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Home » Matt Beane: How Do We Learn to Work with Intelligent Machines? (Transcript)

Matt Beane: How Do We Learn to Work with Intelligent Machines? (Transcript)

In this talk, Matt 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.

Matt Beane – TED Salon: November 2018 TRANSCRIPT

It’s 6:30 in the morning, and Kristen is wheeling her prostate patient into the OR. She’s a resident, a surgeon in training. It’s her job to learn.

Today, she’s really hoping to do some of the nerve-sparing, extremely delicate dissection that can preserve erectile function. That’ll be up to the attending surgeon, though, but he’s not there yet. She and the team put the patient under, and she leads the initial eight-inch incision in the lower abdomen. Once she’s got that clamped back, she tells the nurse to call the attending.

He arrives, gowns up. And from there on in, their four hands are mostly in that patient — with him guiding but Kristin leading the way. When the prostates out (and, yes, he let Kristen do a little nerve sparing), he rips off his scrubs. He starts to do paperwork. Kristen closes the patient by 8:15, with a junior resident looking over her shoulder. And she lets him do the final line of sutures.

Kristen feels great. Patient’s going to be fine, and no doubt she’s a better surgeon than she was at 6:30. Now this is extreme work. But Kristen’s learning to do her job the way that most of us do: watching an expert for a bit, getting involved in easy, safe parts of the work and progressing to riskier and harder tasks as they guide and decide she’s ready.

My whole life I’ve been fascinated by this kind of learning. It feels elemental, part of what makes us human. It has different names: apprenticeship, coaching, mentorship, on the job training. In surgery, it’s called “see one, do one, teach one.” But the process is the same, and it’s been the main path to skill around the globe for thousands of years.

Right now, we’re handling AI in a way that blocks that path. We’re sacrificing learning in our quest for productivity. I found this first in surgery while I was at MIT, but now I’ve got evidence it’s happening all over, in very different industries and with very different kinds of AI.

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