Neural architectures are appealing as mechanisms for implementing intelligence for a number of reasons. Traditional AI programs tend to be brittle and overly sensitive to noise rather than degrading gracefully such programs tend to either be right or fail completely. Neural architectures because they capture knowledge in a large number of fine grained units. Seem to have more potential for partially matching noisy and incomplete data. Neural architectures are also more robust because knowledge is distributed somewhat uniformly around the network.