Algorithms

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

ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Defense Advanced Research Projects Agency (DARPA), Electrical engineering and computer science (EECS), Games, Machine Learning, MIT Schwarzman College of Computing, MIT Siegel Family Quest for Intelligence, MIT-IBM Watson AI Lab, National Science Foundation (NSF), Natural Language Processing, Programming, Research, School of Engineering, School of Science

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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AI users re-create dead pilots’ voices from crash investigation docs

ai, AI (Artificial Intelligence), ai models, aircraft accidents, Algorithms, Artificial Intelligence, audio recordings, cockpit voice recorder, ntsb, Tech

Pilots’ voices from the last seconds of a fatal cargo plane crash have been re-created by Internet sleuths using software and AI tools. The spread of reconstructed audio recordings has prompted a US government agency to suspend all public access to its database of civil transportation accidents—because federal law prohibits investigators from publicly releasing audio

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US scrambles to stop Internet users re-creating dead pilots’ voices

ai, AI (Artificial Intelligence), ai models, aircraft accidents, Algorithms, Artificial Intelligence, audio recordings, cockpit voice recorder, ntsb, Tech

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Games people — and machines — play: Untangling strategic reasoning to advance AI

ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Computer science and technology, Electrical engineering and computer science (EECS), Faculty, Game theory, Human-computer interaction, Laboratory for Information and Decision Systems (LIDS), Machine Learning, MIT Schwarzman College of Computing, Profile, School of Engineering

Gabriele Farina grew up in a small town in a hilly winemaking region of northern Italy. Neither of his parents had college degrees, and although both were convinced they “didn’t understand math,” Farina says, they bought him the technical books he wanted and didn’t discourage him from attending the science-oriented, rather than the classical, high

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

ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Collaboration, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical engineering and computer science (EECS), Funding, Industry, Machine Learning, MIT Schwarzman College of Computing, MIT-IBM Computing Research Lab, MIT-IBM Watson AI Lab, Quantum Computing, Research, Supercomputing

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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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”

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

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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