Natural Language Processing — Connecting The World through Language With AI

As young children, before we can even walk or talk, we’re listening. We’re hearing the sounds and vocalizations made by other people. We’re attaching those combinations of sounds to meanings, such as ‘mother’ and ‘door’, and learning to read the facial expressions of those around us to deepen our understanding of blocks of words. Then we go to school, start interacting with other types of language representation — like cartoons, TV, tablets and mobile phones, as well as books, where we refine our understanding of language.

What is a natural process for the majority of people, is incredibly difficult for computers. Languages are complex data types, with flexible formal rules and lots of exceptions, and they are exceptionally hard to understand when they lack context and intent. Imagine a child comes into your room and says “Door!”. Without context (why are they saying this? Is the door open?) and intent (do they want me to close it?), it’s almost impossible to know how to respond in an appropriate way.

It’s no wonder that it’s taken decades of slow, tedious work to train AI to “understand” language. As machine learning capabilities grow, so does our ability to improve Natural Language Processing or NLP.

As AI and NLP technology gets changes and grows, it’s being applied in different ways to make the world a better place.

What is NLP and LLM?

NLP or Natural Language Processing is a subset of AI that focuses on how computers understand and interpret human language. It involves breaking down language into its component parts, such as words and sentences, and analyzing them to extract meaning. NLP can be done with speech or text, depending on the software. The first iterations of NLP began more than 50 years ago and evolved out of the field of linguistics. Today, the most common example of NLP technology is right in your purse or pocket. Smart assistants in your home or on your smartphone use NLP and AI to provide a voice-driven interface for intelligent search.

On the other hand, LLM or Large Language Models are advanced AI models that have been trained on massive amounts of data to generate human-like language. These models can be used to perform a variety of tasks, including language translation, content creation, and text summarization. When combined with NLP techniques, LLMs can enable more advanced language processing capabilities, such as sentiment analysis and named entity recognition.

The next time you call out to Alexa, Siri, Google, Bixby, or any other virtual assistant — remember, you’re using a technology that was decades in the making and it wouldn’t be possible without advanced AI.

In the beginning, NLP, like linguistics, was a way of developing a deeper understanding of language. As the field grows and AI technology improves, NLP can be scaled for use by a number of different industries while making the world a better and more efficient place.

As AI data-handling improves and access to huge amounts of computational power becomes commonplace, NLP and AI applications will continue to expand in scope. And, when done well with a partner that understands data storage, transformation, and labeling, the technology can benefit so many people.

When it comes to large language models or LLMs, they are are a significant development in the field of NLP, allowing for more accurate and complex language processing than ever before. The application of LLMs in various industries has the potential to make the world a better and more efficient place. For example, in healthcare, LLMs can assist in analyzing large volumes of patient data, enabling doctors to make faster, more accurate diagnoses and develop more effective treatments. In the finance industry, LLMs can analyze financial data and market trends, leading to better investment decisions and ultimately a more stable economy. Additionally, LLMs can be used to improve communication with non-native speakers and those with disabilities, creating a more inclusive society. As NLP and LLM technology continues to evolve, we can expect to see even more exciting and impactful applications in the future.