An unsophisticated translation of such a sentence will therefore not be a good translation. Again, contrary to Mr. Richensi opinion, I believe that the problem involved is serious. There is no simple procedure to find out which, and in what way, the words of the English language are context-dependent. And I don't think that the issue can be belittled for tae reason that contextdependent words do not occur in scientific discussions and writings. They might not be too abundant in ordinary scientific papers on matters physical or chemical, but there would surely be plenty of them in discussions of matters linguistic, for instance. This might be one reason why so far hardly anybody has tried to machine translate papers in linguistics. As soon as this is attempted, the seriousness of the problem will become immediately evident.

Dr. Uttley took an Honours degree in Mathematics at King's College, London where he also took a degree in Psychology and did post-graduate research in Visual Perception. At the Royal Radar establishment he designed and built analogue and digital computers. For the last five years Dr. Uttley has been working on theories of computing in the nervous system. ABSTRACT IN two previous papers it has been suggested that two particular mathematical principles may underlie the organization of nervous systems; the first is that of classification (Uttley, 1954, ref.. 13) and the second is that of. The suggestion is based on the similarity of behaviour of these formal systems and or animals. The design of classification computers is discussed in the first paper; the design of conditional probability computers Is discussed in a third paper (Uttley, 1958, ref. 15); in both papers working models are described. FUrther reference to these papers will be by date only. It is the aim of the present paper to consider whether the two principles might operate in nervous systems. Mere are four requirements for the principle of classification to operate in an area of a nervous system. Firstly, In that area, signalling must be binary; this would be the case if, for example, the impulse frequency were at either a very low rate or at a maximal rate, or if signalling were In terms of standard volleys; in general, if the fibre activity were in one of only two states. The second requirement Is that the fibres which form the input to the area be connected to neurons In as many different ways as possible; there are many areas in which this condition is met. The third requirement Is that more than one synapse of a neuron must become active for it to fire; this appears to be met. The fourth requirement is that there shall be some way of delaying signals for periods of the order of seconds. A block of isolated cortex does remain active for such periods when stimulated briefly so in this way the requirement might be met. If these conditions are all met each neuron will indicate, by firing, the occurrerze of a particular spatio-temporal pattern of activity in the input to the system.

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.

Usually the discussion settles down to the would.)

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?