The field of AI is directed at the fundamental problem of how the mind works; its approach, among other things, is to try to simulate its working -- in bits and pieces. History shows us that mankind has been trying to do this for certainly hundreds of years, but the blooming of current computer technology has sparked an explosion in the research we can now do. The center of AI is the wonderful capacity we call learning, which the field is paying increasing attention to. Learning is difficult and easy, complicated and simple, and most research doesn't look at many aspects of its complexity. However, we in the AI field are starting. Let us now celebrate the efforts of our forebears and rejoice in our own efforts, so that our successors can thrive in their research. This article is the substance, edited and adapted, of the keynote address given at the 1992 annual meeting of the Association for the Advancement of Artificial Intelligence on 14 July in San Jose, California. AI Magazine 14(2): 36-48.

A 25-year-old book about science has some explaining to do. Machines Who Think was conceived as a history of artificial intelligence, beginning with the first dreams of the classical Greek poets (and the nightmares of the Hebrew prophets), up through its realization as twentieth-century science. The interviews with AI's pioneer scientists took place when the field was young and generally unknown. They were nearly all in robust middle age, with a few decades of fertile research behind them, and luckily, more to come. Thus their explanations of what they thought they were doing were spontaneous, provisional, and often full of glorious fun.

Selected quotes:"The Science Research Council has been receiving an increasing number of applications for research support in the rather broad field with mathematical engineering and biological aspects which often goes under the general description Articial Intelligence (Al). The research support applied for is sufficient in volume, and in variety of discipline involved, to demand that a general view of the field be taken by the Council itself.""To supplement the important mass of specialist and detailed information available to the Science Research Council its Chairman decided to commission an independent report by someone outside the Al field but with substantial general experience of research work in multidisciplinary fields including fields with mathematical, engineering and biological aspects."-----"Most workers in Al research and in related elds confess to a pro nounced feeling of disappointment in what has been achieved in the past twenty-five years. Workers entered the feld around 1950, and even around 1960, with high hopes that are very far from having been realised in 1972. In no part of the field have the discoveries made so far produced the major impact that was then promised.""In the meantime, claims and predictions regarding the potential results of Al research had been publicised which went even farther than the expectations of the majority of workers in the field whose embarrassments have been added to by the lamentable failure of such inflated predictions.""These general statements are expanded in a little more detail in the rest of section 3, which has been influenced by the views of large numbers of people listed in section 1 but which like the whole of this report represents in the last analysis only the personal view of the author. Before going into such detail he is inclined, as a mathematician, to single out one rather general cause for the disappointments that have been experienced: failure to recognise the implications of the 'combinatorial explosion'."See also: BBC TV - June 1973 - Lighthill Controversy Debate at the Royal Institution with Professor Sir James Lighthill, Professor Donald Michie, Professor Richard Gregory and Professor John McCarthy.Also in Lighthill, J., Sutherland, N. S., Needham, R. M., Longuet-Higgins, H. C., and Michie, D. (Eds.), Artificial Intelligence: A Paper Symposium. Science Research Council of Great Britain.

E.A. Feigenbaum and J. Feldman (Eds.). Computers and Thought. McGraw-Hill, 1963. This collection includes twenty classic papers by such pioneers as A. M. Turing and Marvin Minsky who were behind the pivotal advances in artificially simulating human thought processes with computers. All Parts are available as downloadable pdf files; most individual chapters are also available separately. COMPUTING MACHINERY AND INTELLIGENCE. A. M. Turing. CHESS-PLAYING PROGRAMS AND THE PROBLEM OF COMPLEXITY. Allen Newell, J.C. Shaw and H.A. Simon. SOME STUDIES IN MACHINE LEARNING USING THE GAME OF CHECKERS. A. L. Samuel. EMPIRICAL EXPLORATIONS WITH THE LOGIC THEORY MACHINE: A CASE STUDY IN HEURISTICS. Allen Newell J.C. Shaw and H.A. Simon. REALIZATION OF A GEOMETRY-THEOREM PROVING MACHINE. H. Gelernter. EMPIRICAL EXPLORATIONS OF THE GEOMETRY-THEOREM PROVING MACHINE. H. Gelernter, J.R. Hansen, and D. W. Loveland. SUMMARY OF A HEURISTIC LINE BALANCING PROCEDURE. Fred M. Tonge. A HEURISTIC PROGRAM THAT SOLVES SYMBOLIC INTEGRATION PROBLEMS IN FRESHMAN CALCULUS. James R. Slagle. BASEBALL: AN AUTOMATIC QUESTION ANSWERER. Green, Bert F. Jr., Alice K. Wolf, Carol Chomsky, and Kenneth Laughery. INFERENTIAL MEMORY AS THE BASIS OF MACHINES WHICH UNDERSTAND NATURAL LANGUAGE. Robert K. Lindsay. PATTERN RECOGNITION BY MACHINE. Oliver G. Selfridge and Ulric Neisser. A PATTERN-RECOGNITION PROGRAM THAT GENERATES, EVALUATES, AND ADJUSTS ITS OWN OPERATORS. Leonard Uhr and Charles Vossler. GPS, A PROGRAM THAT SIMULATES HUMAN THOUGHT. Allen Newell and H.A. Simon. THE SIMULATION OF VERBAL LEARNING BEHAVIOR. Edward A. Feigenbaum. PROGRAMMING A MODEL OF HUMAN CONCEPT FORMULATION. Earl B. Hunt and Carl I. Hovland. SIMULATION OF BEHAVIOR IN THE BINARY CHOICE EXPERIMENT Julian Feldman. A MODEL OF THE TRUST INVESTMENT PROCESS. Geoffrey P. E. Clarkson. A COMPUTER MODEL OF ELEMENTARY SOCIAL BEHAVIOR. John T. Gullahorn and Jeanne E. Gullahorn. TOWARD INTELLIGENT MACHINES. Paul Armer. STEPS TOWARD ARTIFICIAL INTELLIGENCE. Marvin Minsky. A SELECTED DESCRIPTOR-INDEXED BIBLIOGRAPHY TO THE LITERATURE ON ARTIFICIAL INTELLIGENCE. Marvin Minsky.

