Five worthy reads is a regular column on five noteworthy items we’ve discovered while researching trending and timeless topics. This week, we explore the relationship between AI and data privacy. From smart devices and voice assistants to mediating traffic and enhancing personalized shopping experiences, artificial intelligence (AI) has found widespread application in many aspects of life.

We recently launched Elastic Security, combining the threat hunting and analytics tools from Elastic SIEM with the prevention and response features of Elastic Endpoint Security. This combined solution focuses on detecting and flexibly responding to security threats, with machine learning providing core capabilities for real-time protections, detections, and interactive hunting. But why are machine learning tools so important in information security? How is machine learning being applied?

Augmented analytics is trending. You’ve read about it, you’ve heard about it, you may even be in the process of acquiring systems running it. But what exactly is it, and how can you recognize it? As the guys building augmented analytics, we’re here to dispel some of the hype. On the highest level, augmented analytics is the machine learning processes geared at making data more accessible and actionable for both data scientists and business users.

The digital era has brought vast cultural transformations – the sharing economy, microtransactions, lightning-fast communication and much more. Much of this has also resulted in considerable innovation in revenue-related areas. Companies from various industries today manage a large number of revenue streams from different revenue models.

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.