Computer Science and Artificial Intelligence Laboratory (CSAIL)

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AI maps how a new antibiotic targets gut bacteria

Algorithms, Antibiotics, Artificial Intelligence, Bacteria, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Disease, Drug development, Drug discovery, Electrical engineering and computer science (EECS), Health care, Health sciences and technology, Jameel Clinic, Machine Learning, Medicine, MIT Schwarzman College of Computing, Research, School of Engineering, Viruses

For patients with inflammatory bowel disease, antibiotics can be a double-edged sword. The broad-spectrum drugs often prescribed for gut flare-ups can kill helpful microbes alongside harmful ones, sometimes worsening symptoms over time. When fighting gut inflammation, you don’t always want to bring a sledgehammer to a knife fight.Researchers at MIT’s Computer Science and Artificial Intelligence […]

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Using generative AI to diversify virtual training grounds for robots

Artificial Intelligence, Computer modeling, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, data, Electrical engineering and computer science (EECS), Internet, Machine Learning, MIT Schwarzman College of Computing, Research, Robotics, School of Engineering, Simulation

Chatbots like ChatGPT and Claude have experienced a meteoric rise in usage over the past three years because they can help you with a wide range of tasks. Whether you’re writing Shakespearean sonnets, debugging code, or need an answer to an obscure trivia question, artificial intelligence systems seem to have you covered. The source of

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Method teaches generative AI models to locate personalized objects

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, Video

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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New software designs eco-friendly clothing that can reassemble into new items

Artificial Intelligence, Cleaner industry, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Design, Electrical engineering and computer science (EECS), Human-computer interaction, Invention, Machine Learning, Media Lab, MIT Morningside Academy for Design, MIT Schwarzman College of Computing, Research, School of Engineering, Sustainability

It’s hard to keep up with the ever-changing trends of the fashion world. What’s “in” one minute is often out of style the next season, potentially causing you to re-evaluate your wardrobe.Staying current with the latest fashion styles can be wasteful and expensive, though. Roughly 92 million tons of textile waste are produced annually, including the

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Creating AI that matters

Algorithms, Artificial Intelligence, Business and management, Collaboration, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, data, Electrical engineering and computer science (EECS), IDSS, Industry, Machine Learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Research, School of Engineering, Technology and society

When it comes to artificial intelligence, MIT and IBM were there at the beginning: laying foundational work and creating some of the first programs — AI predecessors — and theorizing how machine “intelligence” might come to be.Today, collaborations like the MIT-IBM Watson AI Lab, which launched eight years ago, are continuing to deliver expertise for

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

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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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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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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A greener way to 3D print stronger stuff

3-D printing, Artificial Intelligence, Cleaner industry, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Design, Electrical engineering and computer science (EECS), Invention, Machine Learning, MIT Schwarzman College of Computing, Recycling, Research, School of Engineering, Stock Market, Sustainability

3D printing has come a long way since its invention in 1983 by Chuck Hull, who pioneered stereolithography, a technique that solidifies liquid resin into solid objects using ultraviolet lasers. Over the decades, 3D printers have evolved from experimental curiosities into tools capable of producing everything from custom prosthetics to complex food designs, architectural models,

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