Ever since we have entered the third decade of the 21st century, artificial intelligence has proven to be the driving force behind innovation. However, the growing need for technology and constant development demands access to rapid testing and quality assurance. Besides, the software testing landscape is undergoing a revolutionary transformation, as we hurtle into a tech-driven era. It means AI-powered tools are being tested using the power of AI automation testing tools.

Generative artificial intelligence represents a category of AI that utilizes generative models to produce text, images, or other forms of media. These models grasp the intricacies and structure of their input training data, enabling them to generate new data with similar characteristics. In insurance, generative AI plays a pivotal role in expediting digitization processes.

Large Language Models (LLMs) have now evolved to include capabilities that simplify and/or augment a wide range of jobs. As enterprises consider wide-scale adoption of LLMs for use cases across their workforce or within applications, it’s important to note that while foundation models provide logic and the ability to understand commands, they lack the core knowledge of the business. That’s where fine-tuning becomes a critical step.

The telecom industry is undergoing a monumental transformation. The rise of new technologies such as 5G, cloud computing, and the Internet of Things (IoT) is putting pressure on telecom operators to find new ways to improve the performance of their networks, reduce costs and provide better customer service. Cost pressures especially are incentivizing telecoms to find new ways to implement automation and more efficient processes to help optimize operations and employee productivity.

If you’re building an application today, one of your top product priorities for 2024 is almost certainly adding an AI copilot to your application. AI copilots – AI assistants powered by large language models (LLMs) and deeply embedded into applications – offer one of the most compelling opportunities to reimagine applications since the dawn of the internet.

We are thrilled to announce the latest addition to N|Solid Pro - the N|Solid Copilot, a groundbreaking AI-powered assistant designed to revolutionize your Node.js development experience. This innovative tool is a leap forward in Node.js application observability and security, it’s like having a Node expert on-call. View of N|Solid Pro Console with the Copilot drawer open allowing a user to interact with the AI Assistant.

Lately, AI and Machine Learning have been the most significant breakthroughs in the software testing space, and they will remain a hot topic for the foreseeable future.

Of the many things one might take for granted, access to banking and financial services may not immediately come to mind. But as a thought experiment, imagine trying to buy a home or a car without the ability to take out a loan. Try depending on cash payments from your employer, or relying on alternative banking solutions like short-term payday loans, check-cashing services, and prepaid debit cards.

Artificial Intelligence (AI) is quickly becoming the go-to for businesses looking to up their game. Its advances have led to a suite of AI-powered tools that make running a company smoother—from automating redundant tasks to whipping up stunning visuals to redefining customer service. We’ll introduce you to the best AI tools for productivity and provide strategies to integrate AI into your daily operations.

Building a smart call center app based on generative AI is a promising solution for improving the customer experience and call center efficiency. But developing this app requires overcoming challenges like scalability, costs and audio quality. By building and orchestrating an ML pipeline with MLRun, which includes steps like transcription, masking PII and analysis, data science teams can use LLMs to analyze audio calls from their call centers. In this blog post, we explain how.