In modern software development, clear code documentation and commenting are not only just a part of good practices, but also they are essential for effective collaboration, maintainability, and scaling projects over time. However, they are often neglected due to time constraints or lack of clarity on how much detail is necessary. But, with the advent of artificial intelligence, developers now have tools to streamline this process, allowing them to focus on coding without compromising on clarity.

Today, cyber defenders face an unprecedented set of challenges as they work to secure and protect their organizations. In fact, according to the Identity Theft Resource Center (ITRC) Annual Data Breach Report, there were 2,365 cyber attacks in 2023 with more than 300 million victims, and a 72% increase in data breaches since 2021. The constant barrage of increasingly sophisticated cyberattacks has left many professionals feeling overwhelmed and burned out.

Enterprise organizations collect massive volumes of unstructured data, such as images, handwritten text, documents, and more. They also still capture much of this data through manual processes. The way to leverage this for business insight is to digitize that data. One of the biggest challenges with digitizing the output of these manual processes is transforming this unstructured data into something that can actually deliver actionable insights.

It’s November 2024 and I think it is safe to say, AI is officially everywhere. People are talking about it in cafés worldwide. So are your kids. Or your parents. Or grandparents. It may have been integral to your last car buying experience and has been used as a recommendation engine by your at-home/connected devices that hear you and want to improve your life by recommending products they think you need. It is pervasive and getting more so every day.

When was the last time you used a chat service and noticed messages arriving out of order? It’s likely that you can’t think of one. Getting messages to arrive in the right order is more or less job number one of a chat service. Sounds simple. But scale this to millions of users across a global network and it becomes a complex distributed computing problem. Even at smaller scales, message ordering isn’t guaranteed once you go beyond a simple, direct connection between two users.

Software applications often perform well during short-term testing but encounter issues during continuous use.

Today, cyber defenders face an unprecedented set of challenges as they work to secure and protect their organizations. In fact, according to the Identity Theft Resource Center (ITRC) Annual Data Breach Report, there were 2,365 cyber attacks in 2023 with more than 300 million victims, and a 72% increase in data breaches since 2021. The constant barrage of increasingly sophisticated cyberattacks has left many professionals feeling overwhelmed and burned out.

If there was a way to make your software testing faster, more accurate, and much less expensive, wouldn’t that be a game changer? That’s exactly what AI test automation is making possible. Traditional testing methods are slow, tedious, and expensive—especially as software gets more complex. Manual testing is time-consuming, brings errors throughout the process, and is a costly proposition. However, these problems can be overcome with AI-powered automation.

ClearML’s end-to-end AI Platform supports AI builders through every stage of the process, from data preparation and management to experimentation, deployment, and performance monitoring. At the heart of ClearML’s data management capabilities is its unique approach to visual data handling, known as Hyper-datasets. We’re sure you know all about the importance of data versioning, but here’s a quick reminder: effective data management is essential for.

Bank statements contain useful financial information that can be turned into important insights. With the era of manual bank statement extraction firmly in the past, intelligent document processing (IDP) and artificial intelligence (AI) offer a better way of processing bank statements and obtaining the valuable data they contain.