Systems | Development | Analytics | API | Testing

What is AI NLQ? Understanding AI-Powered Natural Language Query

The rise of natural language query (NLQ) technology in modern business intelligence (BI) and analytics platforms is empowering many companies to streamline data exploration and analysis, and democratize access to insights for more people - not just data experts. But like any technology, the ongoing challenge is to help stakeholders and customers to see the value in using it.

Yellowfin 9.15 Release Highlights: AI-Powered NLQ, Usability Enhancements & More

Yellowfin 9.15 is a significant version release that introduces a major update to our Yellowfin Guided NLQ feature in the form of AI-enabled Natural Language Query (AI NLQ), as well as a host of general product enhancements, fixes and security updates. In this blog, we will cover what AI NLQ brings to your embedded analytics deployment, as well some of the other highlights arriving in the Yellowfin 9.15 version release. For the full technical list of updates, please visit our release notes page.

10 Agentic AI Examples (Use Cases) for Enterprises & How To Build Them

AI is no longer just a tool. It is now handling complex tasks with minimal human intervention and oversight. This transformative shift has given rise to agentic AI, where AI-powered systems make decisions, adapt to new information, and automate workflows across departments. From answering customer inquiries to managing financial data, these AI-driven agents are reshaping how businesses operate.

EP 20 | The Path to Safe AI - Education with Peter Norvig

In this episode of The AI Forecast, host Paul Muller speaks with Peter Norvig, an education fellow at Stanford and the co-author of Artificial Intelligence: A Modern Approach, the leading textbook for AI education. Peter explores the critical role of accessible, up-to-date AI education in building skilled practitioners, guiding policy, and fostering public understanding. Listen in as they explore concepts like "AI literacy" and unpack why continuous learning is essential to keep pace with technological change—and how it can help us build a more informed, ethical, and responsible AI future.

What is a Multi Agent System? Types, Application and Benefits

AI has evolved from simple rule-based systems to models capable of understanding language, generating images, and even assisting in complex decision-making. Yet, most AI systems still operate as a single, standalone entity. But what if AI could work like a team, where each agent brings its own strengths to the table? Multi-agent systems (MAS) make this possible by enabling real-time interaction and coordination among intelligent agents.

Agentic AI vs Generative AI: Understanding the Key Differences

You’ve probably interacted with AI more times than you can count—whether it’s getting a movie recommendation, using an AI-powered chatbot, or watching AI-generated content. But have you ever stopped to think about how these AI systems actually work? Not all AI is built the same way, and two key paradigms are emerging as game-changers: Agentic AI and Generative AI.