In part one of this two part series, I reviewed the history of the chatbot, my 2003 patent, and the reasons why the conditions weren’t right for the type of chat experience we’re all now enjoying with ChatGPT. For part two, we get into what has changed and the different ways enterprises can drive modern chatbot experiences with ChatGPT.

The release of ChatGPT pushed the interest in and expectations of Large Language Model based use cases to record heights. Every company is looking to experiment, qualify and eventually release LLM based services to improve their internal operations and to level up their interactions with their users and customers. At Cloudera, we have been working with our customers to help them benefit from this new wave of innovation.

Today, organizations must do more with less. The pace of innovation has increased exponentially, yet resources remain the same (or are dwindling). Between talent shortages, long development cycles that rely on traditional programming languages, and technology teams that are already stretched perilously thin, many businesses have glaring operational problems they simply can’t solve with their current resources.

Ever since the release of ChatGPT, which showed the potential of generative artificial intelligence (AI), enterprises have raced to operationalize generative AI within their organizations. In fact, AI represents the primary challenge for nearly every organization today. You will either be good at AI or bad at business. Appian was quick on the AI draw.

Atlas AI‘s geospatial artificial intelligence platform that helps organizations anticipate changing societal conditions to help them make investment decisions.

AI is a top action item on the priority list of every leading organization. And as the AI frenzy unfolds, some of the promises about what AI could deliver have become less clear, complicated by matters of security, data, and ultimately, business value. Appian 23.3 builds on Appian’s existing AI capabilities and makes it easier than ever to operationalize AI within your enterprise.

There's no denying that OpenAI's remarkable artificial intelligence applications (ChatGPT and DALL-E) have captured the zeitgeist and hurled the topic of Generative AI into every company boardroom. Conversations range from apocalyptic hand-wringing to blissful ignorance. I wonder if ChatGPT will single-handedly save the chatbot industry, or is this just another tech fad that will quickly wither and die?

Machine Learning (ML) is at the heart of the boom in AI Applications, revolutionizing various domains. From powering intelligent Large Language Model (LLM) based chatbots like ChatGPT and Bard, to enabling text-to-AI image generators like Stable Diffusion, ML continues to drive innovation. Its transformative impact advances multiple fields from genetics to medicine to finance. Without exaggeration, ML has the potential to profoundly change lives, if it hasn’t already.