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Toward Human-Level Machine Intelligence Lotfi A. Zadeh Computer Science Division Department of EECS UC Berkeley IC

LAZ 10/13/2006. 2/49. BACKDROP. LAZ 10/13/2006. 3/49. PREAMBLE. We are in the midst of what is popularly called the information revolutiona revolution which was born shortly after the end of World War II.As a student at MIT and later as an instructor at Columbia University, I witnessed the

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Toward Human-Level Machine Intelligence Lotfi A. Zadeh Computer Science Division Department of EECS UC Berkeley IC

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    2. LAZ 10/13/2006 2/49

    3. LAZ 10/13/2006 3/49 PREAMBLE We are in the midst of what is popularly called the information revolution—a revolution which was born shortly after the end of World War II. As a student at MIT and later as an instructor at Columbia University, I witnessed the birth of this revolution and observed at close distance its progression and impact

    4. LAZ 10/13/2006 4/49 THE BEGINNING OF THE AGE OF INFORMATION AND CONTROL Three major events (ca.1946)heralded the beginning of the age of information and control Invention of the transistor Debut of cybernetics (Wiener) Debut of information theory (Shannon) I heard the first presentation by Shannon of his work at a meeting in New York, in 1946, and was deeply fascinated by his ideas. His lecture opened a new world

    5. LAZ 10/13/2006 5/49 THE NEW WORLD The new world was the world of machine intelligence and automated reasoning It was widely believed that there were no limits to what machines could do The era of thinking machines has arrived Inspired by what I saw, heard and read, I wrote an article about thinking machines which was published in a student magazine

    6. LAZ 10/13/2006 6/49 THINKING MACHINES—A NEW FIELD IN ELECTRICAL ENGINEERING “Psychologists Report Memory is Electrical,” “Electric Brain Able to Translate Foreign Languages is Being Built,” Electronic Brain Does Research,” “Scientists Confer on Electronic Brain,”—these are some of the headlines that were carried in newspapers throughout the nation during the past year. What is behind these headlines? How will “electronic brains” or “thinking machines” affect our way of living? What is the role played by electrical engineers in the design of these devices? These are some of the questions that we shall try to answer in this article.

    7. LAZ 10/13/2006 7/49 CONTINUED Through their association with mathematicians, electrical engineers working on thinking machines have become familiar with such hitherto remote subjects as Boolean algebra, multivalued logic, and so forth. And it seems that the time is not far distant when taking a course in mathematical logic will be just as essential to a graduate student in electrical engineering as taking a course in complex variable is at the present time. Time marches on.

    8. LAZ 10/13/2006 8/49 EXAGGERATED EXPECTATIONS One of the headlines read "Electric Brain Capable of Translating Foreign Languages is Being Built." Considering that the only computers that were in existence at that time were relay computers, gives an idea of the depth of underestimation of the difficulty of building machines that can come close to human-level intelligence. Today, close to sixty years later, we have machine translation programs but their performance leaves much to be desired.

    9. LAZ 10/13/2006 9/49 EXAGGERATED EXPECTATIONS On the occasion of inauguration of IBM’s Mark 1 relay computer in 1948, Howard Aiken, Director of Harvard’s Computation Laboratory, had this to say: There is no problem in applied mathematics that this computer cannot solve In 1953, Burroghs Corporation started a project to design, manufacture and market a phonetic typewriter

    10. LAZ 10/13/2006 10/49 EXAGGERATED EXPECTATIONS Like others, I had exaggerated expectations. Here is an example drawn from my 1950 paper.

    11. LAZ 10/13/2006 11/49 A GLIMPSE INTO THE FUTURE (LAZ 1950) It is 1965. Three years ago for reasons of economy and efficiency the trustees of Columbia University have decided to disband the Office of University Admissions and to install in its place a thinking machine to be called the Electronic Director of Admissions. Installation was completed in the spring of 1964, and since then the Director has been functioning perfectly and has won unanimous acclaim from administration, faculty and student body alike

    12. LAZ 10/13/2006 12/49 ELECTRONIC DIRECTOR OF ADMISSIONS (1950)

    13. LAZ 10/13/2006 13/49 BRILLIANT SUCCESSES AND CONSPICUOUS FAILURES successes landing men on the moon GPS systems search engines bioinformatics failures summarization simultaneous translation automation of driving in city traffic tennis-playing robot

    14. LAZ 10/13/2006 14/49 INFORMATION SYSTEMS / INTELLIGENT SYSTEMS

    15. LAZ 10/13/2006 15/49 MACHINE INTELLIGENT QUOTIENT (MIQ) (ZADEH 1993) Dimension of MIQ handwriting recognition speech recognition natural language understanding summarization disambiguation image understanding and pattern recognition diagnostics unstructured storage and retrieval of information execution of high level instructions (expressed in NL) learning reasoning planning problem solving decision making

