The Road to Human-Level Natural Language Processing

Language is a hallmark of human intelligence, and Natural Language Processing (NLP) has long been a goal of Artificial Intelligence. The ability of early computers to process rules and look up definitions made machine translation seem right around the corner. However language proved to be more complicated than rules and definitions.

The observation that humans use practical knowledge of the world to interpret language set off a quest to create vast databases of human knowledge to apply to NLP. But it wasn’t until deep learning became available that human-level NLP was achieved, using an approach quite unlike human language understanding.

In my latest iMerit blog I trace the path that led to modern NLP systems, which leave meaning to humans and let machines do what they are good at – finding patterns in data.

Author: Tom Robertson

Tom Robertson, Ph.D., is an organizational and engineering consultant specializing in harmonizing human and artificial intelligence. He has been an AI researcher, an aerospace executive, and a consultant in Organizational Development. An international speaker and teacher, he has presented in a dozen countries and has served as visiting faculty at Écoles des Mines d’Ales in France and Portland State University.

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