2008 TowardHumanLevelMachineIntellig

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Subject Headings: Human-level Intelligent Machine.

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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. 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. A prerequisite to achievement of human level machine intelligence is mechanization of these capabilities and, in particular, mechanization of natural language understanding. 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.

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10. Concluding Remarks

There are many reasons why achievement of human level machine intelligence is a challenge that is hard to meet. One of the principal reasons is the need for mechanization of two remarkable human capabilities. First, the capability to converse, communicate, reason and make rational decisions in an environment of imprecision, uncertainty, incompleteness of information, partiality of truth and partiality of possibility. And second, the capability to perform a wide variety of physical and mental tasks — such as driving a car in heavy city traffic — without any measurements and any computations. What is well understood is that a prerequisite to mechanization of these capabilities is mechanization of natural language understanding. But what is widely unrecognized is that mechanization of natural language understanding is beyond the reach of methods based on bivalent logic and bivalent-logic-based probability theory. In addition, what is widely unrecognized is that mechanization of natural language understanding is contingent on precisiation of meaning.

Humans can understand unprecisiated natural language but machines cannot. Natural languages are intrinsically imprecise. Basically, a natural language is a system for describing perceptions. Perceptions are intrinsically imprecise. Imprecision of natural languages is rooted in imprecision of perceptions.

The principal thesis of this paper is that to address the problem of precisiation of meaning it is necessary to employ the machinery of fuzzy logic. In addition, the machinery of fuzzy logic is needed for mechanization of human reasoning. In this perspective, fuzzy logic is of direct relevance to achievement of human level machine intelligence. The cornerstones of fuzzy logic are the concepts of graduation, granulation, precisiation and generalized constraint

References

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2008 TowardHumanLevelMachineIntelligLotfi A. ZadehToward Human Level Machine Intelligence - Is It Achievable? The Need for a Paradigm Shift10.1109/MCI.2008.9265832008
AuthorLotfi A. Zadeh +
doi10.1109/MCI.2008.926583 +
titleToward Human Level Machine Intelligence - Is It Achievable? The Need for a Paradigm Shift +
year2008 +