    16. LAZ 10/13/2006 16/49 information/intelligent systems are emerging as the primary component of the infrastructure of modern societies conception, design, construction and utilization of information/intelligent systems constitute the core of modern science and technology INFORMATION /INTELLIGENT SYSTEMS (I/IS)

    17. LAZ 10/13/2006 17/49 ULTIMATE GOAL

    18. LAZ 10/13/2006 18/49 INFORMATION SYSTEM vs. INTELLIGENT INFORMATION SYSTEM

    19. LAZ 10/13/2006 19/49 INFORMATION/INTELLIGENT SYSTEMS (I/IST) Information/intelligent systems are becoming a reality But why did it take so long? The necessary technologies and methodologies were not in place Key technologies: advanced computer hardware and software advanced sensor hardware and software Key methodology: soft computing

    20. LAZ 10/13/2006 20/49 TIMELINE OF GROWTH OF MIQ (LAZ)

    21. LAZ 10/13/2006 21/49 ACHIEVEMENT OF HUMAN-LEVEL MACHINE INTELLIGENCE Humans have a remarkable capability to perform a wide variety of physical and mental tasks without any measurements and any computations. Familiar examples are: driving in city traffic; summarizing a story; and playing tennis. In performing such tasks humans employ perceptions—perceptions of distance, speed, direction, intent and other attributes of physical and mental objects.

    22. LAZ 10/13/2006 22/49 CONTINUED Limitations of today’s AI reflect the incapability of existing AI techniques to deal with perception-based information. What is widely unrecognized is that to achieve human-level machine intelligence it is necessary to endow AI with the capability to deal with perception-based information.

    23. LAZ 10/13/2006 23/49

    24. LAZ 10/13/2006 24/49 BIRTH OF AI Officially, AI was born in l956. At its birth there was widespread expectation that within a few years it will be possible to build machines that could think like humans. The AI pioneers, notably John McCarthy, Herbert Simon, Allen Newell and Neils Nilsson, but not Marvin Minsky, were firm believers in the ability of classical symbolic logic to lead to human-level machine intelligence.

    25. LAZ 10/13/2006 25/49 CONTINUED I did not share that belief because the world of symbolic logic is an unreal world in which there is no imprecision, no uncertainty and no partiality of knowledge, truth and class membership. The world of symbolic logic is an idealized model of the real world. 

    26. LAZ 10/13/2006 26/49 NEW AI For the AI establishment, anything that involved numerical computations was unwelcome. It took close to thirty years for probability theory to gain grudging acceptance. In large measure, it was the work of Judea Pearl that made probability theory respectable. Today, so-called "New AI" is probability-based. Indeed, Bayesianism has become as fashionable as symbolic logic was in its time.

    27. LAZ 10/13/2006 27/49 CONTINUED Clearly, adding probability theory to the armamentarium of AI is a step in the right direction. But is it sufficient? In my view, the answer is: No.

    28. LAZ 10/13/2006 28/49 CONTINUED This view was advanced in my paper “A New Direction in AI—Toward a Computational Theory of Perceptions,” which was published in the AI Magazine, Vol. 22, No. 1, 73-84, 2001. The initial reviews of my paper were critical. Eventually, my paper was accepted for publication with its provocative title. My principal conclusion was that to achieve human-level machine intelligence it is necessary to have a capability to deal with perception-based information.

    29. LAZ 10/13/2006 29/49 THE PROBLEM OF IMPRECISION Perceptions are intrinsically imprecise, reflecting the bounded ability of human sensory organs and ultimately the brain, to resolve detail and store information. It is this imprecision that places computation and reasoning with perceptions beyond the reach of symbolic logic and probability theory.

    30. LAZ 10/13/2006 30/49 A NEW DIRECTION My AI Magazine paper outlined what may be called the computational theory of perceptions. The key idea in this theory is that of dealing with perceptions through their descriptions in a natural language. In other words, a perception is equated to its description, and computation with perceptions is reduced to computation with information which is described in natural language.

    31. LAZ 10/13/2006 31/49 CONTINUED For this purpose, what is employed is fuzzy logic—a logic which mirrors the remarkable cability of human mind to reason with information which is imprecise, uncertain and partially true.

    32. LAZ 10/13/2006 32/49 CONTINUED The distinguishing features of fuzzy logic are graduation and granulation. More specifically, in fuzzy logic everything is or is allowed to be graduated, that is, be a matter of degree, or equivalently, fuzzy. Furthermore, in fuzzy logic every variable is or is allowed to be granulated, with a granule being a clump of values which are drawn together by indistinguishability, similarity or proximity.

    33. LAZ 10/13/2006 33/49 CONTINUED A granule may be interpreted as a representation of one’s state of knowledge regarding the true value of the variable. As a simple example, Age is granulated when its granular values are assumed to be young, middle-aged and old. Graduation and granulation play essential roles in human cognition.

