Globally, industries are scrambling to turn big data into actionable insight that humans can easily interpret. By far the most promising answer to this problem is the use of machine learning, or artificial intelligence (AI). Analysts are expecting the uses of AI in medical diagnosis, finance, telecommunications and many other contexts to soar. A recent article in puts estimates of the economic value of AI applications in the billion-dollar range, while IBM’s chief executive Ginni Rometty points to a US$2-trillion opportunity in AI systems over the coming decade.

Of course there are caveats about AI. Warnings on the dangers of allowing machines do all of our thinking have come from many voices, including Sir Stephen Hawkins, and in Cambridge, England, Hawkins attended the launch of the Centre for the Future of Intelligence, which is designed specifically to do some of that thinking about the implications of AI.

Nevertheless, AI will make a profound difference to the way we live. Substantial progress has been made in machine learning and other AI techniques to perform a range of complex tasks that have a direct impact on everyday life. Innovators like Facebook, Google, and LinkedIn have pioneered big data and machine learning approaches to analyzing and acting on big data and data lakes. Other applications can range from analyzing skin changes indicative of early-stage cancer to the reduction of energy costs for data centers.

Argyle Data has pioneered new machine learning approaches, focusing on mobile fraud analysis and prevention as the initial applications since they deliver the most immediate results and ROI. Given the extraordinary volumes of data that make up modern mobile communication, the only way to detect anomalies in real time is to apply machine learning at massive scale. The combination of supervised and unsupervised machine learning makes it possible to analyze unthinkable amounts of data and alert fraud analysts in seconds.

Argyle Data recently published a preview of a joint research paper with Carnegie Mellon University Silicon Valley (CMU), “Real-time Anomaly Detection in Streaming Cellular Network Data”, outlining our unique approach to machine learning. You can download the executive preview at

The paper is important because it describes substantial advances in the methods and capabilities of machine learning in a business context. It will be submitted for presentation at academic conferences during the first half of 2017. Meanwhile on Tuesday, October 25, at 12 p.m. CST one of the paper’s authors, Dr. Ole Mengshoel of CMU, and Argyle Data’s Padraig Stapleton will be guests on a radio show focused on emerging tech matters: The Voice of IoT,, wsRadio.xom.

Peggy discusses everything from the latest news on connected devices to the latest applications of IoT/M2M technology. Next week, Ole and Padraig will delve into the world of machine learning and where advances in the technology are taking us. Listen in at either live or to the podcast.