Artificial Intelligence

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Helping K-12 schools navigate the complex world of AI

Artificial Intelligence, Books and authors, Comparative Media Studies/Writing, Education, teaching, academics, Faculty, K-12 education, Podcasts, School of Humanities Arts and Social Sciences, Technology and society

With the rapid advancement of generative artificial intelligence, teachers and school leaders are looking for answers to complicated questions about successfully integrating technology into lessons, while also ensuring students actually learn what they’re trying to teach. Justin Reich, an associate professor in MIT’s Comparative Media Studies/Writing program, hopes a new guidebook published by the MIT Teaching Systems Lab […]

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Teaching robots to map large environments

Aeronautical and astronautical engineering, Algorithms, Artificial Intelligence, Autonomous vehicles, Computer science and technology, Computer vision, Disaster response, Electrical engineering and computer science (EECS), Laboratory for Information and Decision Systems (LIDS), Machine Learning, MIT Schwarzman College of Computing, National Science Foundation (NSF), Research, Robotics, School of Engineering

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

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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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MIT Energy Initiative launches Data Center Power Forum

Artificial Intelligence, Cleaner industry, Climate change, Electric grid, Emissions, Energy, Energy efficiency, MIT Energy Initiative, Research, Sustainability, Technology and policy

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

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Understanding the nuances of human-like intelligence

Artificial Intelligence, Brain and cognitive sciences, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical engineering and computer science (EECS), Faculty, Machine Learning, MIT Schwarzman College of Computing, Profile, School of Engineering

What can we learn about human intelligence by studying how machines “think?” Can we better understand ourselves if we better understand the artificial intelligence systems that are becoming a more significant part of our everyday lives?These questions may be deeply philosophical, but for Phillip Isola, finding the answers is as much about computation as it

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MIT Energy Initiative conference spotlights research priorities amidst a changing energy landscape

Artificial Intelligence, Batteries, Carbon dioxide, Chemistry, Cleaner industry, Climate change, Collaboration, DMSE, Electric grid, Energy, Energy storage, Faculty, government, infrastructure, Innovation and Entrepreneurship (I&E), Investments, Mechanical engineering, MIT Energy Initiative, Policy, Renewable energy, School of Engineering, School of Science, Special events and guest speakers, Startups, Students, Sustainability, The Engine, Transportation

“We’re here to talk about really substantive changes, and we want you to be a participant in that,” said Desirée Plata, the School of Engineering Distinguished Professor of Climate and Energy in MIT’s Department of Civil and Environmental Engineering, at Energizing@MIT: the MIT Energy Initiative’s (MITEI) Annual Research Conference that was held on Sept. 9-10.Plata’s

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New AI agent learns to use CAD to create 3D objects from sketches

3-D, Artificial Intelligence, automation, Computer modeling, Design, Machine Learning, Mechanical engineering, Research, School of Engineering, Software

Computer-Aided Design (CAD) is the go-to method for designing most of today’s physical products. Engineers use CAD to turn 2D sketches into 3D models that they can then test and refine before sending a final version to a production line. But the software is notoriously complicated to learn, with thousands of commands to choose from.

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The cost of thinking

Artificial Intelligence, Brain and cognitive sciences, Computer science and technology, Machine Learning, McGovern Institute, Neuroscience, Research, School of Science

Large language models (LLMs) like ChatGPT can write an essay or plan a menu almost instantly. But until recently, it was also easy to stump them. The models, which rely on language patterns to respond to users’ queries, often failed at math problems and were not good at complex reasoning. Suddenly, however, they’ve gotten a

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How artificial intelligence can help achieve a clean energy future

Artificial Intelligence, Chemical engineering, Cleaner industry, Climate, Electric grid, Electric vehicles, Energy, Energy efficiency, Energy storage, infrastructure, Investments, Machine Learning, Mechanical engineering, MIT Energy Initiative, Renewable energy, Research, School of Engineering, Sustainability, Sustainable computing, Weather modeling

There is growing attention on the links between artificial intelligence and increased energy demands. But while the power-hungry data centers being built to support AI could potentially stress electricity grids, increase customer prices and service interruptions, and generally slow the transition to clean energy, the use of artificial intelligence can also help the energy transition.For

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