Computers and automated machines such as robots have reached an enormous
level of sophistication, but they can't actually think—yet. But they
can
remember, recognize patterns, and make inferences and deductions, all
in ways that can be used in real-world industrial, commercial, and
scientific applications. Such phenomena are the basis of artificial
intelligence, the branch of computer science that encompasses the
increasingly important field of machine learning: the creation and
development of algorithms that allow machines to learn from new data and
change their behavior accordingly. Perhaps the most influential and
innovative researcher in machine learning is Vladimir Vapnik, whose
career and accomplishments practically define the discipline's current
state of the art.
Machine learning and artificial intelligence
draw heavily from mathematics and statistics. Vapnik obtained his M.S.
in the former field in 1958 from Uzbek State University and his
doctorate in the latter field in 1964 from Moscow's Institute of Control
Sciences, where he would later become head of the computer science
department. It was here that he began the work that ultimately led to
his development, in collaboration with Alexey Chervonenkis, of
Vapnik-Chervonenkis (VC) theory, which uses statistical and mathematical
methods to explain the learning process, establishing the foundations
of contemporary machine learning theory.
The seminal importance
of Vapnik;s work did not begin to be fully recognized until he had the
opportunity to leave Soviet Russia for an extended visit to the United
States in 1989. He emigrated permanently to the U.S. in 1991 to take up a
position at AT&T Bell Labs. At Bell, Vapnik continued to develop
and build upon the ideas and implications of VC theory, finally
inventing the concept of the support vector machine (SVM), a model and
algorithm that allows a computer to identify and predict patterns and
classify input into particular categories.
Its mathematically
complex underpinnings may make machine learning appear to be mostly an
abstract theoretical exercise, but Vapnik's research and particularly
his SVM concept have led to a staggering variety of practical everyday
applications. Machine learning algorithms are at the heart of fraud
detection in electronic credit card transactions, computer security,
speech and handwriting recognition, computerized medical diagnosis, DNA
analysis, cataloging and data mining, and a host of other critical
functions involving the identification and recognition of patterns and
the classification of different types of information. In these and many
other functions, Vapnik's work has moved directly from abstract theory
to practical use.
2012 Ben Franklin Award Winner for Computer and Cognitive Science For more information, please visit https://www.fi.edu/laureates/vladimir-vapnik
One of the most fascinating aspects of
artificial intelligence and machine learning research is that it can
provide insight into one of science's most profound mysteries: the
workings of our own brains and consciousness. Although these questions
are not the focus of Vapnik's research, his models of the processes at
work during learning and pattern recognition provide another perspective
guiding the efforts of scientists studying how the human brain
organizes and performs those functions.
Currently Vapnik is a
professor of computer science and statistics at Royal Holloway,
University of London, and holds a professorship in computer science at
Columbia University in New York. Also working on staff at NEC Labs in
Princeton, New Jersey, he continues to forge new paths in advanced
mathematics, statistics, and their interactions and interconnections
within computer science. His honors and awards include election to the
National Academy of Engineering in 1996, the 2008 Paris Kanellakis
Award, and the Alexander Humboldt Research Award for Lifetime
Achievement.
While the question of whether computers will ever be
able to think as human beings do may never be truly answered, there is
no question that Vladimir Vapnik has invented ways to make them "think"
better for all the myriad ways in which we humans use them every day.
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