology from Magic Leap.

Gaynor confirmed that Bradski and Kaehler—whom the filing says reported to Bradski and served as Magic Leap’s vice president of special projects—no longer work for the company. Bradski did not immediately return requests for comment.

Keep Reading

Most Popular

Large language models can do jaw-dropping things. But nobody knows exactly why.

And that's a problem. Figuring it out is one of the biggest scientific puzzles of our time and a crucial step towards controlling more powerful future models.

The problem with plug-in hybrids? Their drivers.

Plug-in hybrids are often sold as a transition to EVs, but new data from Europe shows we’re still underestimating the emissions they produce.

Google DeepMind’s new generative model makes Super Mario–like games from scratch

Genie learns how to control games by watching hours and hours of video. It could help train next-gen robots too.

How scientists traced a mysterious covid case back to six toilets

When wastewater surveillance turns into a hunt for a single infected individual, the ethics get tricky.

Stay connected

Illustration by Rose Wong

Get the latest updates from
MIT Technology Review

Discover special offers, top stories, upcoming events, and more.

Thank you for submitting your email!

Explore more newsletters

It looks like something went wrong.

We’re having trouble saving your preferences. Try refreshing this page and updating them one more time. If you continue to get this message, reach out to us at customer-service@technologyreview.com with a list of newsletters you’d like to receive.