Finally, a third stream of research that has been a central element of AI since its founding
can be broadly characterized as a “learning” approach. Rather than being focused on symbolic
logic, or precise sense-and-react systems, the learning approach attempts to create reliable and
accurate methods for the prediction of particular events (either physical or logical) in the
presence of particular inputs. The concept of a neural network has been particularly important
in this area. A neural network is a program that uses a combination of weights and thresholds to
translate a set of inputs into a set of outputs, measures the “closeness” of these outputs to reality,
and then adjusts the weights it uses to narrow the distance between outputs and reality. In this
way, neural networks can learn as they are fed more inputs (Rosenblatt, 1958; 1963).
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