This listing is intended as an introduction to the literature on Artificial Intelligence, €”i.e., to the literature dealing with the problem of making machines behave intelligently. We have divided this area into categories and cross-indexed the references accordingly. Large bibliographies without some classification facility are next to useless. This particular field is still young, but there are already many instances in which workers have wasted much time in rediscovering (for better or for worse) schemes already reported. In the last year or two this problem has become worse, and in such a situation just about any information is better than none. This bibliography is intended to serve just that purpose-to present some information about this literature. The selection was confined mainly to publications directly concerned with construction of artificial problem-solving systems. Many peripheral areas are omitted completely or represented only by a few citations.IRE Trans. on Human Factors in Electronics, HFE-2, pages 39-55

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

"The 1956 Dartmouth summer research project on artificial intelligence was initiated by this August 31, 1955 proposal, authored by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon. The original typescript consisted of 17 pages plus a title page. Copies of the typescript are housed in the archives at Dartmouth College and Stanford University. The first 5 papers state the proposal, and the remaining pages give qualifications and interests of the four who proposed the study. In the interest of brevity, this article reproduces only the proposal itself, along with the short autobiographical statements of the proposers."Tech. rep., Dartmouth College. Reprinted in AI Magazine, Vol 27, No. 4, p. 12, Winter 2006.

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).

See also:Full Text (scanned)Google BooksTech. rep., National Physical Laboratory. (Reprinted in Meltzer, Bernard and Donald Michie (Eds.), Machine Intelligence 5. Edinburgh University Press. (Also in Ince, 1992)

Oliver Selfridge in The Gardens of Learning wrote: "I have watched AI since its beginnings... In 1943, I was an undergraduate at the Massachusetts Institute of Technology (MIT) and met a man whom I was soon to be a roommate with. He was but three years older than I, and he was writing what I deem to be the first directed and solid piece of work in AI (McCulloch and Pitts 1943) His name was Walter Pitts, and he had teamed up with a neurophysiologist named Warren McCulloch, who was busy finding out how neurons worked (McCulloch and Pitts 1943).... Figure 1 shows a couple of examples of neural nets from this paper---the first AI paper ever." From the introduction to the Warren S. McCulloch Papers, American Philosophical Society.http://www.amphilsoc.org/mole/view?docId=ead/Mss.B.M139-ead.xml;query=;brand=defaultAlthough an important figure in the early development of computing, McCulloch's goal in research was as much to lay bare the foundations for how we think as it was to develop practical applications - or in other words, to develop an "experimental epistemology" with which to relate mind and brain. Perhaps the most significant work to emerge from this period of McCulloch's career was his landmark paper with Walter Pitts, "A Logical Calculus Immanent in Nervous Activity" ( Bulletin of Mathematical Biophysics 5 (1943): 115-133). The "Logical calculus" was an attempt to develop just that: a rigorous description of neural activity independent of resort to theories of a soul or mind. Together with McCulloch and Pitts' follow-up work, "How we know universals: The perception of auditory and visual forms" ( Bulletin of Mathematical Biophysics 9 (1947) 127-147), the "Logical calculus" provided a compact mathematical model for understanding neural relationships laying the groundwork for neural network theory and automata theory, and forming the ur-foundation of modern computation (through John Von Neumannn) and cybernetics. (See Marvin Minsky, Computation: Finite and Infinite Machines, Englewood Cliffs, NJ: Prentice-Hall, 1967, for a very readable treatment of the computational aspects of McCulloch/Pitts neurons.")Bulletin of Mathematical Biophysics, 5, 115–137