An extended version of this story appeared on MIT News on October 15, 2018.

MIT has announced a new $1 billion commitment to address the global opportunities and challenges presented by the prevalence of computing and the rise of artificial intelligence (AI). The initiative marks the single largest investment in computing and AI by an American academic institution and will help position the United States to lead the world in preparing for the rapid evolution of computing and AI.

At the heart of this endeavor will be the new MIT Stephen A. Schwarzman College of Computing, made possible by a $350 million foundational gift from the chairman, CEO, and cofounder of Blackstone, a leading global asset manager. Schwarzman is an active philanthropist with a history of supporting education, culture, and the arts, among other causes.

The new MIT Schwarzman College of Computing will be an interdisciplinary hub for work in computer science, AI, data science, and related fields.

The College will:

  • reorient MIT to bring the power of computing and AI to all fields of study at MIT, allowing the future of computing and AI to be shaped by insights from all other disciplines;
  • create 50 new faculty positions located both within the college and jointly with other departments across MIT—nearly doubling MIT’s academic capability in computing and AI;
  • give MIT’s five schools a shared structure for collaborative education, research, and innovation in computing and AI;
  • educate students in every discipline to responsibly use and develop AI and computing technologies to help make a better world; and
  • transform education and research in public policy and ethical considerations relevant to computing and AI.

Through the College, MIT seeks to strengthen its position as a key international player in the responsible and ethical evolution of technologies that are poised to fundamentally transform society. Amid a rapidly evolving geopolitical environment that is constantly being reshaped by technology, the college will have significant impact on our nation’s competitiveness and security.

“As computing reshapes our world, MIT intends to help make sure it does so for the good of all,” says MIT President L. Rafael Reif. “In keeping with the scope of this challenge, we are reshaping MIT. The MIT Schwarzman College of Computing will constitute both a global center for computing research and education, and an intellectual foundry for powerful new AI tools. Just as important, the college will equip students and researchers in any discipline to use computing and AI to advance their disciplines and vice versa, as well as to think critically about the human impact of their work. With uncommon insight and generosity, Mr. Schwarzman is enabling a bold agenda that will lead to a better world. I am deeply grateful for his commitment to our shared vision.”

“There is no more important opportunity or challenge facing our nation than to responsibly harness the power of artificial intelligence so that we remain competitive globally and achieve breakthroughs that will improve our entire society,” Schwarzman says. “We face fundamental questions about how to ensure that technological advancements benefit all—especially those most vulnerable to the radical changes AI will inevitably bring to the nature of the workforce. MIT’s initiative will help America solve these challenges and continue to lead on computing and AI throughout the 21st century and beyond.”

Representing the most significant structural change to MIT since the early 1950s, the College will be celebrated with a major public showcase on February 28, and is slated to open this September. Construction of a signature new building is scheduled to be completed in 2022, with the current site of Building 44, on Vassar Street, identified as a preferred location. Fifty new faculty positions will be created: 25 to be appointed to advance computing in the College, and 25 to be appointed jointly in the College and departments across MIT. A new deanship will be established.

The College will teach students the foundations of computing broadly and provide integrated curricula designed to satisfy the high level of interest in majors that cross computer science with other disciplines. It will seek to enable advances along the full spectrum of research—from fundamental, curiosity-driven inquiry to research on market-ready applications.

It will be a place for teaching and research on relevant policy and ethics to better ensure that the technologies of the future are responsibly implemented in support of the greater good. To advance these priorities, the College will develop new curricula; host forums to engage leaders from business, government, academia, and journalism to shape policies around the ethics of AI; encourage scientists, engineers, and social scientists to collaborate on analysis and research; and offer research opportunities, fellowships, and grants in ethics and AI.

In its pursuit of ethical questions, the College will bring together researchers in a wide range of MIT departments, labs, centers, and initiatives, such as the Department of Electrical Engineering and Computer Science (EECS); the Computer Science and Artificial Intelligence Lab (CSAIL); the Institute for Data, Systems, and Society; the Operations Research Center; The MIT Quest for Intelligence, of which the Center for Brains, Minds and Machines is a signature initiative; the Center for Computational Engineering; and beyond. The MIT Schwarzman College of Computing builds on MIT’s legacy of excellence in computation and the study of intelligence. In the 1950s, MIT professor Marvin Minsky and others created the very idea of artificial intelligence.

Today, EECS is by far the largest academic department at MIT. Forty percent of MIT’s most recent graduating class chose it, or a combination of it and another discipline, as their major. The largest laboratory at MIT is CSAIL, which has its roots in two storied MIT labs: the Artificial Intelligence Lab, established in 1959 to conduct pioneering research across a range of applications, and the Laboratory for Computer Science, established in 1963 to pursue a Department of Defense project for the development of a computer system accessible to a large number of people.

A search is underway for the College’s inaugural dean, conducted by a committee formed by Provost Martin Schmidt SM ’83, PhD ’88. Schmidt is working closely with the chair of the faculty, Susan Silbey, and the dean of the School of Engineering, Anantha Chandrakasan, to define the path forward.

“I am truly excited by the work ahead,” Schmidt says. “The MIT community will give shape and energy to the new College.”


