There's a lot of buzz about artificial intelligence being a game-changer for the telecom industry, but machine learning is paving the way in the short term.
BT's Neil McRae, chief architect, said there are elements of artificial intelligence in today's machine learning, and that machine learning could enable more, and deeper automation in networks.
“BT Labs have been doing some work with Cambridge University leveraging both machine learning and artificial intelligence to improve our ability to react to events on the network," McRae said. "The initial findings are very promising. There's absolutely a huge need for us to automate. First from an efficiency point of view, but also from a scale and complexity point of view. Networks are more complex, customers demand more and more from the network and I want to ensure that the network is the strongest part of our customers supply change. Today though, more often than not, humans are the reason for problems in the network. Using machine learning to let the network learn rather than scripting that automation will accelerate automation and I believe will bring benefits quicker.
"When you actually look at what artificial intelligence is and what you need to do to deploy and use it in a network function, that's not a trivial thing, but using approaches such as machine learning are going to be crucial to enable the end-to-end automation that we need and we need those ASAP."
As cloud, 5G and IoT began to converge, ML adds value to operating models by helping to create "smart" software networks. Pushed by IoT platforms, automation and cloud-based technologies, the global machine learning as a service (MLaaS) market is projected to grow from $ 679.32 million in 2016 to $7620.18 million by 2023 with a CAGR of 41.2%, according to Stratistics MRC.
McRae said that web-scale Internet companies, such as Google and Amazon Web Services, were among the current leaders for using machine learning (ML) and automation.
"They're still at the very start of this journey, but I believe in the future, for sure, those things will play a huge part in network operations and network optimization," McRae said.
As for current machine learning use cases, BT is trialing ML for deploying segment routing on its network.
"We're using machine learning and artificial intelligence as part of our path computation engine to show what's the best path and what has the least risks in that path," McRae said. "What is the most bandwidth on that path? And then build a set of segments through the network that gives the optimal performance.
"One area where telcos have been pretty terrible is in optimization of our assets. I think machine learning and AI will drive huge benefits in asset optimization.
But BT isn’t just thinking of its own networks.
“We are also trialing using ML with enterprise customers to analyze security threats and then automate the introduction of additional virtualized security applications on our customers' networks in our global services business," McRae explained. "Using AI to recognize adverse weather conditions may help us identify when we need to deploy more capacity in our customers virtual call centers. This is just the start of the trials, but results are very positive.
"But I think AI has the danger of being the latest buzzword across IT, infrastructure and technology. There are many applications and there's a lot of learning for us to still go through, but right now it feels very promising and we're very excited about it. A more model-based approach to network development makes the introduction of AI easier. These two technologies will be game changing for our industry."