Natural language encompasses the vast array of languages that have evolved over time for human interaction, in contrast to artificial languages like programming languages. In AI, processing and understanding natural language involves Natural Language Processing (NLP), which includes tasks such as speech recognition, sentiment analysis, machine translation, and conversational agents. The complexity of natural language arises from its ambiguity, contextuality, and the rich variety of syntactic, semantic, and pragmatic elements that it contains. NLP aims to enable machines to understand and generate human language, bridging the gap between human communication and computer systems.

The term "natural language" has been in use for centuries in linguistics but became prominent in computer science and AI in the mid-20th century, particularly as early NLP systems were developed. The 1950s and 1960s marked the beginning of computational efforts to process natural language, with the term gaining widespread attention as NLP became a critical area of AI research in the 1980s and 1990s.

Significant contributors to the development of natural language processing include Alan Turing, who proposed the Turing Test, highlighting the importance of language understanding in AI, and Noam Chomsky, whose theories on syntax influenced early computational linguistics. In the field of NLP, researchers such as John McCarthy and Geoffrey Hinton have also made substantial contributions, with McCarthy advancing AI concepts and Hinton pioneering deep learning techniques that have significantly improved NLP models.