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3 Questions: What is the best path forward for AI in academia?

ai, AI (Artificial Intelligence), Artificial Intelligence, Brain and cognitive sciences, Computer science and technology, Education, teaching, academics, Faculty, IDSS, Interview, MIT Schwarzman College of Computing, School of Engineering, School of Science, Technology and society

With artificial intelligence capabilities continuing to expand, universities now face complex, urgent questions about how to navigate increasing challenges, as well as new opportunities. Much attention is now directed toward this, including an MIT report on AI and education published this summer. A new essay from MIT Statistics and Data Science Center Director Sasha Rakhlin, the Distinguished Professor

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Using AI to mitigate the growing environmental threat of data centers

ai, AI (Artificial Intelligence), Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, data, Electrical engineering and computer science (EECS), Energy, Faculty, Machine Learning, MIT Schwarzman College of Computing, Profile, School of Engineering, Sustainability, Sustainable computing, Systems design

The global building boom of power-hungry data centers is straining electrical grids, causing greater reliance on energy from polluting fossil fuels.Christina Delimitrou, a newly tenured associate professor at MIT, is fighting this environmental threat by rethinking how the computer servers and networking equipment inside those data centers operate. She and her group apply machine learning to

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MIT announces the MIT for America initiative, to strengthen STEM education across the country

Administration, ai, AI (Artificial Intelligence), Artificial Intelligence, Education, teaching, academics, Faculty, K-12 education, President Sally Kornbluth, Staff, STEM education, Technology and society, Volunteering, outreach, public service

MIT is launching a new strategic initiative today: MIT for America, which seeks to strengthen STEM education nationally, at many levels of learning. Addressing a critical national need, the initiative ranges across STEM fields and will engage students from kindergarten through community college, preparing them to live and work in a world increasingly shaped by science

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Computational tools for society’s most complex challenges

ai, AI (Artificial Intelligence), Algorithms, Artificial Intelligence, Autonomous vehicles, Civil and environmental engineering, Computer science and technology, Electrical engineering and computer science (EECS), Faculty, IDSS, Laboratory for Information and Decision Systems (LIDS), Logistics, Machine Learning, MIT Schwarzman College of Computing, Profile, Robotics, School of Engineering, Supply chains, Technology and society, Traffic management, Transportation

As far back as she can remember, Cathy Wu ’12, MNG ’13 wanted to find ways to solve problems to improve people’s lives. Her parents were Taiwanese immigrants, and her father had a long commute to his job, which took him away from the family. On a tight budget, the rest of the family often

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3 Questions: A new resource to empower young entrepreneurs

ai, AI (Artificial Intelligence), Artificial Intelligence, Education, teaching, academics, Faculty, Innovation and Entrepreneurship (I&E), Interview, K-12 education, Martin Trust Center for MIT Entrepreneurship, MIT Sloan School of Management, Online learning, Startups

The book “Disciplined Entrepreneurship” by Bill Aulet, managing director of the Martin Trust Center for MIT Entrepreneurship and the Ethernet Inventors Professor of the Practice at the MIT Sloan School of Management, walks readers through the 24 steps of starting a venture. With more than half a million copies sold, the approach has proven remarkably

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Who we become when we talk to machines

ai, AI (Artificial Intelligence), Artificial Intelligence, Books and authors, Faculty, Mental health, Program in STS, Psychology, Research, School of Humanities Arts and Social Sciences, Social sciences, Technology and society

Brian, a middle-aged financial consultant, spends his day sitting in front of three screens. Two of them involve his job. The third features a chatbot, which he has given a woman’s name and frequently uses. Brian spent years on the road for work earlier in his career, has never married, and recently broke up with

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Measure by measure, studying society accurately

ai, AI (Artificial Intelligence), Artificial Intelligence, Faculty, IDSS, MIT Schwarzman College of Computing, Political science, Profile, School of Humanities Arts and Social Sciences, Social sciences, Technology and society, Voting and elections

Let’s agree at the outset the world is a complicated place, and social scientists have exacting jobs when it comes to measuring civic phenomena with precision. After all, even careful studies raise follow-up questions: How much do their findings apply in other settings? Do conclusions about politics in one country apply to other countries? If you’re

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Q&A: Rethinking how innovation happens

ai, AI (Artificial Intelligence), Artificial Intelligence, Books and authors, Business and management, DMSE, Faculty, Innovation and Entrepreneurship (I&E), Interview, Invention, School of Engineering, semiconductors, Singapore-MIT Alliance for Research and Technology (SMART)

Innovation is a concept that has become mythologized in the modern era: what it is, how to manage it, how to teach it, and how to get it to work for us. Despite these explorations, it remains fundamentally misunderstood, writes Eugene Fitzgerald, the Merton C. Flemings SMA Professor in MIT’s Department of Materials Science and

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Q&A: Rethinking how innovation happens

ai, AI (Artificial Intelligence), Artificial Intelligence, Books and authors, Business and management, DMSE, Faculty, Innovation and Entrepreneurship (I&E), Interview, Invention, School of Engineering, semiconductors, Singapore-MIT Alliance for Research and Technology (SMART)

Innovation is a concept that has become mythologized in the modern era: what it is, how to manage it, how to teach it, and how to get it to work for us. Despite these explorations, it remains fundamentally misunderstood, writes Eugene Fitzgerald, the Merton C. Flemings SMA Professor in MIT’s Department of Materials Science and

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Alexander Rakhlin named director of the MIT Statistics and Data Science Center

ai, AI (Artificial Intelligence), Alumni/ae, Artificial Intelligence, Brain and cognitive sciences, Computer science and technology, Electrical engineering and computer science (EECS), Faculty, IDSS, Leadership, Machine Learning, MIT Schwarzman College of Computing, School of Engineering, School of Science, Statistics

Alexander “Sasha” Rakhlin PhD ’06, the Distinguished Professor in Data, Systems, and Society at the MIT Institute for Data, Systems, and Society (IDSS); and a professor of brain and cognitive sciences at MIT, has been named the next director of the MIT Statistics and Data Science Center (SDSC). Rakhlin succeeds Ankur Moitra, the Norbert Wiener Professor of Mathematics,

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