ropose a solution. An expert system meant to include all knowledge possessed by an average adult was begun at Stanford University in the mid-1960s, but it is still in the primary development stage 35 years later. Due to their infallibility and expendability, expert systems often proved more effective and reliable than their human counterparts within their narrow areas of knowledge. This specialization occurred in other fields of artificial intelligence as well. Huge amounts of data from thousands of widely disparate sources were collected and stored each day, and some way of picking out meaningful patterns from among the trillions of bits of information was needed. Data mining programs were the answer to this dilemma, but here too it became evident that a general purpose pattern recognition system such as the human eye would be impractical, and individual solutions were developed for applications such as financial analysis and cataloguing of astronomical objects. This trend has continued throughout the 1990s, and nearly every computer program on the market has some amount of what would 50 years ago have been termed artificial intelligence. Many of these have become quite competent within their specific ranges of knowledge, but the drop-off of ability once they reach the edges of this knowledge is complete and immediate. The result has been that a rising level of intelligence and seeming awareness by computer programs has become expected and is taken for granted. Several practitioners of artificial intelligence have speculated that full general intelligence will never be realized on a computer only because once a computer becomes good at something it is no longer regarded by the public as an activity that requires intelligence. At the beginning of the new millennium, the world champion chess player is a computer; robotics and artificial vision have advanced to the point that in a few years the world ping-pong champion will, in al...
