December 19, 2017
Motivations and successes from Dr. Vivienne Ming, neuroscientist and entrepreneur
In partnership with the Accelerate Leadership Center, the Tepper School’s Out&Allied student club invited Dr. Vivienne Ming, DC ’06, to address the campus community on her career path and purposeful innovation. Ming, named one of 10 Women to Watch in Tech in 2013 by Inc. Magazine, is a theoretical neuroscientist who has built five companies motivated by a drive to create good for humankind.
“What I want to talk about is why I think innovation fails,” Ming told a crowd of MBA students and university faculty and staff during the Dec. 1 address in Posner Center. “Not the copious research on why innovation is hard and why it’s hard to come up with good ideas, but why successful innovation often makes the problems they’re meant to solve worse.”
One example she shared was a campaign called “Ban the Box.” The movement encouraged employers to remove the box asking job applicants to disclose felony convictions, a factor that does not correlate with performance. She noted that its removal actually had an overwhelmingly negative impact on the employment of black men. “Now, every black man gets treated as though he was convicted of a felony.”
There is a pattern, she said, in which people rely even more strongly on existing biases when they do not have the information they’re looking for. “In the absence of information saying otherwise, the default assumption of the bias in place is, ‘There’s a problem, and I don’t want to take a risk.’”
One of the topics that repeatedly during the presentation was equity and representation in the workforce. She critiqued the proliferation of a philosophy of “meritocracy” across the tech industry. “The more you emphasize meritocracy,” she said, “the more people fall back on their own bias.” Firms already believed they were hiring the best people, and so adding more complexity meant that employers relied more on superficial traits to predict success.
In her presentation, Ming displayed a provocative statement: “Human capital is a toxic asset.” She said, “Twenty years from now, if we’re thinking in terms of human capital, I don’t really know what it’s going to be worth. No one can. ... But the one thing I can virtually guarantee is given the direction we’re headed right now, it will be worth a lot less than we are currently valuing it at.”
Ming predicts that jobs will look very different as automation replaces specialized skills. “Imagine your general practitioner knows everything. They’re picking up on all of your wearables, all of your devices. They’re making diagnoses before you’ve ever walked in their office, if you actually need to. They don’t have to refer you to a neurologist. They pick up a little sonogram with a bunch of deep neural networks built right into it. They run it over you, and it is doing diagnostics, and you are beginning treatment right there,” she said. “Nothing I’ve just described has not been invented.”
In this example, she said, there’s no real need to hire one amazing doctor to handle that kind of work. Instead, a hospital can hire multiple unskilled lab techs to run the diagnostics, which allow the hospital to treat more patients in less time. “That is the overwhelming economic trend in this space: the deprofessionalization through artificial intelligence — through automation in general.”
Much of Ming’s work has involved analyzing data to assist with decision-making and interventions. After her son was diagnosed with Type I diabetes, Ming and her wife, Norma, collected enormous amounts of biofeedback and behavioral data in an effort to predict glucose levels and insulin needs. Norma is a supervisor of research for the San Francisco Unified School District, co-founder (with Vivienne) and director of learning design at Socos, and a visiting scholar at Berkeley. While his doctors were not interested in the information, companies like Medtronic and Eli Lilly were — Medtronic is developing a predictive continuous glucose monitor and Eli Lilly is building an artificial pancreas.
Ming takes any data she collects, any analyses she develops and any tech she produces, and she gives it to companies who can use it. “If I can give it to Johnson & Johnson or Eli Lilly, they can bring it to market much faster than I can,” she said. “The payoff here is that people will be alive.”
“A big part of my career is not that lives are valued in dollars and cents. But unfortunately, many of the people that make these policy decisions, that’s something they understand,” she said. “Quantifying this in a language that everyone can understand is the only way that I have seen that gets people to change.”
Following her remarks, Ming took questions from the audience in attendance. In response to one question about where her moral center comes from, she explored her own background growing up as relatively privileged, and how her own failings helped inspire her devotion to altruism. At a very low point, she found a reason to keep trying: “Live a life that makes other people’s lives better.”