How Knowledge Graphs Make Generative AI Consumable in Enterprise Environments
Knowledge Graphs Making Generative AI Enterprise-Ready
Ever since OpenAI introduced ChatGPT in late 2022, knowledge managers—and a lot of other people as well—have been inundated, every day, with information about new developments in AI, new AI-centered companies, new uses for generative AI (GenAI), and new technological discoveries based on AI. It’s mind boggling how fast things change. We’re caught up in the whirl of AI changes, going faster than a roller coaster, leaving us overwhelmed and confused. Given the speed at which GenAI is evolving, making sense of it and determining how we can adapt it to the enterprise in a realistic fashion is getting more difficult all the time...
How Knowledge Graphs Make Generative AI Consumable in Enterprise Environments
Few technologies have captivated the collective imagination of the general public as swiftly as Generative AI has. In less than a year, it transitioned from something only data scientists were familiar with to one of the most accomplished and sought-after set of capabilities throughout the data sphere.
Generative AI’s most pervasive application, ChatGPT, first made international headlines in November 2022. In the midst of the ensuing media frenzy, organizations across verticals were galvanized to employ Large Language Models (LLMs) for numerous practical use cases like question answering, content generation for marketing and sales, and customer support interfaces via conversational digital agents. Generative AI’s foundational models also have a remarkable faculty for generating database queries from natural language, data models for business domains, BI reports and visualizations, and substantial amounts of knowledge graphs...