for the Forbes Technology Council, I argued that the rise of large language models and deep learning would be a renaissance for natural language processing , and I highlighted the business reasons why NLP will benefit from the expansion of deep learning and what we now know as “AI.”
Many of the features of an NLP engine, such as named entities, sentence grammar and speaker intention, are all provided by machine learning models, which are foundational to what we call “AI” today. In reality, NLP and AI are not two different technologies; NLP is actually a platform to deploy a series of AI capabilities.
Examples of these problems include intentions and effort . Many of the traditional features of an NLP engine are better suited to simpler techniques that are faster, cheaper and map easily to business needs. They, too, are adding to NLP/NLU systems with features like automated content summaries that help answer business questions like, “Why did the sentiment around my new product dip in Q2?” LLMs like ChatGPT or local LLMs like Llama are ideal for digging into a collection of documents and answering the “why” and “what to do about it” questions.