One machine can do the work of fifty ordinary men.
No machine can do the work of one extraordinary man.
~Elbert Hubbard, The Roycroft Dictionary and Book of Epigrams, 1923
The term artificial intelligence conjures up visions of machine with near-human intelligence. John McCarthy coined the term in 1955, and the field was founded on the claim that a central property of human beings, intelligence—the sapience of Homo sapiens—can be so precisely described that it can be simulated by a machine . However attempting to model the human brain is fraught with difficulties. Modeling parts of the human thought process, be it the brain, or the vision system, is often assumed to be a very clean, structured process. It has been postured that if the brain turns out to be designed as a patchwork, hodgepodge of many ad hoc kludges (inelegant rules), then there can be no cognitive science . The human brain is a complex structure. It was once said that “even if we had a diagram that included every one of the billions of neurons and billions of interconnections in the human brain, it would stare at us as mutely as the grains of sand in a desert” . That the brain is 70% water does complicate matters somewhat.
Computers now have access to immense storage repositories, networks of CPUs with which to process, CPUs with colossal processing speed, and sensors which are capable of producing gigapixel images. In many ways we have outstripped the physical limitations of the human body, but we have only begun to understand the complexities involved in the process of thinking.
Today, more than half a century after the first musings of AI, many “intelligent” systems have the ability to learn from their surroundings in order to make a decision, yet there is no true artificial intelligence. These systems gorge themselves of enormous amounts of data, searching for statistical trends in the data. This is not really dissimilar to how humans learn. Children learn to associate the word “cat” with a physical entity, then later learn to spell the word cat. A similar association works in any other language. With this process children build up their vocabulary, and improve their ability to read. Yet whilst we can show a computer a picture of a cat and associate the word cat with it, we also would have to associate the word cat with pictures of many different species of cats (and find a way of allowing the computer to “characterize” what it sees in the picture). The mind discerns enough information from the features of a cat after one or two viewings to identify a cat regardless of its species. The computer cannot. Much of this lies with how the human mind processes data from our varied senses and stores it. And whilst computers are fast in their ability to process data, they lack the intuition of the human mind.
 McCarthy, John; Minsky, Marvin; Rochester, Nathan; Shannon, Claude (1955), A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence.
 Cherniak, C., “Undebuggability and cognitive science”, Communications of the ACM, 1988, Vol.31, Np.4., pp.402-412.
 Bernstein, J., “A.I.”, New Yorker, Dec.14, 1981, pp.121