By Matt Hartman
Before the promises of artificial intelligence can happen, the theoretical problems with machine learning algorithms must be solved. Fortunately, Duke University Assistant Professor of Computer Science Rong Ge has been making headway on them. In recognition of that work, he has been awarded with a prestigious Sloan Research Fellowship.
Awarded to 126 scholars each year, the Sloan Fellowships provide support to promising early-career scientists and researchers in the United States and Canada. Candidates are nominated by their peers, and the winners are selected by a panel of senior scholars on the basis of their accomplishments and potential to become leaders in their fields.
“Sloan Research Fellows are the best young scientists working today,” says Adam F. Falk, president of the Alfred P. Sloan Foundation, which awards the fellowships. “Sloan Fellows standout for their creativity, for their hard work, for the importance of the issues they tackle, and the energy and innovation with which they tackle them. To be a Sloan Fellow is to be in the vanguard of twenty-first century science.”
“The award means a lot to me,” Ge says. “I’m happy that people like the work I’ve been doing. There are a lot of open problems in it still, and I’m just hoping to continue working on them.”
Ge’s research focuses on how “neural networks” are trained. These networks are essential to machine learning; they are what allow machines to make decisions about new cases without human input. Facial recognition technology is one example. In order to determine whether a photo includes a human face, much less identify whose face it is, the machine needs a framework for analyzing the photo. The neural network provides that framework.
But before that can happen, the neural network must be uncovered. Which way of organizing the network, from all the countless possibilities, will allow the machine to generate the desired prediction about a new case? In facial recognition, the machine could take into account the colors of the photo, the size and shape of the photographed objects, the angle the photo was taken, and more. To guide the machine, computer scientists typically use manually coded examples, providing millions of photos labeled with specific information (like “includes a human face” and “does not include a human face”). As the machine sees more examples, it can create a more accurate neural network.
Finding the right parameters for the network so that it makes the best prediction is an optimization problem. “You want to find the best set of parameters given the data you have,” Ge says. He explains that most research on the issue focuses on a special kind of optimization problem called convex optimization. But machine learning is a non-convex optimization problem, which is more complicated because there can be more than one optimal solution.
In practice, however, very simple algorithms can solve these very complex problems. Ge’s work is focused on understanding why that is the case. Summarizing his work, Ge asks, “Why do we have a problem that is theoretically very difficult that we should not be able to solve, but in practice is solved by a very simple algorithm?”
While he has not yet discovered a complete solution to the dilemma, Ge has found why it works in some cases, like Amazon or Netflix recommendations. In those cases, even though the optimization problems are non-convex, all of the different optimal solutions are equally good, so it doesn’t matter which you find.
Going forward, Ge’s work will focus on expanding our understanding of these problems. Sloan Fellows receive a two-year, $70,000 award to fund their research, which Ge says he will use to hire a postdoctoral researcher to help apply his findings to more complicated forms of machine learning.
“We will first try to understand why the current algorithms are working so well, and then hopefully we can design new algorithms that work even better,” he says.
If he succeeds, he will help open the pathway to even more advanced kinds of artificial intelligence technology, like self-driving cars and personalized medicine.