MIT Schwarzman College of Computing

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Enabling privacy-preserving AI training on everyday devices

ai, AI (Artificial Intelligence), Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, cybersecurity, Electrical engineering and computer science (EECS), Machine Learning, MIT Schwarzman College of Computing, Mobile Devices, Privacy, Research, School of Engineering

A new method developed by MIT researchers can accelerate a privacy-preserving artificial intelligence training method by about 81 percent. This advance could enable a wider array of resource-constrained edge devices, like sensors and smartwatches, to deploy more accurate AI models while keeping user data secure.The MIT researchers boosted the efficiency of a technique known as […]

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A faster way to estimate AI power consumption

ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Computer science and technology, data, Electrical engineering and computer science (EECS), Energy, Environment, Machine Learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Research, School of Engineering, Sustainability, Sustainable computing, Technology and society

Due to the explosive growth of artificial intelligence, it is estimated that data centers will consume up to 12 percent of total U.S. electricity by 2028, according to the Lawrence Berkeley National Laboratory. Improving data center energy efficiency is one way scientists are striving to make AI more sustainable.Toward that goal, researchers from MIT and the

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MIT scientists build the world’s largest collection of Olympiad-level math problems, and open it to everyone

ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Contests and academic competitions, data, Electrical engineering and computer science (EECS), Machine Learning, Mathematics, MIT Schwarzman College of Computing, National Science Foundation (NSF), School of Engineering

Every year, the countries competing in the International Mathematical Olympiad (IMO) arrive with a booklet of their best, most original problems. Those booklets get shared among delegations, then quietly disappear. No one had ever collected them systematically, cleaned them, and made them available, not for AI researchers testing the limits of mathematical reasoning, and not

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Teaching AI models to say “I’m not sure”

ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, data, Electrical engineering and computer science (EECS), Machine Learning, MIT Schwarzman College of Computing, Research, School of Engineering

Confidence is persuasive. In artificial intelligence systems, it is often misleading.Today’s most capable reasoning models share a trait with the loudest voice in the room: They deliver every answer with the same unshakable certainty, whether they’re right or guessing. Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have now traced that overconfidence to

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Jacob Andreas and Brett McGuire named Edgerton Award winners

ai, AI (Artificial Intelligence), Artificial Intelligence, Astronomy and astrophysics, Awards, honors and fellowships, Chemistry, Computer Science and Artificial Intelligence Laboratory (CSAIL), Education, teaching, academics, Electrical engineering and computer science (EECS), Faculty, Machine Learning, MIT Schwarzman College of Computing, Natural Language Processing, School of Engineering, School of Science

MIT Associate Professor Jacob Andreas of the Department of Electrical Engineering and Computer Science [EECS] and MIT Associate Professor Brett McGuire of the Department of Chemistry have been selected as the winners of the 2026 Harold E. Edgerton Faculty Achievement Award. Established in 1982 as a permanent tribute to Institute Professor Emeritus Harold E. Edgerton’s great and enduring

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Q&A: MIT SHASS and the future of education in the age of AI

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The MIT School of Humanities, Arts, and Social Sciences (SHASS) was founded in 1950 in response to “a new era emerging from social upheaval and the disasters of war,” as outlined in the 1949 Lewis Committee Report. The report’s findings emphasized MIT’s role and responsibility in the new nuclear age, which called for doubling down on genuine

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Human-machine teaming dives underwater

ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Autonomous Underwater Vehicles (AUV), Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical engineering and computer science (EECS), Human-computer interaction, Lincoln Laboratory, Machine Learning, MIT Schwarzman College of Computing, Oceanography and ocean engineering, Research, Robotics, School of Engineering, Sensors

The electricity to an island goes out. To find the break in the underwater power cable, a ship pulls up the entire line or deploys remotely operated vehicles (ROVs) to traverse the line. But what if an autonomous underwater vehicle (AUV) could map the line and pinpoint the location of the fault for a diver

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A philosophy of work

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What makes work valuable? Michal Masny, the NC Ethics of Technology Postdoctoral Fellow in the MIT Department of Philosophy, investigates the role work plays in our lives and its impact on our well-being. Masny sees numerous benefits to work, beyond a paycheck. It’s a space for people to develop excellence at something, make a social contribution, gain

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New technique makes AI models leaner and faster while they’re still learning

ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, data, Electrical engineering and computer science (EECS), Machine Learning, MIT Schwarzman College of Computing, Research, School of Engineering

Training a large artificial intelligence model is expensive, not just in dollars, but in time, energy, and computational resources. Traditionally, obtaining a smaller, faster model either requires training a massive one first and then trimming it down, or training a small one from scratch and accepting weaker performance. Researchers at MIT’s Computer Science and Artificial Intelligence

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Helping data centers deliver higher performance with less hardware

ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, data, Defense Advanced Research Projects Agency (DARPA), Electrical engineering and computer science (EECS), Electronics, Machine Learning, MIT Schwarzman College of Computing, National Science Foundation (NSF), Research, School of Engineering, Sustainability

To improve data center efficiency, multiple storage devices are often pooled together over a network so many applications can share them. But even with pooling, significant device capacity remains underutilized due to performance variability across the devices.MIT researchers have now developed a system that boosts the performance of storage devices by handling three major sources

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