Olga Russakovsky, 31
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.”
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