To fully appreciate the advantages of LAI in the area of communication, an encapsulated review of simple neural nets (the standard vehicle for non-logical information processing) is provided. After that review we offer a thought-experiment designed to sharpen our point concerning the communicative advantages of LAI over CAI.
Neural nets are composed of units or nodes, which are connected by links, each of which has a numeric weight. It is usually assumed that some of the units work in symbiosis with the external environment; these units form the sets of input and output units. Each unit has a current activation level, which is its output, and can compute, based on its inputs and weights on those inputs, its activation level at the next moment in time. This computation is entirely local: a unit takes account of but its neighbors in the net. This local computation is calculated in two stages. First, the input function, , gives the weighted sum of the unit's input values, that is, the sum of the input activations multiplied by their weights:
In the second stage, the activation function, g, takes the input from the first stage as argument and generates the output, or activation level, :
One common (and confessedly elementary) choice for the activation function (which ususally governs all units in a given net) is the step function, which usually has a threshold t that sees to it that a 1 is output when the input is greater than t, and that 0 is output otherwise.McCulloch and Pitts (1943) showed long ago that such a simple activation function allows for the representation of the basic Boolean functions of AND, OR and NOT. (This is supposed to look ``brain-like'' to some degree, given the metaphor that 1 represents the firing of a pulse from a neuron through an axon, and 0 represents no firing.)
As you may know, there are many different kinds of neural nets. The main distinction is between feed-forward and recurrent nets. In feed-forward nets, as their name suggests, links move information in one direction, and there are no cycles; recurrent nets allow for cycling back, and can become rather complicated. But no matter what neural net you care to talk about, it should be relatively easy to see that it is likely to be exceedingly difficult for such a net to communicate how, exactly, it has done something, if the ``something'' naturally calls for a symbolic explanation. In order to make the point vivid, begin by noting that neural nets can be viewed as a series of snapshots capturing the state of its nodes. For example, if we assume for simplicity that we have a 3-layer net (one input layer, one ``hidden'' layer, and one output layer) whose nodes, at any given time, or either ``on'' (filled circle) or ``off'' (blank circle), then here is such a snapshot:
As the units in this net compute and the net moves through time, snapshots will capture different patterns. But how could our observation of these non-symbolic patterns provide illuminating answers to questions calling for an account of deliberate ratiocination?