AI at MIT

At MIT, AI has become so pervasive that you can almost find your way into it without meaning to. Take Sili Deng, an associate professor of mechanical engineering. Deng says she still doesn’t know whether she’d have gone all in on artificial intelligence had it not been for the covid pandemic. She had joined the faculty in 2019 and was in the process of setting up her lab to study combustion kinetics, emissions reduction, and flame synthesis of energy materials when covid hit, putting a halt to all lab renovations. Because she needed to start from scratch, she challenged herself and her postdocs to try machine learning “and see, with the fundamental knowledge we have on the combustion side, what are the gaps that we think machine learning could [fill].” Under her leadership, Deng’s Energy and Nanotechnology Group used AI to develop a “digital twin” that mirrors the performance of an energy/flow device—a digital replica of a physical system. Eventually, this model should be able to predict and control the workings of fuel combustion systems in real time. Unlike Deng, who came to AI through the slings and arrows of outrageous fortune, Zachary Cordero, an associate professor of aero-astro, began using AI thanks to a colleague’s expertise. In 2024 John Hart, head of the Department of Mechanical Engineering, suggested that Cordero, who develops novel materials and structures for emerging aerospace applications, meet with Faez Ahmed, an associate professor of mechanical engineering and an expert in machine learning and optimization for engineering design. Cordero says he hadn’t previously been pursuing AI-related research: “This is all totally new to me.” Working with Ahmed and other collaborators on a project sponsored by the US Defense Advanced Research Projects Agency (DARPA), Cordero developed an AI tool that can optimize the material composition of what’s known as a blisk—a bladed disk that’s a key component in jet and rocket turbine engines. Their work aims to improve engine performance and longevity and could lead to more reliable reusable rocket engines for heavy-lift launch vehicles. Cordero says the AI system augmented human intuition—even “on problems where it’s almost impossible to have intuition.” Professor Ju Li posits that if AI is given autonomy to do experiments, to try different things and fail and learn from that, it could evolve into something very similar to human intelligence. Stories like these abound at MIT. In every department, in almost every lab on campus, AI technologies such as machine learning, large language models, and neural networks are transforming research—turbocharging existing methods, opening previously unexplored or inaccessible pathways, and creating novel opportunities in drug development, computing, energy technologies, manufacturing, robotics, neuroscience, metallurgy, and even wildlife preservation. “I cannot think of a single group meeting that we have where we’re not talking about these tools,” says Angela Koehler, the Charles W. and Jennifer C. Johnson Professor of Biological Engineering and faculty lead of the MIT Health and Life Sciences Collaborative (MIT HEALS). Her research group uses AI models to develop drug candidates designed to attach to molecular targets previously considered “undruggable,” such as transcription factors, RNA-binding proteins, or cytokines. “I would say 90% of the thesis committees I’m on involve a significant AI component,” she says. “And that definitely was not the case five years ago.” “Artificial intelligence is everywhere on campus,” says Ian Waitz, MIT’s vice president for research and the Jerome C. Hunsaker Professor of Aero-Astro. “Any field with a tremendous amount of complexity will benefit from it. Life sciences. Materials science. Anyone who does any kind of image analysis uses these tools now. I don’t know of a single research field here at MIT that hasn’t been impacted by AI.” AI isn’t exactly new at MIT Though Deng and Cordero may have come to it through happenstance or clever matchmaking, most developments in AI at MIT don’t arise that way. Nor is the interest in it new. More than 70 years ago, in 1954, computer researcher Belmont G. Farley and physicist Wesley A. Clark ran the world’s first computer simulation of a neural network at MIT. Interest in neural network technology—now better known as deep learning—waxed and waned over the next decades. Ju Li, PhD ’00, the Carl Richard Soderberg Professor of Power Engineering (as well as a professor of nuclear science and engineering and materials science and engineering), remembers taking a course on neural networks during Independent Activities Period (IAP) in 1995, when he was a graduate student. “It was not a deep network—just a few layers,” recalls Li, who researches materials used in nuclear energy, batteries, electrolyzers, and energy-­efficient computing. He characterizes it as essentially a regression tool that they used to fit curves. But over the past few years, activit