  1. I am happy to see that a separate school(?) is being set up for computing. Maybe course VI can go back to just being ENGINEERING. As a member of the class of ’49 I find it abhorrent that course VI has become a combination of software and hardware. SOFTWARE IS NOT ENGINEERING!!! If I had put in as many eco’s into a piece of hardware as the idiots at Microsoft put into their “releases” I would have soon found myself without a job. When the writers of software learn to follow the same rules as hardware people, you can call them engineers.

  2. Bernard Widrow

    Marvin Minsky made a great contribution to the field of AI, specifically inventing the words artificial intelligence. That’s about it. For some reason, he seemed to hate neural networks. He did everything he could to “de-bunk”the idea of artificial neural networks, having published a book with Seymour Paper to do exactly that. AI with the leadership of Minsky and John McCarthy, the founders of AI, was going nowhere in spite of massive support from Darpa. It is ironic that AI has converted to neural networks, and it is now leading the world’s technology. If Minsky were alive today, I wonder what he would say about all this. I must confess that I have been working in the field of neural networks for about 60 years, having started when I was an assistant prof. at MIT in 1956. I started working on learning concepts at MIT. When I moved to Stanford in 1959, together with my first Ph.D. student, Ted Hoff, we invented the LMS algorithm, which is the most widely used learning algorithm in the world today.It is an adaptive form of least squares Wiener theory which of course was developed at
    MIT. The LMS algorithm is one of the enabling technologies of the internet. It is used in adaptive filtering and artificial neural networks, It is at the foundation of the back propagation algorithm of Paul Werbos. Backpropagation in turn is at the foundation of the present AI, which is now driving the world”s technology. MIT can take some credit for this. At least for what I did, it would not have been possible without my MIT background, SB1951, SM1953, Sc.D.1956.

  3. I agree, thoughtfully, about the Minsky bias. But everyone *wanted* to believe him, he just seemed more visibly than anyone else to be pushing directions that appeared to be converging toward a final formalism capable of adaptive learning but — even more importantly to him — to also be able to *explain* itself. He saw no value in computers acting like humans and being opaque in their reasoning, and that’s why he disfavored neural networks. He didn’t much think one could design formal means for explaining how learning happened in anything but through a modernist means of discrete stepwise refinements. The algorithm had to be transparent at each step, and this seemed so rational that no one could argue otherwise. That’s also why Minsky and Papert defended the theory of perceptrons (which they did not invent, since these were first implemented in the late 1950’s): these were single layer neutral networks, and thus their states were formally observable, and based on the biological neuron. As a later development, multi state perceptrons became feedforward neural networks, and these quickly transcended the limitations that even Minsky and Papert had documented in their book. So it is true that Minsky pushed an ideological agenda based on the transparency principle common to all science: one has to be able to explain one’s results before one can make one’s methodology predictive. But it’s also true that during all the time Minsky was championing more rigid and brittle methods — not only perceptrons, but also frames, and later the society of mind paradigm — neural network research, which at first lacked formalisms for explaining its results methodologically, was in fact evolving and reached that mature stage within thirty years. At this point, neural networks were demonstrating learning quite systematically, but Minsky either chose to ignore the newer developments in the field, or was not able to do so. In the same period, Chomsky saw great changes in linguistic research on every front on which he had written, and continued to engage the field with his writings in response to many scholars of the later methods. It’s difficult to justify Minsky’s silence on neural networks in the last fifteen years when he was vocal on so many related problems. There wasn’t a balanced counterculture at MIT during this time, he was the Bishop and a celebrity with a tremendous ability to be articulate in his inquiry (to the point of being humorously provocative). The new college will hopefully engage many diverse lines of effort, eschewing the hegemony of any singular one with a brilliant charismatic leader, because such persons do hold back the clock, and more visibly so after they pass away. At Harvard, we had many Bishops, perhaps none as powerful as B.F. Skinner, and William James Hall, the psychology department, had dozens of groups running experiments with pigeons in different parts of the building. No one speaks of that embarrassment today. There are cautionary fables everywhere, but the antidote is in the missive: balance is Good.

  4. Brandon Rigney

    I applaud the position of Robert Talmbiras, who commented above on the separation of software and hardware.
    I have had many heated discussions with my son, who studied computer science (at Rice U.), and myself (MIT Course II), about who is an “engineer”.

    I simplistically argued that if he had never had a course in drafting, metallurgy, thermodynamics, physics, or electrical engineering, mechanics, etc. , then you are not an “engineer”.

  5. I tend to agree with Robert Talbiras, Class of 49, Course VI, although perhaps for different reasons. In my opinion, The EECS Dept. is in danger of losing sight of what made it great in the first place. During World War II and the years following, MIT was catapulted in a position of prominence by its legendary work with electromagnetic waves, radar, and electronic communication. Since that time, the crucible for that effort, the MIT Radiation Lab, has been demolished without a trace, and I doubt that any staff or faculty remember it or even know much about it. But it’s what launched MIT into greatness. There is still a great demand for innovation and skill regarding electromagnetic waves, especially in our wireless world. I doubt there is even a single course at MIT about antennas, which have been my professional specialty since graduating in 1969. Further, I’d bet that even a fundamental course in Electromagnetic Waves is a requirement. Let’s not forget our roots! Jeremy Raines, Course VI, ’69, Ph.D. ’74.

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