Principal Investigator Anantha Chandrakasan
AI-Powered Integrated Development Environments
Adam Chlipala Arthur J. Conner (1888) Professor, MIT Department of Electrical Engineering and Computer Science (EECS)
Single-cell mass as a clinical tool for guiding treatment decisions
Keith Ligon Associate Professor of Pathology at Harvard Medical School Scott Manalis Andrew and Erna Viterbi Professor of Biological Engineering Clifford Reid CEO of Travera
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Tricia Dinkel Manager of Partnerships & Engagement, MIT Startup Exchange
The birth of artificial-intelligence research as an autonomous discipline is generally thought to have been the month long Dartmouth Summer Research Project on Artificial Intelligence in 1956, which convened 10 leading electrical engineers — including MIT’s Marvin Minsky and Claude Shannon — to discuss “how to make machines use language” and “form abstractions and concepts.” A decade later, impressed by rapid advances in the design of digital computers, Minsky was emboldened to declare that “within a generation ... the problem of creating ‘artificial intelligence’ will substantially be solved.”
The problem, of course, turned out to be much more difficult than AI’s pioneers had imagined. In recent years, by exploiting machine learning — in which computers learn to perform tasks from sets of training examples — artificial-intelligence researchers have built special-purpose systems that can do things like interpret spoken language or play Atari games or drive cars using vision with great success.
But according to Tomaso Poggio, the Eugene McDermott Professor of Brain Sciences and Human Behavior at MIT, “These recent achievements have, ironically, underscored the limitations of computer science and artificial intelligence. We do not yet understand how the brain gives rise to intelligence, nor do we know how to build machines that are as broadly intelligent as we are.”
Poggio thinks that AI research needs to revive its early ambitions. “It’s time to try again,” he says. “We know much more than we did before about biological brains and how they produce intelligent behavior. We’re now at the point where we can start applying that understanding from neuroscience, cognitive science and computer science to the design of intelligent machines.”
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