Interview

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3 Questions: Neural transparency and the future of AI design

ai, AI (Artificial Intelligence), Algorithms, Apps, Artificial Intelligence, Computer science and technology, data, ethics, Faculty, Human-computer interaction, Interview, Machine Learning, Media Lab, Mental health, Research, Safety, School of Architecture and Planning, Technology and society

Millions of people are now designing their own personalized artificial intelligence companions, yet most have little idea how those creations will actually behave. In a new paper, MIT Media Lab Assistant Professor Pat Pataranutaporn and his graduate student researchers Anthony Baez and Sheer Karny introduce “neural transparency,” a tool that lets everyday users glimpse inside […]

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Q&A: What is agentic AI today, and what do we want it to be?

ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, data, Electrical engineering and computer science (EECS), Human-computer interaction, Interview, Machine Learning, MIT Schwarzman College of Computing, School of Engineering

The deployment of automated software systems called AI agents has recently exploded. A November 2025 report by MIT Sloan School of Management and Boston Consulting Group found that 35 percent of surveyed businesses had already deployed AI agents, while another 44 percent planned to implement agentic AI soon. To understand the fundamentals and potential impacts of these

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3 Questions: Beyond data-driven aesthetics

ai, AI (Artificial Intelligence), Algorithms, Alumni/ae, Architecture, Artificial Intelligence, Computer science and technology, data, Design, Exhibits, Interview, Machine Learning, School of Architecture and Planning, Special events and guest speakers

“Beyond Data-Driven Aesthetics,” by MIT Architecture alumnus and researcher Alexandros Haridis, on view at the MIT Keller Gallery through June 30, examines 20th- and 21st-century efforts to transform computing into a medium for creative production and aesthetic judgment in architecture and the applied arts. Drawing on philosophy, mathematics, computer science, and design computation, the exhibition

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Q&A: Expanding MIT’s global reach through Universal Learning

ai, AI (Artificial Intelligence), Artificial Intelligence, Careers, Classes and programs, Climate, Education, teaching, academics, Energy, Faculty, Global, Interview, Labor and jobs, learning, manufacturing, Massive open online courses (MOOCs), Office of Open Learning, Online learning, Staff

MIT’s Universal Learning is a new initiative from MIT Open Learning designed to prepare learners everywhere to tackle complex global challenges through boundary-crossing thinking. Universal Learning offerings combine subject matter expertise from MIT faculty and experts and Open Learning’s more than 25 years of innovation in online education to deliver a learning experience centered on real-world

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Q&A: MIT SHASS and the future of education in the age of AI

Administration, ai, AI (Artificial Intelligence), Artificial Intelligence, Education, teaching, academics, ethics, Faculty, history, History of MIT, Human-computer interaction, Interview, MIT Schwarzman College of Computing, Music technology, Policy, School of Engineering, School of Humanities Arts and Social Sciences, Technology and society

The MIT School of Humanities, Arts, and Social Sciences (SHASS) was founded in 1950 in response to “a new era emerging from social upheaval and the disasters of war,” as outlined in the 1949 Lewis Committee Report. The report’s findings emphasized MIT’s role and responsibility in the new nuclear age, which called for doubling down on genuine

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3 Questions: On the future of AI and the mathematical and physical sciences

ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Astronomy and astrophysics, Chemistry, Computer science and technology, data, Faculty, IDSS, Interview, Laboratory for Nuclear Science, Machine Learning, Materials science and engineering, Mathematics, MIT Schwarzman College of Computing, National Science Foundation (NSF), Physics, Research, School of Science

Curiosity-driven research has long sparked technological transformations. A century ago, curiosity about atoms led to quantum mechanics, and eventually the transistor at the heart of modern computing. Conversely, the steam engine was a practical breakthrough, but it took fundamental research in thermodynamics to fully harness its power. Today, artificial intelligence and science find themselves at a

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3 Questions: Building predictive models to characterize tumor progression

ai, AI (Artificial Intelligence), Artificial Intelligence, Biology, Cancer, Computational biology, DNA, Evolution, Faculty, Genetics, Health sciences and technology, Institute for Medical Engineering and Science (IMES), Interview, Koch Institute, Machine Learning, Medicine, School of Science

Just as Darwin’s finches evolved in response to natural selection in order to endure, the cells that make up a cancerous tumor similarly counter selective pressures in order to survive, evolve, and spread. Tumors are, in fact, complex sets of cells with their own unique structure and ability to change. Today, artificial Intelligence and machine learning

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3 Questions: Using AI to help Olympic skaters land a quint

ai, AI (Artificial Intelligence), Alumni/ae, Artificial Intelligence, data, IDSS, Interview, Literature, Mechanical engineering, MIT Sloan School of Management, Research, School of Engineering, Sports and fitness

Olympic figure skating looks effortless. Athletes sail across the ice, then soar into the air, spinning like a top, before landing on a single blade just 4-5 millimeters wide. To help figure skaters land quadruple axels, Salchows, Lutzes, and maybe even the elusive quintuple without looking the least bit stressed, Jerry Lu MFin ’24 developed

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3 Questions: Using AI to accelerate the discovery and design of therapeutic drugs

ai, AI (Artificial Intelligence), Antibiotics, Artificial Intelligence, Bacteria, Biological engineering, Broad Institute, Collaboration, Computer science and technology, Disease, Drug development, Faculty, Harvard-MIT Health Sciences and Technology, Health sciences and technology, Institute for Medical Engineering and Science (IMES), Interview, Jameel Clinic, Microbes, School of Engineering, Synthetic biology

In the pursuit of solutions to complex global challenges including disease, energy demands, and climate change, scientific researchers, including at MIT, have turned to artificial intelligence, and to quantitative analysis and modeling, to design and construct engineered cells with novel properties. The engineered cells can be programmed to become new therapeutics — battling, and perhaps

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3 Questions: How AI could optimize the power grid

ai, AI (Artificial Intelligence), Algorithms, Alternative energy, Artificial Intelligence, Batteries, Climate change, Computer science and technology, Electrical engineering and computer science (EECS), Energy, Energy storage, Faculty, Interview, Laboratory for Information and Decision Systems (LIDS), Machine Learning, MIT Schwarzman College of Computing, Pollution, Research, School of Engineering, Sustainability, Sustainable computing

Artificial intelligence has captured headlines recently for its rapidly growing energy demands, and particularly the surging electricity usage of data centers that enable the training and deployment of the latest generative AI models. But it’s not all bad news — some AI tools have the potential to reduce some forms of energy consumption and enable cleaner grids.One

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