MIT News about AI
MIT news feed about: Artificial intelligence
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The crucial human component in computing and AI
The MIT Ethics of Computing Research Symposium brought together experts and researchers working at the heart of ethical and social impact in technology. -
PATH to boost AI training and career opportunities for industry-aligned jobs
MIT RAISE and Georgia State University announce an initiative to connect universities, community colleges, industry, and government to expand industry-aligned AI training and career pathways. -
NSF renews support for MIT-led AI and physics institute, expanding a new model for discovery
IAIFI enters its second phase with increased funding, broader ambitions, and a growing community at the frontier of AI and fundamental physics. -
Teaching AI agents to ask better questions by playing “Battleship”
MIT researchers use the classic game as a test bed for AI agents, finding a small AI model can outperform the biggest ones at 1 percent of the cost. -
Tod Machover receives George Peabody Medal for contributions to music and technology
The George Peabody Medal is the highest honor bestowed by the Peabody Institute of the Johns Hopkins University. -
MIT researchers teach AI models to interpret charts
The new ChartNet training dataset could improve the accuracy of vision-language models that help analyze business trends or interpret scientific figures. -
Media Advisory: MIT to establish regional quantum hub
With $25 million investment from the Commonwealth of Massachusetts, MIT to build a new shared-use facility to serve as a statewide quantum toolbox. -
Technology usually creates jobs for young, skilled workers. Will AI do the same?
A new study of the postwar U.S. shows which kinds of workers historically filled new tech-enabled jobs. -
Building AI models that understand chemical principles
Connor Coley works at the interface of chemistry and machine learning, to discover and design new drug compounds. -
Justin Solomon appointed associate dean of engineering education
MIT faculty member in electrical engineering and computer science to focus on innovation in engineering education and new pedagogical approaches. -
Two from MIT named 2026 Knight-Hennessy Scholars
The prestigious fellowship funds graduate studies at Stanford University. -
Q&A: Expanding MIT’s global reach through Universal Learning
Dimitris Bertsimas and Megan Mitchell discuss the motivation behind Universal Learning, and what sets the new MIT Open Learning educational initiative apart. -
Universal AI is “a pathway to AI fluency that’s accessible and approachable to anyone, anywhere”
New AI education program from MIT Open Learning debuts with AI-powered personalization and a free introductory course for learners everywhere. -
Study: Firms often use automation to control certain workers’ wages
MIT economists found US companies tend to target employees earning a “wage premium,” which increases inequality but not necessarily productivity. -
Games people — and machines — play: Untangling strategic reasoning to advance AI
Assistant Professor Gabriele Farina mines the foundations of decision-making in complex multi-agent scenarios. -
Beacon Biosignals is mapping the brain during sleep
Founded by Jake Donoghue PhD ’19 and former MIT researcher Jarrett Revels, the company is creating an AI-driven platform to help diagnose and treat disease. -
Improving understanding with language
MIT senior Olivia Honeycutt investigates how the ways we communicate can shape our views of the world. -
Making the case for curiosity-driven science
President Sally Kornbluth spoke in front of a packed crowd about growing challenges to the U.S. research ecosystem as funding for America’s top research universities becomes increasingly strained. -
Solving the “Whac-a-mole dilemma”: A smarter way to debias AI vision models
A new debiasing technique called WRING avoids creating or amplifying biases that can occur with existing debiasing approaches. -
The MIT-IBM Computing Research Lab launches to shape the future of AI and quantum computing
Building on a long-standing MIT–IBM collaboration, the new lab will chart the convergence of AI, algorithms, and quantum computing. -
Enabling privacy-preserving AI training on everyday devices
A new method could bring more accurate and efficient AI models to high-stakes applications like health care and finance, even in under-resourced settings. -
A faster way to estimate AI power consumption
The “EnergAIzer” method generates reliable results in seconds, enabling data center operators to efficiently allocate resources and reduce wasted energy. -
MIT scientists build the world’s largest collection of Olympiad-level math problems, and open it to everyone
New dataset of 30,000-plus competition math problems from 47 countries gives AI researchers a harder test — and students worldwide a better training ground. -
Teaching AI models to say “I’m not sure”
A new training method improves the reliability of AI confidence estimates without sacrificing performance, addressing a root cause of hallucination in reasoning models. -
Jacob Andreas and Brett McGuire named Edgerton Award winners
The associate professors of EECS and chemistry, respectively, are honored for exceptional contributions to teaching, research, and service at MIT. -
Bringing AI-driven protein-design tools to biologists everywhere
Founded by Tristan Bepler PhD ’20 and former MIT professor Tim Lu PhD ’07, OpenProtein.AI offers researchers open-source models and other tools for protein engineering. -
Human-machine teaming dives underwater
Researchers are developing hardware and algorithms to improve collaboration between divers and autonomous underwater vehicles engaged in maritime missions. -
Q&A: MIT SHASS and the future of education in the age of AI
As the School of Humanities, Arts, and Social Sciences marks 75 years, Dean Agustín Rayo reflects on how AI is reshaping higher education and why SHASS disciplines continue to be central to MIT’s mission. -
A philosophy of work
As the NC Ethics of Technology Postdoctoral Fellow, Michal Masny is advancing dialogue, teaching, and research into the social and ethical dimensions of new computing technologies. -
New technique makes AI models leaner and faster while they’re still learning
Researchers use control theory to shed unnecessary complexity from AI models during training, cutting compute costs without sacrificing performance.