Natural Language Generations can be briefly described as the reverse of Natural Language Processing. It is the AI technology that makes it possible to translate text into naturally spoken language. In the curtain van example, it would have been the part of the bot that makes it possible to reply to the customers.
The range of perspectives with this technology is enormous, and many Use Case scenarios will emerge because what you have here is a technology that is capable of creating meaningful content.
Natural Language Generations enables you to create understandable content based on various sources of data. This makes the technology suitable as a core component in advanced digital agents. An example explains why.
Let’s say that a patient has been diagnosed with a specific disease. As part of the treatment process, different roles need different kind of patient information to perform their jobs.
So instead of giving all roles the same information, then you can use NLP to design the output that fits each individual need.
A specialist (doctor) will be able to get an answer that explains, at a high technical level, the content of the patient’s journal. A nurse will need a different type of information from the same journal to do their job. And the patient themselves may be able to be notified of the results in a way that is understandable (and ethically justifiable) to them.
In other words, Natural Language Generations will be able to create audience-specific content. The perspectives of Natural Language Generations are to go from mass communication (everyone gets the same information) to an ultra-personal message that adapts the form, structure, and message to the framework to which users at the individual level respond best.
This technology has the potential to change how you can run your business on many levels, and I will get back to how to utilize this in more detail in the book’s operations and strategy parts.
One use case for Natural Language Processing is the autogeneration of text. This article explains how AI is used to create books.
Natural Language Processing is also a topic that is only approached in the literature from a tech perspective. I find the best book to be “Handbook of Natural Language Processing” by Nitin Indurkhya.