David Autor, a leading researcher in AI and the future of work, has been named head of MIT's Department of Economics, effective July 1. His work focuses on labor market impacts of technological change and globalization.
David Autor, MIT economics professor since 1999, appointed department head.
He is a leading expert on AI and the future of work, studying technology's impact on jobs and inequality.
To help robots do chores in places like homes and factories, a new approach from MIT uses one language model to clarify users’ instructions, then another to ignore irrelevant info.
Masked IRL uses one LLM to elaborate on ambiguous prompts and another to ignore irrelevant environment details.
The method reduces required demonstration data by nearly five times.
Researchers from MIT and Microsoft developed Murakkab, a system that optimizes agentic workflows (AI-powered multistep tasks). It lets developers describe intent in plain language, automatically selects models, tools, and hardware, and dynamically adjusts configurations to prioritize speed or cost. Tests show it uses only ~35% computation, ~27% energy, and <25% cost versus traditional methods, without performance loss.
Murakkab automates optimization of multi-step AI workflows to reduce resource waste.
Developers specify tasks in natural language; the system dynamically chooses models, tools, and hardware.
At the AI and Society Forum at MIT, experts discussed AI's effects on employment and democracy. Economist David Autor challenged the notion that AI will eliminate jobs, while others explored AI's potential for collaboration and its risks to democratic processes.
Economist David Autor argues AI's impact depends on how it changes the scarcity of human expertise, potentially creating specialized jobs.
Panelists emphasized human judgment remains critical in decision-making, with AI as a collaborative tool.
MIT researchers combined an efficient algorithm with dedicated hardware to develop a low-power chip that enables tiny UAVs and other devices to build real-time 3D maps and navigate using only about 6 milliwatts of power.
Uses ellipsoid Gaussians instead of voxels to represent obstacles, reducing memory and power.
Algorithm processes depth images in a single pass without storing full images.
MIT researchers have developed a machine-learning approach that improves the accuracy of material simulations by constructing training datasets that capture the diversity of atomic environments in chemically disordered metal alloys, potentially accelerating materials discovery.
New method uses information theory to build diverse training datasets for machine-learning models, capturing subtle atomic patterns in disordered alloys.
Outperforms brute-force methods and large-scale models from Google and Microsoft in predicting material properties.
Leaders, faculty across MIT discuss fostering innovation and talent in Greater Boston in special series of articles published alongside the outlet's annual list of 'Tech Power Players'
Eight MIT affiliates named to Boston Globe 2026 Tech Power Players list.
MIT pushes AI advancement through free online courses and entrepreneurship support.
A new spatial memory system for robots efficiently captures details about the objects they see while exploring their environment, enabling them to answer queries like 'Where did I leave my keys?' with high accuracy.
MIT researchers developed DAAAM, a long-term memory framework that combines 3D maps with rich object descriptions.
Robots can form and recall detailed mental models of large-scale environments in real-time.
The Hertz Foundation awarded 2026 fellowships to three current MIT students and one incoming graduate student. The fellowship provides five years of financial support and autonomy for groundbreaking research in applied sciences, engineering, and mathematics.
Four MIT affiliates received 2026 Hertz Foundation Fellowships.
The fellowship offers five years of funding and lifelong networking opportunities.
A new MIT Media Lab study shows that relying on AI to verify news can degrade users' own ability to detect misinformation once the AI is removed. Over one month, 67 participants using an AI chatbot improved fake news detection by 21%, but unassisted accuracy dropped 15 percentage points after AI withdrawal. The research highlights the 'AI dependency paradox' and emphasizes designing AI as a coach rather than a crutch.
MIT Media Lab study: AI-assisted fact-checking improved accuracy by 21%, but unassisted performance declined 15 percentage points after AI removal.
The 'AI dependency paradox' mirrors deskilling from calculators and GPS, with users becoming passive 'Dependency Developers'.
The MIT Ethics of Computing Research Symposium brought together experts and researchers working at the heart of ethical and social impact in technology.
Symposium examined AI alignment, education, and the human-AI interaction gap.
Keynote by Jon Kleinberg used chess and Lord of the Rings to illustrate AI's model mismatch with human reasoning.
