Artificial Intelligence (AI) is transforming industries across the globe, driving innovation, improving efficiencies, and unlocking new possibilities. From autonomous vehicles to personalized healthcare, AI’s capabilities are vast. However, as AI systems become more integrated into our daily lives, the question of trust becomes critical. Can we trust AI to make decisions that align with our values? Can we be sure that AI systems are secure from manipulation or bias?

The Snowflake AI Data Cloud has democratized data for thousands of customers, removing data silos and powering data sharing and collaboration use cases. Many customers have been able to unlock enormous value from their data with Snowflake, including safely collaborating on sensitive data using Snowflake Data Clean Rooms and Data Governance features. However, some highly sensitive data has remained off-limits due to regulatory requirements and privacy concerns — until now.

Healthcare data is set to soar, with projections showing that it will grow from 2,300 exabytes in 2020 to an impressive 10,800 exabytes by 2025. To put that in perspective, that’s like having enough data to fill over 2.5 billion DVDs! What’s more is that a large portion of this data is unstructured—scanned documents, handwritten notes, and PDFs that don’t easily integrate into traditional systems. This is where Intelligent Document Processing (IDP) comes in.

In August, we wrote about how in a future where distributed data architectures are inevitable, unifying and managing operational and business metadata is critical to successfully maximizing the value of data, analytics, and AI. One of the most important innovations in data management is open table formats, specifically Apache Iceberg, which fundamentally transforms the way data teams manage operational metadata in the data lake.

Test automation is awesome. No doubt about that. However, sometimes test scripts just…fail, and it’s quite frustrating. What makes test automation fail in the first place? Knowing these root causes, we can easily develop best practices to make them run smoother and faster.

As highlighted in the Quantum-Safeness of WSO2 Products blog, there is an urgent need for alternative asymmetric encryption methods. Post-quantum algorithms (Q) are gradually gaining prominence but require time to establish trust within the industry. Therefore, combining classical (C) and post-quantum (Q) Key Encapsulation Mechanisms (KEMs) ensures resilience against classical and quantum attacks. The Ballerina Swan Lake Update 9 release adds support for post-quantum secure end-to-end encryption.

Here’s an easy-to-understand and straight-to-the-point guide to scroll down in Selenium. Let’s try this test case: Scroll to a section on a Wikipedia page and screenshot. We have the following test steps: You can customize the element according to your needs.

As businesses increasingly adopt cloud technologies to drive innovation, Cloud Infrastructure Management Services (CIMS) have become a cornerstone of successful digital transformation initiatives. With rapid advancements in generative AI, IoT, and connected devices, enterprises want to modernize their infrastructure, enhance integration, and automate processes to remain competitive.

Slowly but surely, HTTP/2 has become the preferred protocol for transferring data files between clients and servers. In contrast, HTTP/1, which once stood as the sole method for loading web applications, is now falling short, particularly in terms of user experience. Since 2015, HTTP/2 APIs have taken the lead with lightning-fast server responses.