Dr. Lotfi A. Zadeh: Toward Human Level Intelligence – Is it Achievable? – The Need for a Paradigm Shift
Professor Lotfi Zadeh
Abstract
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. The Turing Test lies far beyond. 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, 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 is based on classical, Aristotelian, bivalent logic, and bivalent-logic-based probability theory.
To make significant progress toward achievement of human level machine intelligence a paradigm shift is needed. More specifically, what is needed is an addition to the armamentarium of AI of two methodologies: (a) a nontraditional methodology of computing with words (CW) or more generally, NL-Computation; and (b) a countertraditional methodology which involves a progression from computing with numbers to computing with words. The centerpiece of these methodologies is the concept of precisiation of meaning. Addition of these methodologies to 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 our infocentric society.
Bio
Dr. 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).
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 focused on systems analysis, decision analysis, and information systems. Zadeh’s current research centers 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.
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 Museums Forum Wall of Fame, other awards, and twenty-six honorary doctorates.
Zadeh 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.
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. The Turing Test lies far beyond. 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, 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 is based on classical, Aristotelian, bivalent logic, and bivalent-logic-based probability theory.
To make significant progress toward achievement of human level machine intelligence a paradigm shift is needed. More specifically, what is needed is an addition to the armamentarium of AI of two methodologies: (a) a nontraditional methodology of computing with words (CW) or more generally, NL-Computation; and (b) a countertraditional methodology which involves a progression from computing with numbers to computing with words. The centerpiece of these methodologies is the concept of precisiation of meaning. Addition of these methodologies to 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 our infocentric society.
Bio
Dr. 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).
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 focused on systems analysis, decision analysis, and information systems. Zadeh’s current research centers 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.
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 Museums Forum Wall of Fame, other awards, and twenty-six honorary doctorates.
Zadeh 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.
