An opposing school of thought, sometimes called "neural cy or "self-organizing systems,".

When making a decision a trust officer in a bank is confronted with a large assortment of information. In keeping with the postulates of this theory, the main postulates for the analysis of the investment decision process are that there exist: 1. A memory that contains lists of industries each of which has a list of companies associated with it. The memory also contains information associated with the general economy, industries, and individual companies. The set of rules constitutes the structure of the decision processes for an individual investor. It might be compared to the "rules of thumb" of the traditional "expert," but there is an important difference In common with other problem-solving programs, the processes are used iteratively and recursively. Lists of industries and companies are searched for particular attributes; sublists are created, searched and divided again. For example, to obtain a high growth portfolio, the list of companies stored in memory is searched to obtain securities with the desired pand) characteristics.

Participants in the search for intelligent machines frequently disagree on a basic question of strategy in their quest. On the one hand there are those who believe that the major obstacles can be overcome by reliance on the computer's infallible memory, electronic speed, and arithmetic capabilities uig This report takes the position that immediate, practical applica can derive from the former approach, but the major problems will be "\ To mention a single example, the implementation f information retrieval techniques on present-day computers would be a large step forward, even though the techniques thus far considered have largely been conceptually trivial. Luhn (1958) has u sed a straightforward statistical procedure to extract key sentences from scientific articles, thus yielding useful abstracts of a sort. For even an unintelligent human does more than count frequencies or search for key words. The human displays intelligent features which are generally summed up by saying that he ...

Baseball is a first step toward this goal.

The Imitation Game 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. If the meaning of the words "machine" and "think" are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to the question, "Can machines think?" is to be sought in a statistical survey such as a Gallup poll. Instead of attempting such a definition I shall replace the question by another, which is closely related to it and is expressed in relatively unambiguous words. The new form of the problem can be described in terms of a game which we call the "imitation game." It is played with three people, a man (A), a woman (B), and an interrogator (C) who may be of either sex. The interrogator stays in a room apart' from the other ...

How could one represent with a single prototype the class of figures which have an even number of disconnected parts?

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 ative wrong (although an attempt will be made to refute some of the neg arguments) but that they should be tolerant of research investigating these questions. Samuel Butler (1835-1902), in Erewhon and Erewhon Revisited (1933), concocted a civil war between the "machinists" and the "antimachinists." Butler stated "there is no security against the ultimate development of mechanical consciousness in the fact of machines possessing little consciousness now" and specylated that the time might come when "man shall become to the The topic came into prominence in the late 1940's when Babbage's dreams became a reality with the completion of the first large digital computers. When the popular press applied the term "giant brains" to these machines, computer builders and users, myself included, immediately arose to the defense of the human intellect. We hastened metic to proclaim that computers did not "think"; they only did arith A. M. Turing, who earlier had written one of the most important papers In it he circumvented the problem of properly defining the words "machine" and "thinking" and examined instead the question of a game This is now known throughout the computer field as "Turing's Test." Discussion of machine intelligence died down (but not out) in the early and mid-1950s but has come back in the last several years stronger than ever before. In fact, it has recently invaded the pages of Science (Mac-Gowan, 1960; Wiener, 1960; Taube, 1960; Samuel, 1960b). Like Turing, I avoid defining "to think." This notion is certainly not new, for it has existed since plicit man first compared his mental abilities with another man's, and it is im in all of the positive arguments on machine intelligence. Psychologists long ago developed "intelligence quotient" tinuum, as a yardstick in this con Existing commercial jet transports cannot transport people from one lake to another. But men cannot carry the load that a jeep can nor can men move with the speed of the jeep. Similarly, comparisons can be made between men and machines in the continuum of thinking. If there is objection to the use of the word "thinking," then "ability But it must be admitted that there exists some con of behavior in which men and machines coexist and in which they can be compared.

How could one represent with a single prototype the class of figures which have an even number of disconnected parts?

The purpose of this report is to describe in detail an information Processing model of elementary human symbolic learning processes. This model is realized by a computer program called the Elementary Perceiver and Memorizer (EPAM). The critical evaluation of EPAM must ultimately depend not upon the interest which it may have as a learning machine, but upon its ability to explain and Predict the phenomena of verbal learning. I should like to preface my discussion of the simulation of verbal learning with some brief remarks about the class of information processing models of which EPAM is a member. These are models of mental processes, not brain hardware. No physiological or neuro assumptions are made, nor is any attempt made to explain information processes in terms of more elementary neural processes. The central processing mechanism is assumed to be serial; i.e., capable of doing only one (or a very few) things at a time. These models use as a basic unit the information symbol; i.e., a pattern of bits which is assumed to be the brain's internal representation of environmental data.