Why NLP Matters More than Ever

You have probably heard of the term “natural language processing.” Maybe you think of it as a buzzword - isn’t it just ChatGPT, or a subset of artificial intelligence? However, it is a lot bigger than that. NLP is not just a subset of AI; it is the layer that makes AI usable by humans. It is the reason you are able to interact with the AI models you love and use. At its core, NLP is about teaching computers to interpret, structure, and generate language in a way that captures meaning and intention. It is not about flashy demos or the “state-of-the-art.” Rather, it is about modelling and making sense of the understandability and interactability itself. So if NLP is bigger than chatbots, what does it actually do, and why does it matter for computer scientists today?

At the practical level, NLP enables computers to extract meaning from unstructured language. A good NLP model would have to effectively manage the following from users:

• The semantics of the sentence - the user intent and what the sentence actually means
• The syntax of the sentence - the grammatical structure of a sentence to understand how words relate to each other
• Be able to articulate and understand the output Some examples of effective NLP models would be speech-accessibility tools, document summarization, translation models, and more.

NLP models are also transforming many different industries, and are quickly becoming endemic to the workplace. It is used in the business industry, where it is being used to automatically process vast amounts of data, like customer review data, legal documents, and more. It is also often used in the healthcare industry - aiding in diagnostics, accelerating drug discovery by finding connections in literature, and streamlining administrative tasks like medical coding.

As these models are normalized and become endemic to the workplace, it has become more important to fundamentally understand how they work. Understanding the underlying mechanisms (e.g., transformer architectures) allows professionals to properly integrate, customize, and maintain these systems for specific company needs. Additionally, it is also crucial to realize the pitfalls as well. Models are only as good as their training data; if the data contains human biases (e.g., racial, gender), the model will perpetuate and even amplify them. This can lead to unfair or discriminatory outcomes in hiring, lending, or legal contexts. On top of that, it is not uncommon for models to sometimes lose context or “hallucinate” (confidently generating plausible-sounding but incorrect information).

Overall, NLP is a very important layer between the human and the AI models. It is the reason AI models are able to understand human input, and vice versa. However, when it comes to AI models, the future of work means not just that the use of NLP tools will explode, but there is also a responsibility to understand their inner workings and limits.