And its evolution to generate language.
By Bruce Sharpe
Chief Product Officer
English is a terribly difficult language to master. Just ask anyone who has taken an English as a Second Language Course. But if we apply natural language processing, understanding language becomes a little bit simpler.
Students struggle to understand the grammatical nuances of verbs and nouns that are seemingly the same, such as ‘bank’ and ‘bank.’ Is this a financial institution or the edge of a river?
Now try teaching those nuances to a machine that could never appreciate the subtleties of our constantly evolving language that adds new words every year (hello ‘deepfake’) and seemingly retires words that have outlived their usefulness (goodbye ‘snollygoster’). But that is the challenge for our industry which is trying to build natural language processing (NLP) algorithms that identify the meaning of words and phrases in context.
For the uninitiated, NLP combines computer science, linguistics, and artificial intelligence (AI) to enable computers to understand, interpret, and generate human language. In simpler terms, it is a way for computers to read, analyze, and respond to human language. Essentially, NLP strives to understand how human language works.
Early NLP models used predefined rules to analyze language. These systems were limited in their ability to manage the complexity of natural language because of the difficulty of capturing nuances (i.e., bank vs. bank). Another approach used mathematical models to analyze language. These models chew through large amounts of data and can learn to identify patterns and relationships in language. This approach has been remarkably successful in tasks such as machine translation and sentiment analysis.
But a more recent development in NLP is deep learning, a subfield of machine learning that uses neural networks to model complex patterns in data. Deep learning has achieved state-of-the-art performance in many NLP tasks, such as language translation and text classification.
You’ve seen early NLP applications used in a variety of applications such as voice assistants, chatbots and digital agents in contact centers and have become an essential tool for businesses and organizations to extract insights from unstructured data. But the customer experience (CX) industry is hitting an inflection point with NLP. Next-generation language AI is poised to make the leap from academic research to widespread real-world adoption, generating billions of dollars of value and transforming entire industries.
Natural Language Processing: Generative Artificial Intelligence
CX is moving towards Generative AI (GenAI), a field of artificial intelligence and natural language processing that involves generating human-like language automatically. In simpler terms, GenAI involves using machines to produce text (and other media) that sounds like a human wrote it.
While NLP reads (or hears), GenAI writes (or speaks).
GenAI systems use algorithms and Large Language Models to generate natural language responses that are coherent, fluent, and relevant to the context. The data could be anything from structured data, such as financial reports or medical records, to unstructured data, such as social media posts or a customer inquiry. For example:
Caller: “What time does your bank close?”
Digital Agent: “We’re closing at 5 p.m. tonight but we’ll be open at 9 a.m. tomorrow morning if that works for you too.”
Notice, the digital agent just didn’t list the bank’s hours of operation. It answered with relevant information about the bank’s hours today and anticipated the caller might want to know when they could come in tomorrow if they couldn’t make it today.
GenAI technology is moving quickly and will transform how humans interact with machines, as well as how businesses communicate with their customers.