 Eventually the earliest successful expert systems , such as <unk> , proved too expensive to maintain . They were difficult to update , they could not learn , they were " brittle " ( i.e. , they could make grotesque mistakes when given unusual inputs ) , and they fell prey to problems ( such as the qualification problem ) that had been identified years earlier . Expert systems proved useful , but only in a few special contexts . 
