WashingtonMIT experts discuss brains, minds and machines
May 10, 2011
The central theme of “Brains, Minds and Machines,” the last of a series of symposia celebrating MIT’s 150th anniversary, was that it’s time for artificial-intelligence research, cognitive science, and neuroscience to get ambitious again.
The symposium was in part a launch party for MIT’s Intelligence Initiative (I2), a new program spanning all three disciplines and aiming, as its website puts it, to “answer the cosmic question of just how intelligence works.”
Sydney Brenner, who shared the 2002 Nobel Prize in physiology or medicine for his work on unraveling the genetic code, cautioned that genes are not simple blueprints for building organisms, but rather participants in a dynamic process that reflects many different stages of our evolutionary history.
Marvin Minsky, a professor emeritus at the MIT Media Lab, recalled that in the early 1960s, when MIT’s AI Lab was getting off the ground, he offered to send some of his graduate students to Brenner’s lab to help automate mapping of the C. elegans roundworm’s nervous system — a task that ended up taking Brenner 20 years.
Noam Chomsky contended that because humans are able to instantly infer the syntactic relationships between words that are far apart from each other in long sentences, the brain must represent sentence structure hierarchically rather than linearly.
Emilio Bizzi, an Institute Professor and co-founder of the McGovern Institute for Brain Research, described his lab’s discovery that the bewildering number of neural connections between the spinal cord and the muscles of the trunk and limbs are grouped into a finite set of control modules; different movements, he said, are the result of activating different modules in different orders.
Rodney Brooks, the Panasonic Professor Emeritus of Robotics at MIT and founder of iRobot, said developing robots that can function in the real world and interact with their surroundings has been a long, difficult process. “Perception is effortless for people, but in robots, it’s still largely unsolved,” he said.
Panelist Takeo Kanade, a professor at the Robotics Institute of Carnegie Mellon University, described the driverless cars he and his students have built, making use of computerized vision. He said one of the biggest obstacles is the difficulty — for machines — of recognizing the context in which an object appears.
Thomas Malone, director of MIT’s Center for Collective Intelligence, noted in his introduction that “intelligence is not just something that happens inside individual brains. It also arises within groups of individuals.” Humans and computers working together can form a collective far more intelligent than any single person or computer, he said.
Moderator Tomaso Poggio, the Eugene McDermott Professor in the Brain Sciences and Human Behavior and director of the Center for Biological and Computational Learning, suggested that, like the influenza virus, an infectious interest in artificial intelligence captures the scientific community about every 25 years.
MIT President Susan Hockfield, a member of the panel, agreed with Poggio that now is the time to “push neuroscience toward a new understanding of intelligence,” adding that because of its interdisciplinary approach, “MIT is the place where we can make real inroads into some of these problems.”
Robert Desimone, the Doris and Don Berkey Professor of Neuroscience and director of the McGovern Institute for Brain Research, described some of the new tools now available to neuroscientists to trace anatomical brain connections and monitor brain activity through electrical recording or imaging.
Biologist and Institute Professor Phillip Sharp talked about the importance of genomics in learning about how the human brain works. In the 10 years since the first draft of the human genome was completed, he pointed out, scientists have already identified genetic markers for psychotic diseases such as schizophrenia.
Co-moderator Josh Tenenbaum, an associate professor of computational cognitive science, pointed out the limitations of the highly specialized artificial intelligence systems that have been built so far. He pointed out there’s no such thing as a robot that can do everything a human can do, such as learn languages, play chess, sing and dance, negotiate a job offer, and interact in social relationships.
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