Chatbot evolution: how enterprises can use the latest language models like ChatGPT safely

In recent years, natural language processing (NLP) has seen a rapid advancement in capability thanks to the development of transformer architecture, also commonly known as large language models and the most recently hyped term: generative AI.

These models, such as GPT-3, have achieved remarkable results in various NLP tasks, such as language translation, question answering, and text summarisation.

Thanks to the release of ChatGPT — the chatbot space is one to watch over the next few years.

As technology advances, chatbots have become a vital tool for businesses of all sizes. They are cost-effective, available 24/7, and can handle various tasks.

But even with the invention of transformers, chatbots in the enterprise have still been primarily big logic trees and rudimentary in their ability — especially compared to academic performance.

So why have these models achieved superhuman performance in research and Natural Language Processing applications (NLP), but aren’t finding themselves in public-facing chatbots?

How do these super-chatbots work, and what will it take for enterprises to leverage this technology?

This blog series seeks to answer these questions as I take you on a journey to understand the recently hyped technology’s strengths and weaknesses and explain what large organisations need to make the most of it.

I’ve split this series into seven chapters.

1. What are Large Language Models and Transformers?
2. What Makes ChatGPT So Unique?
3. The Strengths and Weaknesses of Large Language Models
4. Using Emergence to Create the Perfect Chatbot
5. Achieving Chatbot Excellence: Lessons from the Top 5 Performers
6. Knowledge Graphs: the Yin to Transformers Yang
7. Real-time Inferencing — the opportunity for chatbots to provide a super-agent experience
8. Bringing this into production: ML Ops