Prof. Tomaso Poggio

Eugene McDermott Professor in the Brain Sciences and Human Behavior
Investigator, McGovern Institute
Director, Center for Biological and Computational Learning (CBCL)
Director, MIT Intelligence Initiative

Primary DLC

Department of Brain and Cognitive Sciences

MIT Room: 46-5177

Areas of Interest and Expertise

Learning and Networks
Machine and Human Vision
Nonlinear System Theory
Theory of Learning
Object Detection/Recognition
Virtual Financial Markets
Systems and Computational Neuroscience

Research Summary

Computational Neuroscience -- The scientific goal is to discover how intelligence is grounded in computation, how these computations are implemented in neural systems, how they develop during childhood, and how social interaction amplifies the power of these computations. As we progress, we will aggressively pursue opportunities to discover and develop unifying mathematical theories. To foster collaboration across disciplines, we will jointly develop top-to-bottom computational models powerful enough to explain visually perceived situations the way humans do. The models will emerge from fundamental questions about visually perceived situations: who, what, why, where, how, with what motives, with what purpose, and with what expectations. Models of visual understanding will be further advanced by developing computational models of what children know and learn about physical objects and intentional agents, and how they learn so much so rapidly. We will develop computational models of learning, memory, reasoning, and concept formation that are consistent with behavior, neural systems, and neural circuits. We will also develop computational models that enable computers to think new thoughts, imagine new scenes, form hypotheses, propose interventions, and compose narratives. Through these collaborative efforts, we will develop new methodologies and new technologies that will help to reach our goals. Our diversity goal is to ensure that the field of Science and Engineering of Intelligence is broadly inclusive. Our education goal is to ensure that our new knowledge is packaged in accessible ways, including model subjects at graduate and undergraduate levels. Our knowledge transfer goal is to ensure that new knowledge is quickly and broadly disseminated and brought to bear on the great challenges of the 21st century, so as to serve the people of the nation and the world.

Recent Work

  • Video

    Tomaso Poggio - 2016 Japan

    January 29, 2016Conference Video Duration: 39:35

    The Problem of Intelligence: Today’s Science, Tomorrows Engineering

    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.”