Artificial intelligence Archives

November 10, 2025

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

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 capabilities to promote AI usefulness and deployment — from learning when to trust a model that predicts another’s accuracy to more effectively reasoning over knowledge bases. Together, the efforts from the students and their mentors form a through-line, where practical and technically rigorous research leads to more dependable and valuable models across domains.

Building probes, routers, new attention mechanisms, synthetic datasets, and program-synthesis pipelines, the students’ work spans safety, inference efficiency, multimodal data, and knowledge-grounded reasoning. Their techniques emphasize scaling and integration, with impact always in sight.

Learning to trust, and when

MIT math graduate student Andrey Bryutkin’s research prioritizes the trustworthiness of models.

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November 7, 2025

MIT Energy Initiative launches Data Center Power Forum

With global power demand from data centers expected to more than double by 2030, the MIT Energy Initiative (MITEI) in September launched an effort that brings together MIT researchers and industry experts to explore innovative solutions for powering the data-driven future. At its annual research conference, MITEI announced the Data Center Power Forum, a targeted research effort for MITEI member companies interested in addressing the challenges of data center power demand. The Data Center Power Forum builds on lessons from MITEI’s May 2025 symposium on the energy to power the expansion of artificial intelligence (AI) and focus panels related to data centers at the fall 2024 research conference.

In the United States, data centers consumed 4 percent of the country’s electricity in 2023, with demand expected to increase to 9 percent by 2030, according to the Electric Power Research Institute. Much of the growth in demand is from the increasing use of AI,

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November 5, 2025

Teaching robots to map large environments

A robot searching for workers trapped in a partially collapsed mine shaft must rapidly generate a map of the scene and identify its location within that scene as it navigates the treacherous terrain.

Researchers have recently started building powerful machine-learning models to perform this complex task using only images from the robot’s onboard cameras, but even the best models can only process a few images at a time. In a real-world disaster where every second counts, a search-and-rescue robot would need to quickly traverse large areas and process thousands of images to complete its mission.

To overcome this problem, MIT researchers drew on ideas from both recent artificial intelligence vision models and classical computer vision to develop a new system that can process an arbitrary number of images. Their system accurately generates 3D maps of complicated scenes like a crowded office corridor in a matter of seconds.

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October 24, 2025

The brain power behind sustainable AI

How can you use science to build a better gingerbread house?

That was something Miranda Schwacke spent a lot of time thinking about. The MIT graduate student in the Department of Materials Science and Engineering (DMSE) is part of Kitchen Matters, a group of grad students who use food and kitchen tools to explain scientific concepts through short videos and outreach events. Past topics included why chocolate “seizes,” or becomes difficult to work with when melting (spoiler: water gets in), and how to make isomalt, the sugar glass that stunt performers jump through in action movies.

Two years ago, when the group was making a video on how to build a structurally sound gingerbread house, Schwacke scoured cookbooks for a variable that would produce the most dramatic difference in the cookies.

“I was reading about what determines the texture of cookies,

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October 18, 2025

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

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 clothes we discard when they go out of style or no longer fit. But what if we could simply reassemble our clothes into whatever outfits we wanted, adapting to trends and the ways our bodies change?

A team of researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Adobe are attempting to bring eco-friendly, versatile garments to life. Their new “Refashion” software system breaks down fashion design into modules — essentially, smaller building blocks — by allowing users to draw, plan,

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October 17, 2025

Method teaches generative AI models to locate personalized objects

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 this basic task. Vision-language models like GPT-5 often excel at recognizing general objects, like a dog, but they perform poorly at locating personalized objects, like Bowser the French Bulldog.

To address this shortcoming, researchers from MIT and the MIT-IBM Watson AI Lab have introduced a new training method that teaches vision-language models to localize personalized objects in a scene.

Their method uses carefully prepared video-tracking data in which the same object is tracked across multiple frames. They designed the dataset so the model must focus on contextual clues to identify the personalized object,

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October 12, 2025

Using generative AI to diversify virtual training grounds for robots

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 this versatility? Billions, or even trillions, of textual data points across the internet.

Those data aren’t enough to teach a robot to be a helpful household or factory assistant, though. To understand how to handle, stack, and place various arrangements of objects across diverse environments, robots need demonstrations. You can think of robot training data as a collection of how-to videos that walk the systems through each motion of a task. Collecting these demonstrations on real robots is time-consuming and not perfectly repeatable, so engineers have created training data by generating simulations with AI (which don’t often reflect real-world physics),

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October 10, 2025

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

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 from both institutions will collaborate on fundamental research projects to advance the technological foundations of AI and its applications in three core areas: scientific discovery, human thriving, and the health of the planet.

“Artificial intelligence is transforming nearly every aspect of human endeavor. MIT’s leadership in AI is greatly enriched through collaborations with leading academic institutions in the U.S. and around the world,” says Dan Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Ellis Warren Professor of Electrical Engineering and Computer Science. “Our collaboration with MBZUAI reflects a shared commitment to advancing AI in ways that are responsible,

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October 7, 2025

New prediction model could improve the reliability of fusion power plants

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 limitless fusion energy.

Today, there are a number of experimental tokamaks in operation around the world, with more underway. Most are small-scale research machines built to investigate how the devices can spin up plasma and harness its energy. One of the challenges that tokamaks face is how to safely and reliably turn off a plasma current that is circulating at speeds of up to 100 kilometers per second, at temperatures of over 100 million degrees Celsius.

Such “rampdowns” are necessary when a plasma becomes unstable. To prevent the plasma from further disrupting and potentially damaging the device’s interior,

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October 4, 2025

AI maps how a new antibiotic targets gut bacteria

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 Laboratory (CSAIL) and McMaster University have identified a new compound that takes a more targeted approach. The molecule, called enterololin, suppresses a group of bacteria linked to Crohn’s disease flare-ups while leaving the rest of the microbiome largely intact. Using a generative AI model, the team mapped how the compound works, a process that usually takes years but was accelerated here to just months.

“This discovery speaks to a central challenge in antibiotic development,” says Jon Stokes, senior author of a new paper on the work,

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