26 LEARNING IN RANDOM NETS

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.