G. Selfridge was born in London 10 May PANDEMONIUM: A PARADIGM FOR LEARNING O. G. SELFRIDGE INTRODUCTION WE are proposing here a model of a process which we claim can adaptively improve itself to handle certain pattern recognition problems Which cannot be adequately specified in advance. Such problems are usual when trying' to build a machine to Imitate any one of a very large class of human data processing techniques. A speech typewriter is a good example of something that very many people have been trying unsuccessfully to build for some time. We do not suggest that we have proposed a model which can learn to typewrite from merely hearing speech. Pandemonium does not, however, seem on paper to have the same kinds of inherent restrictions or inflexibility that many previous proposals have had. The basic motif behind our model is the Inn of parallel processing. This is suggested on two grounds: first, it is often easier to handle data in a parallel manner, and, indeed, it is usually the more "natural" manner to handle it in; and, secondly, it is easier to modify an assembly of quasi We are not going to apologize for a frequent use of anthropomorphic or biamorphic terminology.

Particular attention is given to processes involving pattern recognition, learning, planning ahead, and the use of analogies or?models!. Also considered is the question of designing "administrative" procedures to manage the use of these other devices. The paper begins with a discussion of what is meant by "Intelligence" and concludes with a sec-- tion concerned with some techniques through which a machine might further improve itself by adding to Its collection of problem--solving methods. I. INTELLIGENCE I feel that it would not be useful to lay down any absolute defini-- tion of "intelligence" or of "intelligent behaviour". For our goals in trying to design "thinking machines" are constantly changing in relation to our ever--increasing resources in this area. Certainly there are many kinds of performances which if exhibited by a man we would all agree, today, require or manifest intelligence. But would we agree tomorrow?

The Dawn of Dynamic Programming Richard E. Bellman (1920–1984) is best known for the invention of dynamic programming in the 1950s. During his amazingly prolific career, based primarily at The University of Southern California, he published 39 books (several of which were reprinted by Dover, including Dynamic Programming, 42809-5, 2003) and 619 papers. Despite battling the crippling effects of a brain injury, he still published 100 papers during the last eleven years of his life. He was a frequent informal advisor to Dover during the 1960s and 1970s. Professor Bellman was awarded the IEEE Medal of Honor in 1979 "for contributions to decision processes and control system theory, particularly the creation and application of dynamic programming."

An excellent place to start. In this article, Turing not only proposes the Imitation Game in its original form, but addresses nine different arguments against AI, including Goedel's theorem and consciousness. Several recent arguments against AI are variations on the ones Turing enumerates. 'I propose to consider the question, "Can machines think?" This should begin with definitions of the meaning of the terms "machine" and "think." The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous....The new form of the problem can be described in terms of a game which we call the "imitation game."' I.—COMPUTING MACHINERY AND INTELLIGENCE. Mind 59, p. 433-460 (PDF from Oxford University Press).