Public
Whaddya know. Every now and then somebody says something about AI that sounds plain crazy ("pattern recognition depends on human values"), and when you look at their argument, it turns out that they actually make sense.
"According to the theorem of the Ugly Duckling, any pair of nonidentical objects share an equal number of predicates as any other pair of nonidentical objects, insofar as the number of predicates is finite [10], [12]. That is to say, from a logical point of view there is no such thing as a natural kind. In the case of pattern recognition, the new arrival shares the same number of predicates with any other paradigm of any class. This shows that pattern recognition is a logically indeterminate problem. The class-defining properties are generalizations of certain of the properties shared by the paradigms of the class. Which of the properties should be used for generalization is not logically defined. If it were logically determinable, then pattern recognition would have a definite answer in violation of the theorem of the Ugly Duckling.
"This conclusion is somewhat disturbing because our empirical knowledge is based on natural kinds of objects. The source of the trouble lies in the fact that we were just counting the number of predicates in the foregoing, treating them as if they were all equally important. The fact is that some predicates are more important than some others. Objects are similar if they share a large number of important predicates.
"Important in what scale? We have to conclude that a predicate is important if it leads to a classification that is useful for some purpose. From a logical point of view, a whale can be put together in the same box with a fish or with an elephant. However, for the purpose of building an elegant zoological theory, it is better to put it together with the elephant, and for classifying industries it is better to put it together with the fish. The property characterizing mammals is important for the purpose of theory building in biology, while the property of living in water is more important for the purpose of classification of industries.
"The conclusion is that classification is a value-dependent task and pattern recognition is mechanically possible only if we smuggle into the machine the scale of importance of predicates. Alternatively, we can introduce into the machine the scale of distance or similarity between objects. This seems to be an innocuous set of auxiliary data, but in reality we are thereby telling the machine our value judgment, which is of an entirely extra-logical nature. The human mind has an innate scale of importance of predicates closely related to the sensory organs. This scale of importance seems to have been developed during the process of evolution in such a way as to help maintain and expand life [12], [14]."
(Watanabe, S. [1974] Paradigmatic Symbol-A Comparative Study of Human and Artificial Intelligence. IEEE Transactions on Systems, Man and Cybernetics, SMC-4 , Issue: 1, 100 - 103.)
"According to the theorem of the Ugly Duckling, any pair of nonidentical objects share an equal number of predicates as any other pair of nonidentical objects, insofar as the number of predicates is finite [10], [12]. That is to say, from a logical point of view there is no such thing as a natural kind. In the case of pattern recognition, the new arrival shares the same number of predicates with any other paradigm of any class. This shows that pattern recognition is a logically indeterminate problem. The class-defining properties are generalizations of certain of the properties shared by the paradigms of the class. Which of the properties should be used for generalization is not logically defined. If it were logically determinable, then pattern recognition would have a definite answer in violation of the theorem of the Ugly Duckling.
"This conclusion is somewhat disturbing because our empirical knowledge is based on natural kinds of objects. The source of the trouble lies in the fact that we were just counting the number of predicates in the foregoing, treating them as if they were all equally important. The fact is that some predicates are more important than some others. Objects are similar if they share a large number of important predicates.
"Important in what scale? We have to conclude that a predicate is important if it leads to a classification that is useful for some purpose. From a logical point of view, a whale can be put together in the same box with a fish or with an elephant. However, for the purpose of building an elegant zoological theory, it is better to put it together with the elephant, and for classifying industries it is better to put it together with the fish. The property characterizing mammals is important for the purpose of theory building in biology, while the property of living in water is more important for the purpose of classification of industries.
"The conclusion is that classification is a value-dependent task and pattern recognition is mechanically possible only if we smuggle into the machine the scale of importance of predicates. Alternatively, we can introduce into the machine the scale of distance or similarity between objects. This seems to be an innocuous set of auxiliary data, but in reality we are thereby telling the machine our value judgment, which is of an entirely extra-logical nature. The human mind has an innate scale of importance of predicates closely related to the sensory organs. This scale of importance seems to have been developed during the process of evolution in such a way as to help maintain and expand life [12], [14]."
(Watanabe, S. [1974] Paradigmatic Symbol-A Comparative Study of Human and Artificial Intelligence. IEEE Transactions on Systems, Man and Cybernetics, SMC-4 , Issue: 1, 100 - 103.)
Kolmogorov prior.Jul 24, 2012
