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AI-complete is, by analogy to NP-completeness in complexity theory, a term first coined by Fanya S. Montalvo to indicate that the difficulty of a computational problem is equivalent to solving the central Artificial Intelligence problem, in other words, making computers as intelligent as people. Note that unlike NP-completeness, this term is typically used informally.
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:
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.
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