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

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, MIT-IBM Watson AI Lab, Research, School of Engineering

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?

Artificial Intelligence, Collaboration, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical engineering and computer science (EECS), Industry, Machine Learning, MIT Schwarzman College of Computing, MIT Sloan School of Management, President Sally Kornbluth, Provost, Research, Robotics, School of Architecture and Planning, School of Engineering, School of Humanities Arts and Social Sciences, School of Science, Special events and guest speakers, Startups, Technology and society

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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How are MIT entrepreneurs using AI?

Artificial Intelligence, Business and management, Classes and programs, Faculty, Innovation and Entrepreneurship (I&E), Martin Trust Center for MIT Entrepreneurship, MIT Sloan School of Management, Startups

The Martin Trust Center for MIT Entrepreneurship strives to teach students the craft of entrepreneurship. Over the last few years, no technology has changed that craft more than artificial intelligence.While many are predicting a rapid and complete transformation in how startups are built, the Trust Center’s leaders have a more nuanced view.“The fundamentals of entrepreneurship

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New tool makes generative AI models more likely to create breakthrough materials

Artificial Intelligence, Department of Energy (DoE), Energy, Machine Learning, Materials science and engineering, National Science Foundation (NSF), Nuclear science and engineering, Quantum physics, Research, School of Engineering, Superconductivity

The artificial intelligence models that turn text into images are also useful for generating new materials. Over the last few years, generative materials models from companies like Google, Microsoft, and Meta have drawn on their training data to help researchers design tens of millions of new materials.But when it comes to designing materials with exotic

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MIT affiliates win AI for Math grants to accelerate mathematical discovery

Algorithms, Artificial Intelligence, Awards, honors and fellowships, Computer science and technology, data, Funding, Mathematics, Research, School of Science, Staff

MIT Department of Mathematics researchers David Roe ’06 and Andrew Sutherland ’90, PhD ’07 are among the inaugural recipients of the Renaissance Philanthropy and XTX Markets’ AI for Math grants. Four additional MIT alumni — Anshula Gandhi ’19, Viktor Kunčak SM ’01, PhD ’07; Gireeja Ranade ’07; and Damiano Testa PhD ’05 — were also honored

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Improving the workplace of the future

Artificial Intelligence, Business and management, Economics, Finance, Graduate, postdoctoral, Labor and jobs, National Science Foundation (NSF), Profile, School of Humanities Arts and Social Sciences, Statistics, Students, Technology and society, Undergraduate Research Opportunities Program (UROP)

Whitney Zhang ’21 believes in the importance of valuing workers regardless of where they fit into an organizational chart.Zhang is a PhD student in MIT’s Department of Economics studying labor economics. She explores how the technological and managerial decisions companies make affect workers across the pay spectrum. “I’ve been interested in economics, economic impacts, and related social

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New AI system could accelerate clinical research

Artificial Intelligence, Broad Institute, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical engineering and computer science (EECS), Health, Health care, Imaging, Machine Learning, Medicine, MIT Schwarzman College of Computing, National Institutes of Health (NIH), Research, School of Engineering

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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AI system learns from many types of scientific information and runs experiments to discover new materials

Artificial Intelligence, DMSE, Energy, Machine Learning, Materials science and engineering, MIT.nano, Nanoscience and nanotechnology, Nuclear science and engineering, Research, School of Engineering

Machine-learning models can speed up the discovery of new materials by making predictions and suggesting experiments. But most models today only consider a few specific types of data or variables. Compare that with human scientists, who work in a collaborative environment and consider experimental results, the broader scientific literature, imaging and structural analysis, personal experience

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Responding to the climate impact of generative AI

Algorithms, Artificial Intelligence, Climate change, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical engineering and computer science (EECS), Emissions, Energy, Energy storage, infrastructure, Lincoln Laboratory, Machine Learning, MIT Energy Initiative, MIT Schwarzman College of Computing, MIT Sloan School of Management, Research, School of Engineering, Supercomputing, Sustainable computing

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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Lincoln Lab unveils the most powerful AI supercomputer at any US university

Artificial Intelligence, cybersecurity, Department of Defense (DoD), Energy, Haystack Observatory, Human-computer interaction, Lincoln Laboratory, Supercomputing

The new TX-Generative AI Next (TX-GAIN) computing system at the Lincoln Laboratory Supercomputing Center  (LLSC) is the most powerful AI supercomputer at any U.S. university. With its recent ranking from  TOP500, which biannually publishes a list of the top supercomputers in various categories, TX-GAIN joins the ranks of other powerful systems at the LLSC, all supporting

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