Artificial intelligence summit addresses impact of technology on jobs and global economy
Speakers at the summit included Massachusetts Secretary of Labor Rosalin Acosta and former Google chairman Eric Schmidt.
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A bottom-up view of the state
Embedded with street-level bureaucrats, political theorist Bernardo Zacka reveals the complex moral landscape civil servants must navigate.
Bridge to the future of engineering
The School of Engineering’s faculty leadership weigh in on what the MIT Stephen A. Schwarzman College of Computing will mean for their students and faculty.
The many interfaces of computing
A roundup of MIT student research projects offers a glimpse of where computing is going next.
A ground-breaking mash-up
A new building at 314 Main Street for the MIT Museum, Boeing, and others brings Cambridge and MIT together for memorable HUBweek celebration.
Tang family gift supports MIT.nano, MIT Quest for Intelligence
$20 million gift names imaging suite in new facility, creates catalyst fund.
MIT reshapes itself to shape the future
Gift of $350 million establishes the MIT Stephen A. Schwarzman College of Computing, an unprecedented, $1 billion commitment to world-changing breakthroughs and their ethical application.
Regina Barzilay, James Collins, and Phil Sharp join leadership of new effort on machine learning in health
MacArthur “geniuses” in machine learning and synthetic biology to serve as faculty co-leads; Nobel laureate to chair advisory board of new research center.
Ideas abound at Quest for Intelligence workshop
Community event generates ideas for sparking innovative and ambitious plans to advance research in human and machine intelligence.
MIT Solve selects new class of innovators for 2018
At the Solve Challenge Finals, judges selected 33 tech entrepreneurs who will receive the support necessary to scale their solutions.
3Q: Antonio Torralba on what's next for the Quest for Intelligence
Inaugural director of The Quest discusses what's been accomplished since last spring's launch and what is on the horizon.
Reducing false positives in credit card fraud detection
Model extracts granular behavioral patterns from transaction data to more accurately flag suspicious activity.