Computer Science and Artificial Intelligence Laboratory (CSAIL)

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Teaching AI agents to ask better questions by playing “Battleship”

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In 2026, the hype for artificial intelligence agents is louder than ever before. These semi-autonomous programs can “think” and execute well-defined tasks in areas like customer service and software development, typically using language models (LMs). But fields like medical diagnosis and scientific discovery require them to inquire about a vast range of solutions in uncertain […]

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MIT researchers teach AI models to interpret charts

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

To accelerate and refine decision-making in a fast-paced, global marketplace, enterprises may deploy generative artificial intelligence models to help summarize and interpret the charts that often fill market summaries and financial reports.But even the latest vision-language models sometimes struggle with this task, since it requires a model to integrate visual, numerical, and linguistic understanding. A

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Justin Solomon appointed associate dean of engineering education

ai, AI (Artificial Intelligence), Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, data, Education, teaching, academics, Electrical engineering and computer science (EECS), Faculty, Institute for Medical Engineering and Science (IMES), Leadership, Machine Learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, School of Engineering

Justin Solomon, associate professor in the MIT Department of Electrical Engineering and Computer Science (EECS), has been appointed associate dean of engineering education in the MIT School of Engineering, effective July 1.In this new role, Solomon will focus on advancing innovation in engineering education across the school. He will help shape new pedagogical approaches in

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The MIT-IBM Computing Research Lab launches to shape the future of AI and quantum computing

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The following is a joint announcement by the MIT Schwarzman College of Computing and IBM.IBM and MIT today announced the launch of the MIT-IBM Computing Research Lab, advancing their long-standing collaboration to shape the next era of computing. The new lab expands its scope to include quantum computing, alongside foundational artificial intelligence research, with the

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Enabling privacy-preserving AI training on everyday devices

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

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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

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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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Human-machine teaming dives underwater

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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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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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