Gold's Language Identification in the Limit

Gold's Language Identification in the Limit is a theoretical concept in computer science and linguistics that explores how a learner can identify a language from a set of strings over time. It proposes that if a learner receives an infinite number of examples from a specific language, they will eventually converge on a correct description of that language. The key idea is that the learner must only make finitely many mistakes, ultimately reaching a consistent understanding after enough exposure, reflecting a process where knowledge improves progressively with experience.