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THE TIMES OF ISRAEL | www.timesofisrael.com
Brains, minds, and
machines
JUNE 12, 2012, 8:48 PM
Tomaso Poggio is a Professor of Brain
and Cognitive Sciences and Co-Director
of the Center for Biological and
Computational Learning at the
Massachusetts Institute of Technology…
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Back in the 1950s and ’60s, vigorous intellectual activity
developed around the new engineering and physics of
electronics and information processing. MIT was a major
Institution contributing to this expanding whirlwind of
ideas. Building 20 was at its center, the focus of such
diverse fields as information theory, cybernetics, neural
networks, linguistics, neuroscience, and computer
science. Researchers roaming the building included
Claude Shannon, Norbert Wiener, Warren McCulloch,
Walter Pitts, Noam Chomsky, Jerry Lettvin, and Marvin
Minsky. The intellectual ferment eventually ignited an
ambitious attempt to understand intelligence and
replicate it in machines.
Marvin Minsky and Seymour Papert were key in
launching the field of artificial intelligence, while Noam
Chomsky and David Marr pioneered cognitive science
and computational neuroscience.
Over the past five decades the field of machine learning has quietly led to a host of technologies
that have changed our lives, beginning with computers such as Deep Blue grandmasters of
chess, and leading into Google search, speech recognition, face detection in digital cameras, cars
that see, and computers (like IBM’s Watson) that win at Jeopardy.
The next several years will be a golden age for the development of similar intelligent “apps” for
our phones, computers, and cars that will both help us work and entertain us. Cars equipped
with Mobileye cameras (an Israeli technology) already drive themselves. Orcam is working on a
system to help visually impaired people see obstacles and read. Each of these systems will be at
human level performance in a narrow domain of intelligence, yet none can really be called
intelligent. That is, none of them could be mistaken for a person; they would not pass the
Turing test. It seems that the problem of intelligence, of how the brain generates it, and of how
to make truly intelligent machines, is still wide open.
Intelligence presents one of the greatest problems in science today. I would argue that this
problem is greater than solving the questions of the origin of the universe, the structure of
matter, and the origin of life, because the brain is the very tool we use to understand all other
great problems. Understanding intelligence requires an understanding of how the brain works,
of how to build machines as intelligent as we are, and of how to develop more intelligent
organizations. Even a partial solution to the problem of intelligence has great potential benefits
for our society, technology, and the economy.
Not quite there yet. A screen capture of Apple's Siri software
Imagine a world where intelligence and its emergence from brain activity is truly understood.
Instead of narrow systems, such as Watson and Apple’s Siri, imagine really smart systems that
could change the world. Smart systems could revolutionize the education of people with special
needs and unusual learning styles, and provide compassionate care for the aged and challenged
with robots that anticipate their needs. Systems that recognize how culture influences thinking
could help avoid social conflict. The work of scientists and engineers could be amplified to help
solve the world’s most pressing technical problems. Mental health could be understood on a
deeper level to find better ways to intervene. These accomplishments will take decades. But the
time has come to actively and energetically pursue research focused on the scientific
foundations and practical applications of this ambitious goal.
The prospect of understanding intelligence is tantalizing and timeless. Yet, now is a uniquely
appropriate time to pursue this vision at the level of basic research and of technology
development.
Why now? Because today, the key fields of cognitive science, neuroscience, and computer
science/artificial intelligence are re-converging. This convergence is driven by powerful new
tools that allow studies of the brain and mind that inform the design of intelligent artifacts and
vice versa – a positive loop from science to engineering and from engineering to science. These
new tools include a dramatic increase in computing power and storage, the availability of
massive datasets, the development of mathematical frameworks for learning, and the broad
progress in neuroscience over the course of the past five decades.
Integrating intelligence across vision, language, planning, and other domains is possible now
because of advances in understanding the cognitive, neural, and computational bases of each
domain. A developmental approach to intelligence is now possible because of revolutionary
work in infant cognition based on looking time, reaching, and increasingly large-scale video
data collection, and physiological and neuroimaging methods. Neuroimaging, plus genetic
methods of dissecting the neural circuits underlying learning and decision, now allows the
study of intelligence and the brain at both large and small scales.
Recent discoveries about brain systems and innate behavioral capacities for social cognition,
and the development of robotic and AI systems that for the first time interact intelligently with
humans, make it clear that we must study intelligence in a social context. (I will be discussing
these discoveries in great detail during my masterclass session at the upcoming Israeli
Presidential Conference in Jerusalem.)
The broader impact of this research effort will be the establishment of a new field: the Science
and Engineering of Intelligence. This new area of research and technology will leverage the
progress in computer science, neuroscience, and cognitive science. The ability to develop more
intelligent machines will influence a technology-based economy in the long term. The dream is
to understand, at the computational level, how human intelligence emerges from the
integration of nature and nurture — innate capacities and learning — and how we may
reproduce this aspect of intelligent behavior in machines.
Success will ultimately enable us to understand ourselves better, to produce more intelligent
machines, and perhaps even to make ourselves smarter.
The opinions and facts here are presented solely by the author, and The Times of Israel assumes no responsibility for them.