Steven Pinker -- How the Mind Works - Hampshire High Italian ...

Thinking Machines 101It is utterly impractical, though, to represent propositions, or even theconcepts composing them, in logic gates, whether those logic gates aremade out of neurons or semiconductors. The problem is that every conceptand proposition has to be hard-wired in advance as a separate unit.Instead, both computers and brains represent concepts as patterns ofactivity over sets of units. A simple example is the lowly byte, which representsan alphanumeric character in your computer. The representationof the letter B is 01000010, where the digits (bits) correspond to tinypieces of silicon laid out in a row. The second and seventh pieces arecharged, corresponding to the ones, and the other pieces are uncharged,corresponding to the zeros. A byte can also be built out of toy neurons,and a circuit for recognizing the B pattern can be built as a simple neuralnetwork:You can imagine that this network is one of the parts making up ademon. If the bottom row of toy neurons is connected to short-termmemory, the top one detects whether short-term memory contains aninstance of the symbol B. And on page 102 is a network for a demon-partthat writes the symbol B into memory.We are on our way to building a conventional digital computer out oftoy neurons, but let's change direction a bit and make a more biomorphiccomputer. First, we can use the toy neurons to implement not classicallogic but fuzzy logic. In many domains people do not have all-or-noneconvictions about whether something is true. A thing can be a better or aworse example of a category rather than being either in or out. Take thecategory "vegetable." Most people agree that celery is a full-fledged