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The Value of Symbolic AI in Practical Natural Language Use Cases

Release Date: Jun 24, 2021

Business executives have notoriously struggled to assess the business value of AI. They understand the potential value of it, but the general lack of institutional AI knowledge has made the evaluation process rather uncertain.

Language powers business. It fuels processes, shapes internal and external communications, and offers insight into the markets that surround us. We spend enormous amounts of time immersed in the language of our work, whether we’re processing and interpreting documents, searching for information or engaging with customers and each other.

AI-based natural language processing and understanding (NLP/NLU) technologies enable us to comprehend language and extract data from documents, manage interactions in natural language (e.g., chatbots) and process unstructured information at speed and scale. As the volume of language continues to grow exponentially, NLP/NLU technologies provide a key competitive advantage for enterprises in every industry.

AI-based NLU technologies leverage different techniques that can be divided into two segments: symbolic and machine learning/deep learning. Though symbolic is lesser known than machine learning as an AI technique, it has proven essential to successful natural language processing and natural language understanding solutions.

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