Jianxiong Xiao

His company AutoX aims to make self-driving cars more accessible.

Jianxiong Xiao aims to make self-driving cars as widely accessible as computers are today. He’s the founder and CEO of AutoX, which recently demonstrated an autonomous car built not with expensive laser sensors but with ordinary webcams and some sophisticated computer-vision algorithms. Remarkably, the vehicle can navigate even at night and in bad weather.

AutoX hasn’t revealed details of its software, but Xiao is an expert at using deep learning, an AI technique that lets machines teach themselves to perform difficult tasks such as recognizing pedestrians from different angles and in different lighting.

Growing up without much money in Chaozhou, a city in eastern China, Xiao became mesmerized by books about computers—fantastic-sounding machines that could encode knowledge, logic, and reason. Without access to the real thing, he taught himself to touch-type on a keyboard drawn on paper.

The soft-spoken entrepreneur asks people to call him “Professor X” rather than struggle to pronounce his name. He’s published dozens of papers demonstrating clever ways of teaching machines to understand and interact with the world. Last year, Xiao showed how an autonomous car could learn about salient visual features of the real world by contrasting features shown in Google Maps with images from Google Street View.

Bill Liu

His flexible components could change the way people use electronics.

Bill Liu thinks he can do something Samsung, LG, and Lenovo can’t: manufacture affordable, flexible electronics that can be bent, folded, or rolled up into a tube.

Other researchers and companies have had similar ideas, but Liu moved fast to commercialize his vision. In 2012, he founded a startup called Royole, and in 2014 the company—under his leadership as CEO—unveiled the world’s thinnest flexible display. Compared with rival technologies that can be curved into a fixed shape but aren’t completely pliable, Royole’s displays are as thin as an onion skin and can be rolled tightly around a pen. They can also be fabricated using simpler manufacturing processes, at lower temperatures, which allows Royole to make them at lower cost than competing versions. The company operates its own factory in Shenzhen, China, and is finishing construction on a 1.1-million-square-foot campus nearby. Once complete, the facility will produce 50 million flexible panels a year, says Royole.

Liu dreams of creating an all-in-one computing device that would combine the benefits of a watch, smartphone, tablet, and TV. “I think our flexible displays and sensors will eventually make that possible,” he says. For now, users will have to settle for a $799 headset that they can don like goggles to watch movies and video games in 3-D.

Rachel Haurwitz

Overseeing the commercialization of the promising gene-editing method called CRISPR.

Rachel Haurwitz quickly went from lab rat to CEO at the center of the frenzy over CRISPR, the breakthrough gene-editing technology. In 2012 she’d been working at Jennifer Doudna’s lab at the University of California, Berkeley, when it made a breakthrough showing how to edit any DNA strand using CRISPR. Weeks later, Haurwitz traded the lab’s top-floor views of San Francisco Bay for a sub-basement office with no cell coverage and one desk. There she became CEO of Caribou Biosciences, a spinout that has licensed Berkeley’s CRISPR patents and has made deals with drug makers, research firms, and agricultural giants like DuPont. She now oversees a staff of 44 that spends its time improving the core gene-editing technology. One recent development: a tool called SITE-Seq to help spot when CRISPR makes mistakes.

Kathy Gong

Developing new models for entrepreneurship in China.

Kathy Gong became a chess master at 13, and four years later she boarded a plane with a one-way ticket to New York City to attend Columbia University. She knew little English at the time but learned as she studied, and after graduation she returned to China, where she soon became a standout among a rising class of fearless young technology entrepreneurs. Gong has launched a series of companies in different industries. One is Law.ai, a machine-learning company that created both a robotic divorce lawyer called Lily and a robotic visa and immigration lawyer called Mike. Now Gong and her team have founded a new company called Wafa Games that’s aiming to test the Middle East market, which Gong says most other game companies are ignoring.

Tallis Gomes

An “Uber for beauty.”

Tallis Gomes had spent four years as the CEO of EasyTaxi, the “Uber of Brazil,” when he decided in 2015 to aim the same concept in a new direction—the beauty industry.

His on-demand services platform, called Singu, allows customers to summon a masseuse, manicurist, or other beauty professional to their home or office. Scheduling is done by an algorithm factoring in data from Singu and third parties, including location and weather. The professionals see fewer customers than they would in a shop, but they make more money because they don’t have to cover the overhead. Gomes says the algorithm can get a manicurist as many as 110 customers in a month, and earnings of $2,000—comparable to what a lawyer or junior engineer might make.

