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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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Simpler models can outperform deep learning at climate prediction

Algorithms, Artificial Intelligence, Climate change, Computer modeling, data, EAPS, Earth and atmospheric sciences, Emissions, IDSS, Machine Learning, MIT Schwarzman College of Computing, Pollution, Public health, Research, School of Science, Sustainability

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

Algorithms, Artificial Intelligence, Computer science and technology, data, Electrical engineering and computer science (EECS), Interview, Laboratory for Information and Decision Systems (LIDS), Machine Learning, MIT Schwarzman College of Computing, Research, School of Engineering

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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