Systems | Development | Analytics | API | Testing


Capturing the opportunity of AI while keeping an eye on sustainability with STaaS

The ever-growing tide of data, fueled by analytics and AI, places a significant strain on data center resources and increases energy consumption. With more stakeholder scrutiny and evolving regulations, such as the EU’s Corporate Sustainability Reporting Directive (CSRD), and U.S. regulations on the horizon, organizations are taking notice now more than ever before. As a result, organizations are prioritizing sustainability in their IT strategies.

A Look Back at the Gartner Data and Analytics Summit

Artificial intelligence (AI) is something that, by its very nature, can be surrounded by a sea of skepticism but also excitement and optimism when it comes to harnessing its power. With the arrival of the latest AI-powered technologies like large language models (LLMs) and generative AI (GenAI), there’s a vast amount of opportunities for innovation, growth, and improved business outcomes right around the corner. All of that technology, though, depends on data to be successful.

Introducing Qlik's AI Accelerator - Delivering Tangible Customer Outcomes in Generative AI Integration

At Qlik, we're witnessing a thrilling shift in the landscape of data analysis, customer engagement, and decision-making processes, all thanks to the advent of generative AI, especially Large Language Models (LLMs). The potential for transformation across all sectors is enormous, but the journey toward integration can be daunting for many businesses with many leaders wondering where to start in integrating the exciting capabilities of AI into their daily workflows.

Embedded analytics in the age of generative AI

Every company around the globe is trying to get in on the GenAI wave to simplify user experiences with natural language. And this is especially true in the realm of data and analytics. Imagine if you could enable all of your marketers to evaluate the performance of their campaigns with a simple question? Or, if you could provide all of your insurance risk managers with the ability to analyze the risk profile of their claims with the power of search and automated insights?

Are We in an AI Information Bubble?

Are we in an AI bubble? We can't stop talking about AI in tech. It's at every conference and in every startup pitch. But is the rest of the world as enamored as we are? In this conversation, we explore AI’s impact beyond the echo chamber of the tech industry. We look at attitudes toward AI in other spaces, from healthcare to finance, weighing the risks and benefits of its application. We also look to the future, questioning whether we’ve reached the limits of AI given compute power constraints.

How to Perform Database Analysis with AI

This blog explores how DreamFactory leverages its robust features to perform database analysis with AI, ensuring secure and efficient data operations. We will discuss the platform’s ability to generate dynamic APIs, provide real-time data insights, and maintain stringent security measures to protect data integrity.

Introducing Tricentis Copilot solutions

We are thrilled to announce Tricentis Copilot solutions, a collection of advanced generative AI capabilities available across our products that help customers boost their efficiency throughout the entire testing lifecycle. With Tricentis Copilot solutions, you can autogenerate manual tests from requirements, optimize your portfolio, autogenerate custom code, and get meaningful insights.

Turbocharging Your Business with (Gen)AI

If you were to stop someone walking down the street and ask them how long artificial intelligence, or AI, has been a hot topic, they might say it’s something that’s emerged mostly in recent years. But AI has been around for a long time, with the term first being coined as long ago as 1955. Generative AI however is a different beast, and one that's largely responsible for moving the topic of AI to the tip of everyone’s tongues – from consumers to enterprises alike.

Testing generative AI systems and red teaming: An introductory guide

The topic of testing AI and ensuring its responsibility, safety, and security has never been more urgent. Controversy and incidents of AI misuse have increased 26-fold since 2021, highlighting growing concerns. As users quickly find out, AI tools are not infallible; they can make mistakes, display overconfidence, and lack critical questioning. The reality of the market is that AI is prone to error. This is exactly why testing AI is crucial. But how do we test AI?