Gregory Wayne

Using an understanding of the brain to create smarter machines.

Greg Wayne, a researcher at DeepMind, designs software that gets better the same way a person might—by learning from its own mistakes. In a 2016 Nature paper that Wayne coauthored, it was demonstrated that such software can solve things like graph problems, logic puzzles, and tree structures that traditional neural networks used in artificial intelligence can’t.

Wayne’s computing insights play off his interest in connections between neurons in the human brain—why certain structures elicit specific sensations, emotions, or decisions. Now he often repurposes the concepts behind those brain structures as he designs machines.

Michael Saliba

Finding ways to make promising perovskite-based solar cells practical.

Crystalline-silicon panels—which make up about 90 percent of deployed photovoltaics—are expensive, and they’re already bumping up against efficiency limits in converting sunlight to electricity. So a few years ago, Michael S­aliba, a researcher at the Swiss Federal Institute of Technology in Lausanne, set out to investigate a new type of solar cell based on a family of materials known as perovskites. The first so-called perovskite solar cells, built in 2009, promised a cheaper, easier-to-process technology. But those early perovskite-based cells converted only about 4 percent of sunlight into electricity.

Saliba improved performance by adding positively charged ions to the known perovskites. He has since pushed solar cells built of the stuff to over 21 percent efficiency and shown the way to versions with far higher potential.

Olga Russakovsky

Employed crowdsourcing to vastly improve computer-vision system.

“It’s hard to navigate a human environment without seeing,” says Olga Russakovsky, an assistant professor at Princeton who is working to create artificial-intelligence systems that have a better understanding of what they’re looking at.

A few years ago, machines were capable of spotting only about 20 objects—a list that included people, airplanes, and chairs. Russakovsky devised a method, based partly on crowdsourcing the identification of objects in photos, that has led to AI systems capable of detecting 200 objects, including accordions and waffle irons.

Russakovsky ultimately expects AI to power robots or smart cameras that allow older people to remain at home, or autonomous vehicles that can confidently detect a person or a trash can in the road. “We’re not there yet,” she says, “and one of the big reasons is because the vision technology is just not there yet.”

A woman in a field dominated by men, Russakovsky started AI4ALL, a group that pushes for greater diversity among those working in artificial intelligence. While she wants greater ethnic and gender diversity, she also wants diversity of thought. “We are bringing the same kind of people over and over into the field,” she says. “And I think that’s actually going to harm us very seriously down the line.”

If robotics are to become integral and integrated into our lives, she reasons, why shouldn’t there be people of varying professional backgrounds creating them, and helping them become attuned to what all types of people need?

Russakovsky took a rather conventional path from studying mathematics as an undergrad at Stanford, where she also earned a PhD in computer science, to a postdoc at Carnegie Mellon. But, she suggests, “We also need many others: biologists who are maybe not great at coding but can bring that expertise. We need psychologists—the diversity of thought really injects creativity into the field and allows us to think very broadly about what we should be doing and what type of problems we should be tackling, rather than just coming at it from one particular angle.”

Franziska Roesner

Preparing for the security and privacy threats that augmented reality will bring.

hat would hacks of augmented reality look like? Imagine a see-through AR display on your car helping you navigate—now imagine a hacker adding images of virtual dogs or pedestrians in the street.

Franzi Roesner, 31, recognized this challenge early on and is leading the thinking into what security and privacy provisions AR devices will need to protect them, and ourselves. Her research group at the University of Washington created a prototype AR platform that can, for example, block a windshield app from hiding any signs or people in the real world while a car is in motion.

“I’ve been asking the question, ‘What could a buggy or malicious application do?’” she says.

Lorenz Meier

An open-source autopilot for drones.

Lorenz Meier was curious about technologies that could allow robots to move around on their own, but in 2008, when he started looking, he was unimpressed—most systems had not yet even adopted the affordable motion sensors found in smartphones.

So Meier, now a postdoc at the Swiss Federal Institute of Technology in Zurich, built his own system instead: PX4, an open-source autopilot for autonomous drone control. Importantly, Meier’s system aims to use cheap cameras and computer logic to let drones fly themselves around obstacles, determine their optimal paths, and control their overall flight with little or no user input. It has already been adopted by companies including Intel, Qualcomm, Sony, and GoPro.