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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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Physical AI moves closer to factory floors as companies test humanoid robots

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British technology company Humanoid will deploy humanoid robots at factories operated by German industrial supplier Schaeffler, Reuters reported. The two companies’ agreement covers an estimated 1,000 to 2,000 robots in Schaeffler’s global manufacturing sites by 2032, according to a Humanoid spokesperson. The companies have not disclosed the contract value. The first deployment is scheduled between

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Bain sees US$100 billion SaaS market in agentic AI automation

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Bain & Company has estimated a US$100 billion market in the US for SaaS companies using agentic AI. The firm said the market is tied to automating coordination work in enterprise systems. The estimate comes from the second report in Bain’s five-part series on the software industry in the age of AI. The report examines

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Kakao Mobility details Level 4 autonomous driving roadmap for physical AI

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Kakao Mobility has set out plans to develop Level 4 autonomous driving technologies in-house as part of its physical AI strategy. Kim Jin-kyu, vice president and head of Kakao Mobility’s Physical AI division, presented the roadmap at the 2026 World IT Show conference at COEX in Seoul. His session focused on autonomous driving services built

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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

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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”

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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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IBM: How robust AI governance protects enterprise margins

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To protect enterprise margins, business leaders must invest in robust AI governance to securely manage AI infrastructure. When evaluating enterprise software adoption, a recurring pattern dictates how technology matures across industries. As Rob Thomas, SVP and CCO at IBM, recently outlined, software typically graduates from a standalone product to a platform, and then from a

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

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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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