AI-complete

In the field of artificial intelligence, the most difficult problems are informally known as AI-complete or AI-hard, implying that the difficulty of these computational problems is equivalent to solving the central artificial intelligence problem, in other words, making computers as intelligent as people. The usage is analogous to the use of concepts such as NP-complete and NP-hard in complexity theory, which formally describes the most famous class of difficult problems. John Mallery said in 1988 that the term was coined by Fanya Montalvo. Early uses of the term are in Erik Mueller's 1987 Ph.D. dissertation and in Eric Raymond's 1991 jargon file.

To call a problem AI-complete reflects an attitude that it won't be solved by a simple algorithm, such as those used in ELIZA. Such problems are hypothesised to include:


 * Computer vision
 * Natural language understanding
 * Passing the Turing Test

These problems are easy for humans to do (in fact, some are described directly in terms of imitating humans), and all, at their core, are about representing complex relationships between a large number of human concepts. Some systems can solve very simple restricted versions of these problems, but none can solve them in their full generality.