Five worthy reads is a regular column on five noteworthy items we’ve discovered while researching trending and timeless topics. This week, sticking with October’s cybersecurity awareness theme, we’ll take an in-depth look at both the good and the bad of artificial intelligence (AI) and machine learning (ML) in cybersecurity.

Despite a rocky year in the global economy, global venture deal volume grew by over 9.3 percent in the third quarter of 2019, up nearly 9.9 percent from Q3 2018, according to Crunchbase. The year 2018 was a banner year for dollar volume: startups raised $130.9 billion, which surpassed the epic year of 2000, according to Pitchbook.

By Des Nnochiri Robotic process automation, or RPA, promises to increase efficiency and improve work rates at reduced cost to the enterprise. In this article, we’ve assembled eight of the top RPA tools currently on the market. Of course, there are considerations to bear in mind before implementing this emerging technology.

Ready to unlock the power of machine learning to accelerate diagnostics and activate AI-powered root-cause analysis? Here's how to get started.

If you happen to be running multiple clusters, each with a large number of services, you’ll find that it’s rather impractical to use static alerts, such as “number of pods < X” or “ingress requests > Y”, or to simply measure the number of HTTP errors. Values fluctuate for every region, data center, cluster, etc. It’s difficult to manually adjust alerts and, when not done properly, you either get way too many false-positives or you could miss a key event.

There used to be a distinct, technical separation between terms such as AI and machine learning (ML) – but only while these technologies remained largely theoretical. As soon as they became practical in the real world, and then commodifiable into products, the marketers stepped in. Widespread overuse of the terms AI/ML in marketing have managed to thoroughly confuse the meanings of these words.

Software has eaten the world and every company today is a software company. This is because every company today is more and more serving its customers digitally. That service can be a spectrum, such as offering traditional physical products and services through digital channels on one end to offering entirely new digital products on the other end. Regardless of where on the spectrum a company is, it does not change the fact that its primary interface with its customers has become its software.

Many data science discussions focus on model development. But as any data scientist will tell you, this is only a small—and often relatively quick—part of the data science pipeline. An important, but often overlooked, component of model stewardship is monitoring models once they’ve been released to the wild. Here we’ll aim to convince any unbelievers that monitoring deployed models is as important as any other task in the data science workflow.