BCIG SPEAKER EVENT:
“Toward Human Level Machine Intelligence – Is It Achievable?”
- view the seminar archive
Clinical
Center (Building 10) Medical Board Room (Room 2C116)
ABSTRACT: Achievement of human level machine intelligence has long
been one of the basic objectives of AI. Officially, AI was born in 1956. Since
then, very impressive progress has been made in many areas—but not in the realm
of human level machine intelligence. Anyone who has been forced to use a dumb
automated customer service system will readily agree that human level machine
intelligence is not yet a reality. Today, no machine can pass the Turing test
and none is likely to do so in the foreseeable future. During much of its early
history, AI was rife with exaggerated expectations. A headline in an article
published in the late forties of last century was headlined, “Electric brain
capable of translating foreign languages is being built.” Today, more than half
a century later, we do have translation software, but nothing that can approach
the quality of human translation. Clearly, achievement of human level machine
intelligence is a challenge that is hard to meet. Humans have many remarkable
capabilities; there are two that stand out in importance. First, the capability
to reason, converse and make rational decisions in an environment of
imprecision, uncertainty, incompleteness of information and partiality of truth
and possibility. And second, the capability to perform a wide variety of
physical and mental tasks without any measurements and any computations. A
prerequisite to achievement of human level machine intelligence is mechanization
of these capabilities and, in particular, mechanization of natural language
understanding. In my view, mechanization of these capabilities is beyond the
reach of the armamentarium of AI—an armamentarium which in large measure employs
classical, Aristotelian, bivalent logic and bivalent-logic-based probability
theory. To make progress toward achievement of human level machine intelligence,
AI must add to its armamentarium concepts and techniques drawn from other
methodologies, especially evolutionary computing, neurocomputing and fuzzy
logic. A key contribution of fuzzy logic is the machinery of Computing with
Words (CW) and, more generally, NL-Computation. This machinery opens the door to
mechanization of natural language understanding and computation with information
described in natural language. Addition of this machinery to the armamentarium
of AI would be an important step toward the achievement of human level machine
intelligence and its applications in decision-making, pattern recognition,
analysis of evidence, diagnosis and assessment of causality. Such applications
have a position of centrality in medicine.
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3:00 - 4:30 pm April 10, 2008
Professor Lotfi Zadeh, Ph.D.
LOTFI A. ZADEH is a Professor in the Graduate School, Computer Science Division,
Department of EECS, University of California, Berkeley. In addition, he is
serving as the Director of BISC (Berkeley Initiative in Soft Computing). Lotfi
Zadeh is an alumnus of the University of Tehran, MIT and Columbia University. He
held visiting appointments at the Institute for Advanced Study, Princeton, NJ;
MIT, Cambridge, MA; IBM Research Laboratory, San Jose, CA; AI Center, SRI
International, Menlo Park, CA; and the Center for the Study of Language and
Information, Stanford University. His earlier work was concerned in the main
with systems analysis, decision analysis and information systems. His current
research is focused on fuzzy logic, computing with words and soft computing,
which is a coalition of fuzzy logic, neurocomputing, evolutionary computing,
probabilistic computing and parts of machine learning. Lotfi Zadeh is a Fellow
of the IEEE, AAAS, ACM, AAAI, and IFSA. He is a member of the National Academy
of Engineering and a Foreign Member of the Russian Academy of Natural Sciences,
the Finnish Academy of Sciences, the Polish Academy of Sciences, Korean Academy
of Science & Technology and the Bulgarian Academy of Sciences. He is a recipient
of the IEEE Education Medal, the IEEE Richard W. Hamming Medal, the IEEE Medal
of Honor, the ASME Rufus Oldenburger Medal, the B. Bolzano Medal of the Czech
Academy of Sciences, the Kampe de Feriet Medal, the AACC Richard E. Bellman
Control Heritage Award, the Grigore Moisil Prize, the Honda Prize, the Okawa
Prize, the AIM Information Science Award, the IEEE-SMC J. P. Wohl Career
Achievement Award, the SOFT Scientific Contribution Memorial Award of the Japan
Society for Fuzzy Theory, the IEEE Millennium Medal, the ACM 2001 Allen Newell
Award, the Norbert Wiener Award of the IEEE Systems, Man and Cybernetics
Society, Civitate Honoris Causa by Budapest Tech (BT) Polytechnical Institution,
Budapest, Hungary, the V. Kaufmann Prize, International Association for
Fuzzy-Set Management and Economy (SIGEF), the Nicolaus Copernicus Medal of the
Polish Academy of Sciences, the J. Keith Brimacombe IPMM Award, the Silicon
Valley Engineering Hall of Fame, the Heinz Nixdorf MuseumsForum Wall of Fame,
other awards and twenty-six honorary doctorates. He has published extensively on
a wide variety of subjects relating to the conception, design and analysis of
information/intelligent systems, and is serving on the editorial boards of over
sixty journals.
Professor in the Graduate School, Computer Science Division
Department of Electrical Engineering and Computer Sciences
University of California
Berkeley, CA 94720 -1776
Director, Berkeley Initiative in Soft Computing (BISC)
zadeh@eecs.berkeley.edu
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