A problem is considered AI-complete or AI-hard if solving it is equivalent to creating AGI. For example, natural language processing (or machine translation) is often considered AI-complete because understanding arbitrary language constructs seems to require broad general knowledge. It was coined by the computer scientist Fanya Montalvo as an analogy with NP-complete, a class of problems in complexity theory. While formalizations have been attempted, the term is usually used to communicate the qualitatively difficulty of a problem.