    34. LAZ 10/13/2006 34/49 THE CONCEPT OF GRANULAR VALUE

    35. LAZ 10/13/2006 35/49 GRANULATION OF A VARIABLE continuous quantized granulated Example: Age

    36. LAZ 10/13/2006 36/49

    37. LAZ 10/13/2006 37/49 ANALOGY In bivalent logic, one writes and draws with a ballpoint pen In fuzzy logic, one writes and draws with a spray pen which has an adjustable and precisely defined spray pattern This simple analogy suggests many mathematical problems What is the maximum value of f? Precisiation/imprecisiation principle

    38. LAZ 10/13/2006 38/49 A BIT OF HISTORY A precursor of fuzzy logic was the theory of fuzzy sets. Informally, a fuzzy set is a class without unsharp boundaries, e.g., the class of beautiful women, the class of honest men, and a class of historical monument. My first paper on fuzzy sets appeared in 1965.

    39. LAZ 10/13/2006 39/49 CONTINUED Its reception was mixed and mostly critical. My best friend, Richard Bellman, the father of dynamic programming was one of the few who welcomed the idea. This is what he wrote when I sent him the manuscript of my paper, “Fuzzy Sets.”

    40. LAZ 10/13/2006 40/49 Journal of Mathematical Analysis and Applications Dear Lotfi: I think that the paper is extremely interesting and I would like to publish it in JMAA, if agreeable to you. When I return, or while in Paris, I will write a companion paper on optimal decomposition of a set into subsets along the lines of our discussion. Cordially, Richard Bellman CONTINUED

    41. LAZ 10/13/2006 41/49 Many others were not so kind. Here is a sample. Following the presentation of my first paper on the concept of a linguistic variable, Professor Rudolf Kalman, a brilliant scientist and a good friend of mine, had this to say: CONTINUED

    42. LAZ 10/13/2006 42/49 “I would like to comment briefly on Professor Zadeh’s presentation. His proposals could be severely, ferociously, even brutally criticized from a technical point of view. This would be out of place here. But a blunt question remains: Is Professor Zadeh presenting important ideas or is he indulging in wishful thinking? CONTINUED

    43. LAZ 10/13/2006 43/49 No doubt Professor Zadeh’s enthusiasm for fuzziness has been reinforced by the prevailing climate in the U.S.—one of unprecedented permissiveness. ‘Fuzzification’ is a kind of scientific permissiveness; it tends to result in socially appealing slogans unaccompanied by the discipline of hard scientific work and patient observation.” CONTINUED

    44. LAZ 10/13/2006 44/49 In a similar vein, my esteemed colleague Professor William Kahan—a man with a brilliant mind—offered this assessment in 1975. “Fuzzy theory is wrong, wrong, and pernicious.” says William Kahan, a professor of computer sciences and mathematics at Cal whose Evans Hall Office is a few doors from Zadeh’s. “I can not think of any problem that could not be solved better by ordinary logic.” CONTINUED

    45. LAZ 10/13/2006 45/49 “What Zadeh is saying is the same sort of things ‘Technology got us into this mess and now it can’t get us out.’” Kahan says. “Well, technology did not get us into this mess. Greed and weakness and ambivalence got us into this mess. What we need is more logical thinking, not less. The danger of fuzzy theory is that it will encourage the sort of imprecise thinking that has brought us so much trouble.”

    46. LAZ 10/13/2006 46/49   PATENTS Number of fuzzy-logic-related patents applied for in Japan: 17,740 Number of fuzzy-logic-related patents issued in Japan:  4,801 Number of fuzzy-logic-related patents issued in the US: around 1,700

    47. LAZ 10/13/2006 47/49 PUBLICATIONS   Count of papers containing the word “fuzzy” in title, as cited in INSPEC and MATH.SCI.NET databases. Compiled by Camille Wanat, Head, Engineering Library, UC Berkeley, August 25, 2006.   Number of papers in INSPEC and MathSciNet which have "fuzzy" in title:

    48. LAZ 10/13/2006 48/49 JOURNALS (“fuzzy” or “soft computing” in title)   Fuzzy Sets and Systems IEEE Transactions on Fuzzy Systems Fuzzy Optimization and Decision Making Journal of Intelligent & Fuzzy Systems Fuzzy Economic Review International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems Journal of Japan Society for Fuzzy Theory and Systems International Journal of Fuzzy Systems International Review of Fuzzy Mathematics Soft Computing International Journal of Approximate Reasoning--Soft Computing in Recognition and Search Intelligent Automation and Soft Computing Journal of Multiple-Valued Logic and Soft Computing Mathware and Soft Computing Biomedical Soft Computing and Human Sciences Applied Soft Computing

    49. LAZ 10/13/2006 49/49 SUMMATION Humans have a remarkable capability to reason and make decisions in an environment of imprecision, uncertainty and partiality of knowledge, truth and class membership. It is this capability that is needed to achieve human-level machine intelligence. Achievement of human-level machine intelligence is beyond the reach of existing AI techniques. New direction is needed. Computational theory of perceptions is a step in this direction.

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