John McCarthy, born at Boston, Mass. in 1927, received his B.S. degree in mathematics at the California Institute of Technology in 1948, and his Ph.D. also in mathematics at Princeton University in 1951. He is at present Assistant Professor of Communication Sciences at the Massachusetts Institute of Technology. His present interests are in the artificial intelligence problem, automatic programming and mathematical logic. He is co-editor with Dr. C. E. Shannon of "Automatic Studies". SUMMARY INTERESTING work is being done in programming computers to solve problems which require a high degree of intelligence in humans. However, certain elementary verbal reasoning processes so simple that they can be carried out by any non--feeble--minded human have yet to be simulated by machine programs. This paper will discuss programs to manipulate in a suitable formal language (most likely a part of the predicate calculus) common instrumental statements. The basic program will draw immediate conclusions from a list of premises. These conclusions will be either declarative or imperative sentences. When an imperative sentence is deduced the program takes a corresponding action. These actions may include printing sentences, moving sentences on lists, and reinitiating the basic deduction process on these lists. Facilities will be provided for communication with humans In the system via manual intervention and display devices connected to the computer.

If ability to perform complex calculations were a sufficient criterion, then even a conventional digital computor could lay claim to more intelligence than any of usand perhaps we had better let it make away with the word and be done with it.

Marvin Lee Minsky was born in New York on 9th August, 1927. He received his B.A from Harvard in 1950 and Ph.D in Mathematics from Princeton in 1954. For the next three years he was a member of the Harvard University Society of Fellows, and in 1957-58 was staff member of the M.I.T. Lincoln Laboratories. At present he is Assistant Professor of Mathematics at M.I.T. where he is giving a course in Automata and Artificial Intelligence and is also staff member of the Research Laboratory of Electronics. SUMMARY THIS paper is an attempt to discuss and partially organize a number of ideas concerning the design or programming of machines to work on problems for which the designer does not have, in advance, practical methods of solution. 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.

Biology seems to be a science in its own right, or set of sciences having common aims, and so it should have its own language and explanatory concepts; yet when any specifically biological concept is suggested and used as an explanatory concept it seems to be unsatisfactory and even mystical. There are many biological concepts of this kind: Purpose, Drive, elan vital, Entelechy, Gestalten.* Physicists and engineers seem, on the other hand, to have clearly defined concepts having great power within biology.

If ability to perform complex calculations were a sufficient criterion, then even a conventional digital computor could lay claim to more intelligence than any of usand perhaps we had better let it make away with the word and be done with it.

When it was suggested that I contribute a paper to this session, I had in mind that I would discuss and try and put into some kind of technological context the other papers of the session. Much of my own work of recent years has been in the field of learning machines, and artificial intelligence. There are some of us who are interested in seeing machines behave intelligently, and some of us who are only interested in having the machine simulate theories about how real brains work. I suppose that the former must predominate here, and I belong to that class myself. It is therefore a reasonable question to ask how we shall recognize intelligent behavior in a machine when we manage to find some. I'm not sure that I can answer that except by saying that I should try to use the same standards that I use in people; but I start out by being prejudiced that people, my friends at least, are intelligent and that machines are not, even the ones I'm friendly to. There are a very few computer programs that have behavior which, even if not bright, cannot be called stupid; the famous checkers program by Arthur Samuel of IBM is one.

We are here today,I take it, to appraise what has been done, and to discern the future, if we may. I notice that a man's worth these times is in the words he speaks and writes. The understanding that may lead to a publishable paper is much to be preferred to the understanding that leads to a useful machine. "But I say unto you, that every idle word that men shall speak, they shall give account thereof in the day of judgment. For by thy words thou shalt be justified, and by thy words thou shalt be condemned." Can we say anything true and useful in generalization about Pattern Recognition? Are there any broad statements that have any chance of being helpful to someone building a better machine?

S MAN RUSHES to build his replacements, he communication. In the meantime at least, Though such abstraction is difficult, we already have computers must be able to, but cannot, understand the given some of our machines limited ability to read printing writing and talking of men. We are protected from technological in certain type faces [1], [2]. But reading scratchpad unemployment so long as we are buffered by handwriting or transcribing conversational speech punched cards, magnetic tapes, and on-line or off-line by machine is far beyond our ken. Also, it seems clear printers. But the day will come!

Reprinted from Information Theory, Fourth London Symposium published by Butterworths, 88 Kingsway, London, W.C.2. MARVIN MINSKY and OLIVER G. SELFRIDGE Lincoln Laboratory*, Massachusetts Institute of Technology INTRODUCTION THE general nature of the problem is that an organism must learn to make the'right', or appropriate, response to its inputs. Typically, the inputs are large amounts of data, so that the machine must learn to recognize the similarities between different inputs which call for the same response, contrasted with the distinctions that call for different responses. The particular machines we are concerned with are random nets. A random net is a large set of similar and simply-acting elements whose attributes and interactive connections may be randomly established. The extent to which randomness is a part of setting up or maintaining a net varies in the literature, and more recent accounts tend to minimize the use of randomness. Some of the units are usually designated input, and some output units. The units themselves are termed neurons or cells. The underlying reason for the interest in random nets is the belief that if'right' responses are rewarded by some'reinforcement', perhaps of the contributing connections, and'wrong' ones discouraged, then the net as a whole will organize itself so as to tend to make only right responses, even when they are very complicated and abstruse.

Summary If we have a machine with 1000 knobs, how can we set them so as to minimize some output S of the machine, which represents, say, its error or departure from the behavior we want from it? We describe units, driven by S, which will each work a knob so that the whole system will tend toward an optimum. The units can be substantially identical, regardless of the actual structure of the machine. We exhibit three versions of these units, each with virtues and faults, and discuss -their behavior, with concrete and synthetic illustrative experiments. There are divers aspects of their joint behaviors shown, and some caveats about their use, especially in large numbers. We have not finished testing these units in large assemblies, but it is probably unimportant that the component parts of each unit work accurately or reliably.