[MIT, Stanford: by analogy with NP-complete (see NP-)] Used to describe problems or subproblems in AI, to indicate that the solution presupposes a solution to the ‘strong AI problem’ (that is, the synthesis of a human-level intelligence). A problem that is AI-complete is, in other words, just too hard.

Examples of AI-complete problems are ‘The Vision Problem’ (building a system that can see as well as a human) and ‘The Natural Language Problem’ (building a system that can understand and speak a natural language as well as a human). These may appear to be modular, but all attempts so far (2003) to solve them have foundered on the amount of context information and ‘intelligence’ they seem to require. See also gedanken.