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Posted Tuesday February 5, 2019


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Implications

How do we learn to work with intelligent machines?

Matt Beane is an Assistant Professor in the Technology Management Program at the University of California, Santa Barbara and a Research Affiliate with MIT's Institute for the Digital Economy.

Matt Beane does field research on work involving robots to help us understand the implications of intelligent machines for the broader world of work. Any of his projects mean many hundreds of hours -- sometimes years -- watching, interviewing and often working side by side with people trying to work with robots to get their jobs done.

Beane has studied robotic surgery, robotic materials transport and robotic telepresence in healthcare, elder care and knowledge work. He has published in top management journals such as Administrative Science Quarterly, he was selected in 2012 as a Human Robot Interaction Pioneer and is a regular contributor to popular outlets such as Wired, MIT Technology Review, TechCrunch, Forbes and Robohub. He also took a two-year hiatus from his doctoral studies to help found and fund Humatics, an MIT-connected, full-stack IoT startup.

Beane is an Assistant Professor in the Technology Management Program at the University of California, Santa Barbara and a Research Affiliate with MIT's Institute for the Digital Economy. He received his PhD from the MIT Sloan School of Management.

The path to skill around the globe has been the same for thousands of years: train under an expert and take on small, easy tasks before progressing to riskier, harder ones. But right now, we're handling AI in a way that blocks that path -- and sacrificing learning in our quest for productivity, says organizational ethnographer Matt Beane. What can be done? Beane shares a vision that flips the current story into one of distributed, machine-enhanced mentorship that takes full advantage of AI's amazing capabilities while enhancing our skills at the same time.

09:50 minutes · TED Salon: The Next Wave

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