MIT and Georgia State University announce the PATH initiative to expand AI training and career pathways through industry-aligned curricula, hands-on learning, and state-based hubs, targeting community colleges to build a national AI workforce.
PATH is a multiyear initiative by MIT RAISE and Georgia State University focusing on affordable, industry-aligned AI training.
First two hubs launched in Massachusetts and Georgia, with over 1,000 students enrolled at GSU.
The George Peabody Medal, the highest honor of the Peabody Institute at Johns Hopkins University, has been awarded to Tod Machover, a professor at MIT Media Lab, for his pioneering work in participatory opera, AI, and creative technologies.
Tod Machover awarded the George Peabody Medal, the highest honor from the Peabody Institute.
Previous recipients include Stevie Wonder, Yo-Yo Ma, and Leonard Bernstein.
The new ChartNet training dataset could improve the accuracy of vision-language models that help analyze business trends or interpret scientific figures.
ChartNet dataset includes over a million diverse charts with rich annotations.
Small open-source models trained on ChartNet outperformed much larger commercial models.
MIT and the Commonwealth of Massachusetts announced plans to establish the Quantum Systems Laboratory (QSL) at MIT, with a $25 million state investment, creating a shared-use facility to accelerate quantum research and innovation across the region.
MIT and Massachusetts partner to create Quantum Systems Laboratory (QSL) with $25 million state investment
QSL to be world's first facility combining quantum computers, sensors, and interconnects
A new study led by MIT economist David Autor examines post-WWII U.S. employment data and finds that new technology-enabled jobs have historically been filled by young, college-educated workers in urban areas. The research also highlights the role of government-driven demand (e.g., WWII manufacturing expansion) in creating new specialized work. These insights provide historical context for AI's impact on employment, though Autor cautions it's too early to predict how AI will reshape the workplace.
New work tends to benefit workers under 30, college graduates, and those in urban settings.
Government-backed demand, such as WWII manufacturing, significantly drove the creation of new specialized jobs.
MIT Associate Professor Connor Coley combines chemistry and machine learning to accelerate drug discovery, developing AI models that incorporate chemical intuition.
Coley's research uses AI to screen billions of potential drug compounds and design new molecules.
Models like ShEPhERD evaluate drug candidates based on 3D interactions with target proteins.
MIT Open Learning launches Universal AI, an online self-paced modular program covering AI fundamentals to industry applications, with the first course free. It features AI personalization via AskTIM and aims to make AI education accessible globally.
MIT Open Learning launches Universal AI, a modular online program from fundamentals to industry-specific courses.
The first course, Fundamentals of Programming and Machine Learning, is free for all learners.
Dimitris Bertsimas and Megan Mitchell discuss the motivation behind Universal Learning, and what sets the new MIT Open Learning educational initiative apart.
Universal Learning is a new MIT Open Learning initiative combining faculty expertise and 25+ years of online education innovation.
The first offering, Universal AI, launches today; future topics include climate, energy, biology, healthcare, and manufacturing.
Assistant Professor Gabriele Farina mines the foundations of decision-making in complex multi-agent scenarios, combining game theory with machine learning to develop AI that can bluff and negotiate, and achieving superhuman performance in Stratego at minimal cost.
Farina's early fascination with machines outperforming humans led him to game theory and AI.
He co-developed Cicero, an AI that excels at negotiation and deception in games.
Beacon Biosignals uses a lightweight EEG headband to monitor brain activity during sleep at home, enabling AI analysis for diagnosing and treating neurological disorders.
Beacon developed a headband that records brain waves during sleep at home.
Machine learning algorithms process the data to detect disease progression and treatment effects.
MIT President Sally Kornbluth discusses the importance of basic research, funding challenges, and the role of universities in a live podcast conversation.
Kornbluth emphasizes that curiosity-driven science is critical for the nation's future.
She warns that strained funding could have long-term negative effects on U.S. research and talent pipelines.
A new debiasing technique called WRING avoids creating or amplifying biases that can occur with existing debiasing approaches.
WRING is a post-processing debiasing method that rotates coordinates in high-dimensional space to remove bias for a target concept without affecting other relationships.
Existing projection debiasing can inadvertently amplify or create new biases, known as the Whac-a-Mole dilemma.