Large language models (LLMs) are all the rage, fueled by the release of OpenAI's ChatGPT in late 2022, initially powered by the LLM GPT-3. Aside from the news hype, what can LLMs actually, getting-down-to-brass-tacks, nitty-gritty do for your business? Here, we’ll look at three examples of problems they can solve. But first, a quick definition of LLMs.

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.

Generative AI (GenAI) has the potential to transform enterprise product operations, and as a Chief Product Officer (CPO), it’s essential to understand how to leverage generative AI to drive success within your product organization. This article serves as a comprehensive guide for how CPOs can use GenAI in product strategy, design, and innovation – generating new product ideas, creating unique designs, and exploring different variations and options.

If you're not thinking about integrating AI into your apps, you're missing out. In this tutorial, we will walk you through how to set up a React app that harnesses the vast knowledge of ChatGPT via the OpenAI API, allowing you to take your UI components to a whole new level.

Machine learning watching generative artificial intelligence (AI) take off feels a little bit like an American Girl doll envying the Barbie movie excitement from afar. What is she, chopped liver? But we can’t forget about machine learning, because it’s the giant that generative AI is standing on. How? Well, machine learning is how generative AI learns. Generative AI takes machine learning a step further by leveraging those learnings to produce something new.

Artificial intelligence (AI) has become a driving force in the digital transformation of businesses across various industries. As Chief Information Officers (CIOs) strive to stay ahead of the AI hype cycle in today’s competitive landscape, harnessing generative AI in particular can help them achieve their enterprise AI goals – by transforming processes, boosting productivity, and enhancing decision-making.

The conversation around generative AI naturally veers toward productivity, but people overlook this one, salient benefit: jumpstarting creativity. The best marketing combines data insights and creativity—and that’s where one of the many generative AI opportunities is for marketers.

Google the topic of artificial intelligence, and you’re likely to be taken down a deep, winding rabbit hole. If you venture only a little under the surface, you will encounter fantastical terms like perceptron, sigmoid neuron, and nonlinearly separable classifications. To save you from falling into that hole, this article will give a short, clear explanation of AI vs. generative AI.

Marketing and sales is undergoing a profound transformation as generative AI (gen AI) paves the way for advancements and innovation. With gen AI, businesses are rethinking their approaches to customer experience, productivity, revenue, and growth in both the B2B and the B2C domains.

AI has reshaped the landscape of almost every industry around the world, and the software testing industry is no exception. The emergence of AI-powered tools for test automation has changed the way QA teams approach their testing activities. This article walks you through the new ways that you can leverage AI testing tools and lists the top 7 best AI testing tools that can bring your testing game to the next level.

Artificial intelligence (AI) has revolutionized the way we do quality assurance (QA). The immense value that AI brings to optimizing testing processes and enhancing efficiency simply can’t be ignored. Organizations that know how to incorporate AI into their testing will gain a strong competitive edge against their competitors.

Today, APIs are based on modern communication patterns: REST, GraphQL, or gRPC. But two decades ago, the majority of Web Services were developed with SOAP/XML. In this blog, we’ll explain how Kong Konnect can manage SOAP/XML Web Services by creating custom plugins and by using ChatGPT. We’ll cover using ChatGPT to develop a Lua custom plugin and how to deploy and test a SOAP/XML custom plugin on Kong Konnect and Kong Enterprise.

How our analytics engineering team uses ChatGPT to write the most efficient dbt packages for your most common analytics use cases.

Organizations across all industries are racing to understand large language models (LLMs) and how to incorporate the generative artificial intelligence (AI) capabilities provided by LLMs into their business activities. Thanks to LLMs’ broad utility in classifying, editing, summarizing, answering questions, and drafting new content, among other tasks they are being embedded into existing processes and used to create new applications and services.