Artificial Intelligence and Automation: Bringing Intelligent Assurance to Telecommunications Networks
Artificial intelligence is ubiquitous. From telephones to cars, vacuum cleaners to lighting systems – AI has so many and varied applications that we often don’t realize that it is there.
Machines operating on predetermined command algorithms have been around for more than half a century. What’s changed is the way that today’s devices learn; human input isn’t always required for AI to take in lessons and change the way it behaves.
Some of the biggest challenges for AI lie in their capabilities to learn through interaction. On the machine to machine level, it’s not such an issue. But factor in unpredictable human behavior, and it’s a different story altogether. Nevertheless, today’s AI is able to handle complex interaction with humans, as evidenced by virtual reality devices for gaming, military and aviation technology, and for training people how to act in difficult or dangerous situations.
In telecommunications, AI has proved essential.
AI and Automation for Intelligent Assurance in Telecommunications
- First is automated situation generation, with root-cause analysis and removal powered by machine learning. It is used in network and service surveillance/operations centers to automate the process of analyzing and removing causes of multiple events, including those reported directly by customers. Because a single root cause can lead to many alarms, this approach cuts time and work required to fix an issue, and keeps services available for customers.
- Next is automated problem generation, which involves deep ML-powered analysis of recurring network problems concerning alarm situations and the links between them. The result should be that embedded problems are rooted out, with the added benefit that the software carrying out analysis is not subject to human frailties such as tiredness, poor concentration or the tendency to take shortcuts, and does not disrupt services.
- The third area is automated baseline generation and anomaly detection. This requires information to be collected by human operators, and involves analysis of multiple sources and comparisons of one-off parameters that vary from the norm. This tool is primarily based on the designation of typical parameters established by human experts. In this case, automation will allow more flexible and dynamic variables to be brought into play, which can help identify symptoms of problems before they become issues for customers.
And that, really is the driver of AI and automation in digital telecommunications. Customers are demanding, and telcos strive to meet those demands, creating a new type of demand in itself – for innovative solutions that can take over arduous repetitive tasks and bring AI to every area of intelligent assurance and analytics.