AI-complete
From Wikipedia, the free encyclopedia
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:
- 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.
