In many ways, NLP and the rest of the AI stack are building a new world - a world where machines are trained to comprehend humans and respond appropriately.
 

Forward-looking organizations, from tech startups to established enterprises, are increasingly investing in NLP-powered apps and systems to streamline operations and drive productivity and business results.

In fact, Grand View Research states, “The global NLP market size was close to USD 27.73 billion in 2022 and is likely to grow at an impressive CAGR of 40.4% from 2023 to 2030.” The rapid growth in the NLP space is a testament to the fact that businesses across the globe are willing to invest in this technology.
 

The growth in NLP will also help push the existing boundaries of Artificial Intelligence and make AI a far more precious asset in the future. That’s what we’ll focus on in this blog. We’ll learn about the fundamentals of NLP. More importantly, we’ll look into ways this technology will help build a new era of AI.
 

Let’s start with what natural language processing (NLP) means.
 

Natural Language Processing (NLP) in AI: An overview
 

Natural language processing (NLP) in AI is a pivotal innovation in modern AI. The simplest way to understand NLP is to imagine a bridge connecting humans with machines at a far deeper level than ever before.
 

We use NLP in numerous real-life situations. So, when you interact with a chatbox installed on a website, with voice assistants such as Siri and Alexa, or with tools translating languages in real-time, you are using NLP-powered apps and systems.
 

NLP uses an ever-increasing number of techniques to understand, process, and generate human language. The most common natural language processing techniques are tokenization, stemming and lemmatization, and named entity recognition (NER).