This is sometimes called the 100 step argument, for the following reason. A person can make many complex judgments within a second. But a neuron can only change states, say, 100-1000 times in a second. So, whatever allows us to make judgments, it must take only 100-1000 steps. Standard classical AI programs take many more steps.

There are two problems with this argument. First, the number of steps one takes depends on the complexity of the step. For example, when you have a document on your computer, you might move the mouse to press the "print" button at the top of the screen, thereby printing the document. Is that one step? Or is it many, for the computer must first print the first letter of your document, and then the second letter, and so on. The number of steps required depends on degree of complexity of the steps.

A second problem with this argument is that it seems to assume that all classical architectures must compute in a serial, or "one at a time" fashion. Connectionist networks, in contrast, engage in parallel computing, performing many operations at once. But one could build a symbolist architecture that computes in a parallel fashion as well. Thus, there¡¦s no reason to think that classical architectures are really so much slower than connectionist architectures.

From Ron Mallon's lecture notes