This is an attempt to analyze attitudes and arguments brought forth by questions like "Can machines think?" and "Can machines exhibit intelligence?" Its purpose is to improve the climate which surrounds research in the field of machine or artificial intelligence. Its goal is not to convince those who answer the above questions negatively that they are wrong (although an attempt will be made to refute some of the negative arguments) but that they should be tolerant of research investigating these questions. The negative attitudes existent today tend to inhibit such research.Reprinted in Feigenbaum & Feldman, Computers and Thought (1963).Also in Datamation 9(3), March 1963, pp.34-38.Symposium on Bionics, Rand Technical Report 60 600, pp. 13-19

Journal of the Association for Computing Machinery, Vol 7, pp. 102-128

This is the first clear call for the separation of knowledge and inference procedure in AI. In this paper McCarthy advocates using predicate logic as a declarative representation of knowledge and first-order logic as the inference procedure.Additional notes on this landmark paper at http://www-formal.stanford.edu/jmc/mcc59/mcc59.html.Bar-Hilel's comments in the discussion section from the conference are also interesting:"PROF. Y. BAR-HILLEL: Dr. McCarthy's paper belongs in the Journal of Half-Baked Ideas, the creation of which was recently proposed by Dr. I. J. Good. Dr. McCarthy will probably be the first to admit this. Before he goes on to bake his ideas fully, it might be well to give him some advice and raise some objections. He himself mentions some possible objections, but I do not think that he treats them with the full consideration they deserve; there are others he does not mention.For lack of time, I shall not go into the first part of his paper, although I think that it contains a lot of highly unclear philosophical, or pseudo-philosophical assumptions. I shall rather spend my time in commenting on the example he works out in his paper at some length. Before I start, let me voice my protest against the general assumption of Dr. McCarthy -- slightly caricatured -- that a machine, if only its program is specified with a sufficient degree of carelessness, will be able to carry out satisfactory even rather difficult tasks."In Proceedings of the Symposium on the Mechanization of Thought Processes, National Physical Laboratory 1:77-84

'The modern science of switching theory began

TABLE OF CONTENTS FOR the two volumes of papers and discussions of papers from the 1959 conference held at the National Physical Laboratory, sometimes known as "The Teddington Conference". Officially these two volumes are the Proceedings of the Symposium on Mechanisation of Thought Processes. Many of the classics are downloadable separately, the remainder are available from the longer downloads of the complete volumes:Vol 1 = http://aitopics.org/sites/default/files/classic/TeddingtonConference/Mechanisation of Thought Processes Vol. 1.pdfVol. 2 = http://aitopics.org/sites/default/files/classic/TeddingtonConference/Mechanisation of Thought Processes Vol. 2.pdfTeddington Conference

Group Report 54-15, Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, Mass

"MANY EFFORTS have been made to discriminate,  categorize, and quantitate patterns, and  to reduce them into a usable machine language.  The results have ordinarily been methods or devices  with a high degree of specificity. For example, some  devices require a special type font; others can read  only one type font; still others require magnetic ink. We have an interest in decision-making circuits  with the following qualities: (1) measurable high reliability  in decision making, (2) either a high or a low  reliability input, and (3) possibly low reliability components.  The high specificity of the devices and  methods mentioned above was felt to be a drawback  for our purposes. All of these approaches prove upon inspection to center upon analysis of the specific  characteristics of patterns into parts, followed by a  synthesis of the whole from the parts. In these  studies, pattern recognition of the whole, that is, Gestalt recognition, was chosen as a more fruitful  avenue of approach and as a satisfactory problem for  the initial phases of the over-all study." Proceedings of the Eastern Joint Computer Conference, pp. 225-232, New York: Association for Computing Machinery

His present interests are: study of complex equilibria, especially in their topological aspects, as applied to the intelligent and adaptive aspects of the brain. He is now in the Department of Research of Barnwood House Hospital, Gloucester.

The studies reported here have been concerned with the programming of a digital computer to behave in a way which, if done by human beings oranimals, would be described as involving the process of learning. Whilethis is not the place to dwell on the importance of machine-learning procedures,or to discourse on the philosophical aspects,1 there is obviously avery large amount of work, now done by people, which is quite trivial inits demands on the intellect but does, nevertheless, involve some learning.Also in Computers and Thought. Feigenbaum, Edward A. and Julian Feldman (Editors) 1963.See also:IEEE XploreSome Studies in Machine Learning Using the Game of Checkers, II - Recent ProgressIBM Journal of Research and Development, 3:211-229

Frank Rosenblatt, born in New Rochelle, New York, U.S.A., July 11, 1928, graduated from Cornell University in 1950, and received a PhD degree in psychology, from the same university, in 1956. He was engaged in research on schizophrenia, as a Fellow of the U.S. Public Health Service, 1951-1953. He has made contributions to techniques of multivariate analysis, psychopathology, information processing and control systems, and physiological brain models. He is currently a Research Psychologist at the Cornell Aeronautical Laboratory, Inc., in Buffalo, New York, where he Is Project Engineer responsible for Project PARA (Perceiving and Recognizing Automaton). FRANK ROSENBLATT SUMMARY A THEORETICAL brain model, the perceptron, has been developed at the Cornell Aeronautical Laboratory, In Buffalo, New York. The perceptron is a probabilistic system, capable of learning to recognize and differentiate stimuli in its environment. Previous reports have covered the theory of a class of perceptrons ...