 Finding the right set of weights to accomplish a given task is the central goal in connectionist research. Luckily, learning algorithms have been devised that can calculate the right weights for carrying out many tasks (see Hinton 1992 for an accessible review). These fall into two broad categories: supervised and unsupervised learning. Hebbian learning is the best known unsupervised form. As each input is presented to the net, weights between nodes that are active together are increased, while those weights connecting nodes that are not active together are decreased. This form of training is especially useful for building nets that can classify the input into useful categories. The most widely used supervised algorithm is called backpropagation. To use this method, one needs a training set consisting of many examples of inputs and their desired outputs for a given task. This external set of examples “supervises” the training process. If, for example, the task is to distinguish male from female faces, the training set might contain pictures of faces together with an indication of the sex of the person depicted in each one. A net that can learn this task might have two output units (indicating the categories male and female) and many input units, one devoted to the brightness of each pixel (tiny area) in the picture. The weights of the net to be trained are initially set to random values, and then members of the training set are repeatedly exposed to the net. The values for the input of a member are placed on the input units and the output of the net is compared with the desired output for this member. Then all the weights in the net are adjusted slightly in the direction that would bring the net’s output values closer to the values for the desired output. For example, when male’s face is presented to the input units the weights are adjusted so that the value of the male output unit is increased and the value of the female output unit is decreased. After many repetitions of this process the net may learn to produce the desired output for each input in the training set. If the training goes well, the net may also have learned to generalize to the desired behavior for inputs and outputs that were not in the training set. For example, it may do a good job of distinguishing males from females in pictures that were never presented to it before.