MIT Schwarzman College of Computing

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MIT Schwarzman College of Computing and MBZUAI launch international collaboration to shape the future of AI

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The MIT Schwarzman College of Computing and the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) recently celebrated the launch of the MIT–MBZUAI Collaborative Research Program, a new effort to strengthen the building blocks of artificial intelligence and accelerate its use in pressing scientific and societal challenges.Under the five-year agreement, faculty, students, and research staff […]

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Blending neuroscience, AI, and music to create mental health innovations

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Computational neuroscientist and singer/songwriter Kimaya (Kimy) Lecamwasam, who also plays electric bass and guitar, says music has been a core part of her life for as long as she can remember. She grew up in a musical family and played in bands all through high school.“For most of my life, writing and playing music was

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Method teaches generative AI models to locate personalized objects

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Say a person takes their French Bulldog, Bowser, to the dog park. Identifying Bowser as he plays among the other canines is easy for the dog-owner to do while onsite.But if someone wants to use a generative AI model like GPT-5 to monitor their pet while they are at work, the model could fail at

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DOE selects MIT to establish a Center for the Exascale Simulation of Coupled High-Enthalpy Fluid–Solid Interactions

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The U.S. Department of Energy’s National Nuclear Security Administration (DOE/NNSA) recently announced that it has selected MIT to establish a new research center dedicated to advancing the predictive simulation of extreme environments, such as those encountered in hypersonic flight and atmospheric re-entry. The center will be part of the fourth phase of NNSA’s Predictive Science Academic Alliance

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How to build AI scaling laws for efficient LLM training and budget maximization

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When researchers are building large language models (LLMs), they aim to maximize performance under a particular computational and financial budget. Since training a model can amount to millions of dollars, developers need to be judicious with cost-impacting decisions about, for instance, the model architecture, optimizers, and training datasets before committing to a model. To anticipate

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What does the future hold for generative AI?

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When OpenAI introduced ChatGPT to the world in 2022, it brought generative artificial intelligence into the mainstream and started a snowball effect that led to its rapid integration into industry, scientific research, health care, and the everyday lives of people who use the technology.What comes next for this powerful but imperfect tool?With that question in

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New AI system could accelerate clinical research

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Annotating regions of interest in medical images, a process known as segmentation, is often one of the first steps clinical researchers take when running a new study involving biomedical images.For instance, to determine how the size of the brain’s hippocampus changes as patients age, the scientist first outlines each hippocampus in a series of brain

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Responding to the climate impact of generative AI

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In part 2 of our two-part series on generative artificial intelligence’s environmental impacts, MIT News explores some of the ways experts are working to reduce the technology’s carbon footprint.The energy demands of generative AI are expected to continue increasing dramatically over the next decade.For instance, an April 2025 report from the International Energy Agency predicts that the global

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Simpler models can outperform deep learning at climate prediction

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Environmental scientists are increasingly using enormous artificial intelligence models to make predictions about changes in weather and climate, but a new study by MIT researchers shows that bigger models are not always better.The team demonstrates that, in certain climate scenarios, much simpler, physics-based models can generate more accurate predictions than state-of-the-art deep-learning models.Their analysis also

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3 Questions: The pros and cons of synthetic data in AI

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Synthetic data are artificially generated by algorithms to mimic the statistical properties of actual data, without containing any information from real-world sources. While concrete numbers are hard to pin down, some estimates suggest that more than 60 percent of data used for AI applications in 2024 was synthetic, and this figure is expected to grow

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