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Memory, like identity, can be a slippery thing. Vivienne Ming, now one of America’s most celebrated speakers on artificial intelligence, remembers herself as a girl growing up in Monterey, California, a ruggedly beautiful beach town on the state’s central coast. “I think back to high school and I see myself walking around in a summer dress,” she says. “And then I think: that never happened. I wasn’t that person.”
Before Ming was who she is now — a tall blonde who can hold a Tedx Talk audience rapt as she discusses using deep neural networks to overcome corporate diversity issues — she was Evan Smith, a maths whizz and star kicker for the high school football team, who was secretly uncomfortable in his own skin. “I feel like I had a brother I lost touch with, or perhaps who passed away,” she says, as we sit for an early Sunday lunch at Jean-Georges in London’s Connaught Hotel, sipping fizzy water while the summer sun pours in through the large windows. “I have a lot of sympathy for that person, at the same time that I own [his] failures,” she says. “I’m a sucker for any melodrama about loneliness.”
It’s hard to imagine Ming as lonely. When I first met her, at a conference of chief executives held in South Carolina, she was stunning her audience with her insights on how AI and brain science can help businesses to grow. While she sounded at times like your typical Silicon Valley tech enthusiast, wildly optimistic about the potential for algorithms to make the world a better place, she also did not shrink from the darker side of innovation.
I was particularly struck by her prediction that within a generation we might see widespread early-onset dementia because the apps we use to do everything from navigate us through traffic to select a restaurant are depriving our brains of the exercise they’ve become used to over millions of years.
“There’s research, involving London cabbies, actually, that tells us that navigating through space [without the help of apps] is prophylactic against cognitive decline,” she tells me at the Connaught. “I used Google Maps to get here from my hotel. I use it to get everywhere, and I do it knowing that it’s making me better in the short term but worse in the long.”
The second reason Ming caught my attention back in South Carolina was that she was wearing a killer combo of a great black motorcycle jacket with a jewel-toned sheath dress — edgy, yet professional. So I was bowled over when she started sharing stories about the dual perspective her gender transition had given her. “I remember flying back from a talk I had given on education,” she says, “and there’s this man and I look at him and I say, ‘I think you might be sitting in my seat,’ and he gets out his ticket, and says, ‘You know, you’re right.’ Then he waits a beat and says, ‘You want to sit in my lap?’ And there was a part of me that wanted to be very non-politically correct and say, ‘I think you and I need to have a conversation about my life history and see whether you want to maintain that offer!’ ”
We both laugh, loudly. The CEOs did too.
Cognitive “de-silo-ing” is, to put it mildly, Vivienne Ming’s thing. Evan Smith grew up as the privileged son of an upper-middle-class doctor and his wife in the little valley that John Steinbeck wrote about in his short-story cycle The Pastures of Heaven. But such privilege came with the pressures of perfection; there was plenty of bourgeois entitlement in Smith’s family.
“I was supposed to win a Nobel Prize,” says Ming, deadpan, as we peruse the menu from our cushy window seats. But by high school, gender dysphoria had turned the brilliant science student into a depressive. “By the time I’m off to college,” says Ming, “I’ve got terrible insomnia, a horrible eating disorder, and it’s just really unpleasant being me. And I came from such a wonderful life, this is entirely self-inflicted.”
Smith dropped out of the University of California San Diego, and became homeless, still unaware of what was causing the distress that made him wake up each day wishing he were someone else. “I was living in my car in the rough part of Mountain View — well, actually, there really isn’t a rough part — but there’s an industrial part where you can live in your car without being rousted by the cops.”
Yet even then, there was a spark — in true Californian self-reinvention style, Smith managed to pull himself up by the bootstraps, joining an acquaintance to start a small film production company. The story goes on in this way for some time — a few crappy scripts, a few more dead-end jobs, some suicidal thoughts. But by now the waiter has come, and so we pause to order.
“Choose an adventure!” Ming’s assistant emailed when I asked for her boss’s preferred lunch venue. I explained the Lunch with the FT rule that the subject must select the venue, but Ming has no idea where to eat in London. So I compromise and offer up a few ideas, which is how we end up at Jean-Georges.
I’m the first to admit that the Connaught is hardly an “adventure” — unless perhaps you are a sheltered Middle Eastern princess headed for your first couture fitting (Roland Mouret is just across the street, and Céline, Roksanda, Christian Louboutin and Nicholas Kirkwood are a few steps beyond). The hotel sits in the heart of Mayfair, one of London’s very grandest neighbourhoods, and was formed from two homes owned by the Duke of Westminster. It blends turn-of-the-century (the 19th century, that is) glamour with modern luxe — including inspired cocktails. Ming, who has a diabetic son, avoids high-glycemic index foods (she recently developed an AI-based predictive model for diabetes in her “free time”). We opt for the pea soup. The waiter tells us about all the things we might want to take out of it — croutons, parmesan foam, etc. We hold the line and take the full carbs and fat.
Isn’t it amazing, I remark, how many things people don’t want to eat these days? “I don’t cut anything out of my life entirely,” Ming responds. “That black truffle pizza caught my attention, but I think I’ll have a little bit of fish instead.” Her Dover sole is ordered off the bone.
Evan Smith’s come-to-Jesus moment (possibly the wrong metaphor, since Ming is an atheist) occurred in his twenties when, after a dismal stint working at an abalone farm, he pulled himself together, was accepted at Carnegie Mellon in Pittsburgh on the strength of raw test scores, and began studying neuroscience — not only of human brains, but artificial ones too. Two transformative things happened. First, he saw the potential of AI to enhance human performance, using the likes of prosthetic limbs that connect to the brain’s own neurons. Ming has studied this area as a visiting scholar in theoretical neuroscience at the University of California Berkeley.
