Launched in 2019-'20, Schwarzman is MIT's only college, so called because it cuts across the Institute's five schools in a new effort to integrate advanced computing and artificial intelligence into all areas of study. "We want to do two things: ensure that MIT stays at the forefront of computer science, AI research, and education," Huttenlocher says, "and infuse the forefront of computing into disciplines across MIT." He adds that safety and ethical considerations are also critical.
To that end, the college now encompasses multiple existing labs and centers, including the Computer Science and Artificial Intelligence Laboratory (CSAIL), and multiple academic units, including the Department of Electrical Engineering and Computer Science. (EECS -- which was reorganized into the overlapping subunits of electrical engineering, computer science, and artificial intelligence and decision-making -- is now part of both the college and the School of Engineering.) At the same time, the college has embarked on a plan to hire 50 new faculty members, half of whom will have shared appointments in other departments across all five schools to create a true Institute-wide entity. Those faculty members -- two-thirds of whom have already been hired -- will conduct research at the boundaries of advanced computing and AI.
"We want to do two things: ensure that MIT stays at the forefront of computer science, AI research, and education and infuse the forefront of computing into disciplines across MIT."
The new faculty members have already begun helping the college respond to an undeniable reality facing many students: They've been overwhelmingly drawn to advanced computing tools, yet computer science classes are often too technical for nonmajors who want to apply those tools in other disciplines. And for students in other majors, it can be tricky to fit computer science classes into their schedules.
Meanwhile, the appetite for computer science education is so great that nearly half of MIT's undergraduates major in EECS, voting with their feet about the importance of computing. Graduate-level classes on deep learning and machine vision are among the largest on campus, with over 500 students each. And a blended major in cognition and computing has almost four times as many enrollees as brain and cognitive sciences.
"We've been calling these students 'computing bilinguals,'" Huttenlocher says, and the college aims to make sure that MIT students, whatever their field, are fluent in the language of computing. "As we change the landscape," he says, "it's not about seeing computing as a tool in service of a particular discipline, or a discipline in the service of computing, but asking: How can we bring these things together to forge something new?"
The college has been the hub of this experiment, sponsoring over a dozen new courses that integrate computing with other disciplines, and it provides a variety of spaces that bring people together for conversations about the future of computing at MIT.
More than just a nexus for computing on campus, the college has also positioned itself as a broad-based leader on AI, presenting policy briefs to Congress and the White House about how to manage the pressing ethical and political concerns raised by the rapidly evolving technology.
"Right now, digital technologies are changing every aspect of our lives with breakneck speed," says Asu Ozdaglar, SM '98, PhD '03, EECS department head and Schwarzman's deputy dean of academics. "The college is MIT's response to the ongoing digital transformation of our society."
Huttenlocher, who also holds the title of Henry Ellis Warren (1894) Professor of Electrical Engineering and Computer Science and coauthored the book The Age of AI: And Our Human Future with Henry Kissinger and Eric Schmidt, has long been exploring such issues. He started programming computers back in middle school in Connecticut in the 1970s on an ASR 33 teletype machine, and eventually he studied at the University of Michigan as a double major in cognitive psychology and computer science, exploring speech recognition and visual perception. "AI work back then was relatively disconnected from the physical world," he says. "Being interested in the perceptual side of things was kind of an outlier for what was going on in AI then." When he looked at grad schools in the 1980s, only MIT, Carnegie Mellon, and Stanford were doing significant work in AI, he says: "I applied to those three schools and figured if it didn't work out, I'd get a job."