exploratory one-year projects, some of which will grow into larger and longer-term research collaborations over time. The lab is organized around four pillars: A.I. algorithms, A.I. applications, physics of A.I. (e.g. advanced hardware for A.I.), and A.I. for shared prosperity (applications and studies of A.I. for social good). The first batch of projects span a broad range of topics within these pillars, from new tools for explainable A.I., to neuroscience-inspired algorithms, to applications of A.I. in healthcare, to quantum computing for A.I., and so on.
X: Are we entering (or at risk of entering) a period of stagnating innovation in A.I.?
DC: The progress enabled by deep learning in the last five to six years has been nothing short of extraordinary, but we are still in the very early stages of A.I. development. Today’s most successful deep learning systems require enormous amounts of carefully labeled data to work well, and even then, they struggle in a number of important ways. For example, we don’t yet know how to build deep learning systems that can operate with only small amounts of training data. This is especially important because many problems (especially in [business]) involve smaller data sets or data sets that aren’t well curated.
I believe that bridging the divide between where we are today and where we need to be will require new ideas, and it will require us to take risks—but I do believe we can get there.
X: Much of your work has centered around the idea that human brain research can help advance A.I., and vice versa. That’s also a central tenet of MIT’s new Intelligence Quest initiative, in which IBM is also involved. What are the opportunities and limitations of this approach?
DC: I personally believe that there is a great deal that we can learn about intelligence by looking at how biological brains work. The only working examples of what we’re trying to build are naturally occurring intelligences, like our own brains. It’s certainly not the only way to advance A.I., and I don’t believe that A.I. will spontaneously emerge from simply studying or copying the brain. But the nexus of computer science, neuroscience, and cognitive science is an incredibly fertile ground of ideas and inspiration in building A.I. systems.
X: IBM helped usher in the current zeitgeist in A.I. with Watson, but it has also been maligned by some as not being all that innovative in A.I., just good at marketing. Is this fair?
DC: I accepted this position because I feel strongly that IBM, as a company, views the MIT-IBM Watson AI Lab as a strategic and positive investment in the broader research community. Our partnership with MIT is an indication of how seriously we take openness and partnership with academia. We are defining a whole new set of goals that extend beyond today’s deep learning, and I hope the research community will judge us based on what we produce and discover.