Second, he fell in love with Norma Chang, a Harvard graduate who had come to do a PhD in psychology. The two were eventually engaged, even though Smith continued to struggle with feelings of self-loathing that he was beginning to suspect had something to do with gender. In the spring of 2005, Smith told Chang, “I have a deep dark secret — maybe some day I’ll share it.” She wasn’t scared off. Three and a half years later, after taking part in an experiment conducted by another student in which he had to take anti-anxiety medication, he finally came out to Norma: “My secret is that I wish I were a woman.” She stuck with him. They have since married (Ming is an amalgam of their surnames), and had two children — one “the old-fashioned way”, quips Ming, in a tone that conceals more than it reveals, and one with sperm stored before Evan became Vivienne.
This remarkable personal journey is topped off by the fact that the two now work together at Socos Labs, in Berkeley, doing what they call “mad science”. In practice, this means a few different things. Ming consults with public and private entities, including the UN and the California state government, using AI and neuroscience to offer insights into challenges around hiring, workplace development, diversity and education reform.
Ming has done research, using a database of 122m US workers, that shows how conventional hiring measures, which usually home in on credentials from a handful of schools or the impressions taken from one-on-one interviews, have little to do with workplace success. What’s more, many of the existing AI tools that corporations use to promote diversity have algorithmic deficiencies based on the biases of the programmers.
“If I take a deep neural network and throw it at a bunch of hiring data for the last 10 years, it will tell me to hire straight white men from wealthy conservative backgrounds,” she says. “There are many published economics papers about this, peer-reviewed. But in any big data set, there are two types of relationships that the AI can learn from. One is the spurious correlations, like the historical fact that white men hold most jobs. And then you have actual causal relationships such as the fact that successful people are resilient and have a growth mindset, social skills, emotional intelligence, creativity, meta-cognition and so on.”
Ming creates algorithms that help companies select those people more effectively. She has developed bots that trawl the web looking for high-tech programmers who may not even have a degree yet are doing great work. She’s also used AI to tally the “tax on being different”, calculating, for example, that in the technology sector, a Latino worker needs about six years’ more education than a white worker to be considered for the same job — something, given the cost of US tertiary education, that can amount to $500,000 or more.
Our food arrives — Ming’s Dover sole with an Asian-style Béarnaise sauce that is pleasantly spicy, and my wild grains with spring veggies have a refreshing lemon mint sauce. Since we are both feeling virtuous about our main courses, we put in a dessert order too — rhubarb compote for her, and a strawberry and verbena candyfloss for me.
Ming is in high demand. Uber and Netflix have made offers, she says, and Amazon once tried to hire her as a chief scientist, with the aim of running real-time experiments on how “technology could make people’s lives better . . . what I decided in the end is that Jeff [Bezos] and I had different definitions of ‘better’ . . . pretty profoundly different.” How so? “I’ll give an example,” she says. “The company recently put out a press release about a patent they’ve taken out, a little wristband that factory workers would wear, and if they started reaching for the wrong package, it would buzz. And I’m thinking, I would never want to build that sort of thing. I wouldn’t build anything to help the worker in doing that kind of work . . . it’s factory work.”
What she means is that it’s work that will soon be automated. People such as Tesla founder Elon Musk, who worry about the robots turning into Skynet and taking over the world, Terminator-style, are missing the point, she says.
“The only reason he talks about this stuff is because it prevents financial analysts from asking tough questions on quarterly calls.” What we really need to worry about is AI-related labour market disruption, not just for factory workers, but for brain workers too. “I think the global professional middle class is about to be blindsided. She cites a recent competition at Columbia University between human lawyers and their artificial counterparts, in which both read a series of non-disclosure agreements with loopholes in them. “The AI found 95 per cent of them, and the humans 88 per cent,” she says. “But the human took 90 minutes to read them. The AI took 22 seconds.” Game, set and match to the robots.
All this is one reason why Ming is working with firms such as Accenture to figure out how they can retrain staff to do more creative jobs, the kind that incorporate human emotional intelligence with machine IQ so that they won’t have to lay off hundreds of thousands of accountants or back-office sales staff or even lower-level programmers in the future. She’s also trying to push not only her clients but society away from the Silicon Valley model of hiring mainly geeky white men with high IQs and test scores. One example, which comes out of the non-profit arm of Socos, is an AI-driven education app called “Muse”, designed to be used by parents, who are asked to input information on their children and can then track their development milestones in something other than school grades, namely persistence, self-control and the ability to regulate their behaviour.
Her ultimate goal is to use AI and neuroscience to increase the value of skill sets that haven’t been properly valued (or even seen) in the marketplace — which brings me back to Ming herself. A funny, transgender AI expert who bills herself as a “Human Potential Maximiser”, has a lot of brand value, as you might imagine. Ming milks it all. She’s constantly giving speeches and launching disparate projects. The irony — and joy — of her life is that the hidden identity that gave her such pain is now her calling card.
You know, I tell her, you are the first transgender person I’ve ever actually spent time with.
“Oh really,” she says, with a winning smile. If there’s any judgment, she’s reserved it.
Yes, I go on. And you make perfect sense to me. You were the Homecoming King, I say, referring to a high-school graduation picture of Evan Smith. And now, you are the Homecoming Queen.
“Thank you for that wonderful gift!” she says, smiling with delight. Our desserts come and they are over-the-top — bedazzled, tall, voluptuous and expensive. She claps her hands. We both take pictures of the giant candyfloss to show our kids. We are a couple of mums in sheath dresses, talking about technology and humanity.
Rana Foroohar is the FT’s global business columnist
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