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.
If we do nothing, millions of us are going to hit a brick wall as we try to learn to deal with AI. Let’s go back to surgery to see how.
Fast forward six months. It’s 6:30 a.m. again, and Kristen is wheeling another prostate patient in, but this time to the robotic OR. The attending leads attaching a four-armed, thousand-pound robot to the patient. They both rip off their scrubs, head to control consoles 10 or 15 feet away, and Kristen just watches. The robot allows the attending to do the whole procedure himself, so he basically does. He knows she needs practice.
He wants to give her control. But he also knows she’d be slower and make more mistakes, and his patient comes first. So Kristen has no hope of getting anywhere near those nerves during this rotation. She’ll be lucky if she operates more than 15 minutes during a four-hour procedure. And she knows that when she slips up, he’ll tap a touch screen, and she’ll be watching again, feeling like a kid in the corner with a dunce cap.
Like all the studies of robots and work I’ve done in the last eight years, I started this one with a big, open question: How do we learn to work with intelligent machines? To find out, I spent two and a half years observing dozens of residents and surgeons doing traditional and robotic surgery, interviewing them and in general hanging out with the residents as they tried to learn.
I covered 18 of the top US teaching hospitals, and the story was the same. Most residents were in Kristen’s shoes. They got to “see one” plenty, but the “do one” was barely available. So they couldn’t struggle, and they weren’t learning.
This was important news for surgeons, but I needed to know how widespread it was: Where else was using AI blocking learning on the job? To find out, I’ve connected with a small but growing group of young researchers who’ve done boots-on-the-ground studies of work involving AI in very diverse settings like start-ups, policing, investment banking and online education.
Like me, they spent at least a year and many hundreds of hours observing, interviewing and often working side-by-side with the people they studied. We shared data, and I looked for patterns. No matter the industry, the work, the AI, the story was the same.
Organizations were trying harder and harder to get results from AI, and they were peeling learners away from expert work as they did it. Start-up managers were outsourcing their customer contact. Cops had to learn to deal with crime forecasts without experts support. Junior bankers were getting cut out of complex analysis, and professors had to build online courses without help.
And the effect of all of this was the same as in surgery. Learning on the job was getting much harder. This can’t last McKinsey estimates that between half a billion and a billion of us are going to have to adapt to AI in our daily work by 2030. And we’re assuming that on-the-job learning will be there for us as we try.
Accenture’s latest workers survey showed that most workers learned key skills on the job, not in formal training. So while we talk a lot about its potential future impact, the aspect of AI that may matter most right now is that we’re handling it in a way that blocks learning on the job just when we need it most.