Machine Learning

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MIT researchers propose a new model for legible, modular software

Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical engineering and computer science (EECS), Human-computer interaction, Machine Learning, MIT Schwarzman College of Computing, Programming, Programming languages, Research, School of Engineering, Software

Coding with large language models (LLMs) holds huge promise, but it also exposes some long-standing flaws in software: code that’s messy, hard to change safely, and often opaque about what’s really happening under the hood. Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) are charting a more “modular” path ahead. Their new approach breaks

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Charting the future of AI, from safer answers to faster thinking

Algorithms, Artificial Intelligence, Classes and programs, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, data, Electrical engineering and computer science (EECS), Graduate, postdoctoral, Institute for Medical Engineering and Science (IMES), Laboratory for Information and Decision Systems (LIDS), Machine Learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Research, School of Engineering, Students

Adoption of new tools and technologies occurs when users largely perceive them as reliable, accessible, and an improvement over the available methods and workflows for the cost. Five PhD students from the inaugural class of the MIT-IBM Watson AI Lab Summer Program are utilizing state-of-the-art resources, alleviating AI pain points, and creating new features and

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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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Printable aluminum alloy sets strength records, may enable lighter aircraft parts

3-D printing, Artificial Intelligence, DMSE, Machine Learning, Materials science and engineering, Mechanical engineering, MIT.nano, Research, School of Engineering

MIT engineers have developed a printable aluminum alloy that can withstand high temperatures and is five times stronger than traditionally manufactured aluminum.The new printable metal is made from a mix of aluminum and other elements that the team identified using a combination of simulations and machine learning, which significantly pruned the number of possible combinations

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New prediction model could improve the reliability of fusion power plants

Aeronautical and astronautical engineering, Artificial Intelligence, Computer modeling, Energy, Fluid dynamics, Fusion, Laboratory for Information and Decision Systems (LIDS), Machine Learning, MIT Schwarzman College of Computing, Nuclear power and reactors, Plasma Science and Fusion Center, Renewable energy, Research, School of Engineering, Sustainability

Tokamaks are machines that are meant to hold and harness the power of the sun. These fusion machines use powerful magnets to contain a plasma hotter than the sun’s core and push the plasma’s atoms to fuse and release energy. If tokamaks can operate safely and efficiently, the machines could one day provide clean and

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Fighting for the health of the planet with AI

Algorithms, Artificial Intelligence, Climate change, Computer science and technology, Electric grid, Electrical engineering and computer science (EECS), Energy, Faculty, Laboratory for Information and Decision Systems (LIDS), Machine Learning, MIT Schwarzman College of Computing, Profile, Renewable energy, School of Engineering, Sustainability, Technology and policy

For Priya Donti, childhood trips to India were more than an opportunity to visit extended family. The biennial journeys activated in her a motivation that continues to shape her research and her teaching.Contrasting her family home in Massachusetts, Donti — now the Silverman Family Career Development Professor in the MIT Department of Electrical Engineering and

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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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Helping scientists run complex data analyses without writing code

Algorithms, Alumni/ae, Artificial Intelligence, Disease, Electrical engineering and computer science (EECS), Health, Innovation and Entrepreneurship (I&E), Machine Learning, Medicine, School of Engineering, Startups

As costs for diagnostic and sequencing technologies have plummeted in recent years, researchers have collected an unprecedented amount of data around disease and biology. Unfortunately, scientists hoping to go from data to new cures often require help from someone with experience in software engineering.Now, Watershed Bio is helping scientists and bioinformaticians run experiments and get

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Checking the quality of materials just got easier with a new AI tool

Artificial Intelligence, Imaging, Light, Machine Learning, manufacturing, Materials science and engineering, Mechanical engineering, Research, School of Engineering

Manufacturing better batteries, faster electronics, and more effective pharmaceuticals depends on the discovery of new materials and the verification of their quality. Artificial intelligence is helping with the former, with tools that comb through catalogs of materials to quickly tag promising candidates.But once a material is made, verifying its quality still involves scanning it with

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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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