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.

Usually the discussion settles down to the would.)

By this we mean the extraction of E CONSIDER the process we call Pattern clearly one of the most primitive sources of patterns.

Papers read at a Symposium on'Information Theory' held at the Royal Institution, London, September 12th to 16th 1955 Published by BUTTERWORTHS SCIENTIFIC PUBLICATIONS 88 KINGSWAY, LONDON, W.C.2 MANY psychologists studying learning have assumed that the subject--rat, dog, or graduate student--invariably knows what the stimulus is. They have not concerned themselves with how a dog knows that it is the bell ringing which is the stimulus to jump over a fence. A bell ringing never gives the same set of nervous impulses into the brain twice (of course the argument would still apply even if it did); why then should the dog classify all cases of bell ringing into one category--'stimulus'? There is then the further question of how this category is more or less quickly'associated' with a response: the point is that the stimulus is not a priori considered a significant entity by the subject. In designing programmes for computers to imitate conditioned reflexes, for instance, we have found that the real problem was to identify the stimulus.

At the time of the Dartmouth Well, when Digital built the PDP-1, you and we had studied philosophy. Not only conference, there were certain mathematical sat at the console and you wrote your program that, but we also knew McCulloch, who games called Post tag systems.

In either view, typical examples are closer where (only) positive examples ought to exceptional cases, case memory, analogy, to the prototype, and atypical examples and be.

"... software technology has promulgated a series of A vital problem is the measurement of overall performance, failed promises....