Machine-learning & operations for telcos; and a discount-code for AI World

A lot of discussion about deep/machine learning in the telecoms industry focuses on either customer management, or applications in various enterprise verticals. Typical use-cases are around assisted self-care for end-users (for example, diagnosing WiFi configuration problems), or spotting customer behaviours that suggest imminent churn (or fraud). 

Some telcos are looking at chatbots or voice assistants, for example for connected-home applications. Then there are offers around image recognition, perhaps for public-safety use, where telecoms operators have traditionally had a major role in many countries.

All this remains very important, but recently, I've been seeing a lot more "behind-the-scenes" use-cases being discussed, as both mobile and fixed operators look to improve their operational effectiveness and cost-base. Many of these are less-glamorous, and less-likely to highlight in non-telecoms articles, but they are nonetheless important. 

A few examples have been:

• BT has talked about using ML to improve efficiency of maintenance staff schedules, depending on particular tasks and base locations.
• Vodafone talked at the recent Huawei Mobile Broadband Forum about AI being used in radio networks for predictive load-balancing, predicting patterns of users/usage, and optimising configurations and parameters for better coverage and throughput. It also referenced using ML to help distinguish real from fake alarms
• KT in Korea, talking about collecting 50TB per day of operational data from its fibre network, and using it to optimise performance, improve security and predict faults. (It also realised that it accidentally created a huge realtime seismic detector, if earthquakes - or maybe North Korean nuclear detonations - flex the fibres)
• Telefonica working with Facebook's Telecom Infra Project initiative to map population density (from satellite images) to network usage data, to work out coverage gaps (see here)

As well as traditional telecom operators, new breeds of Internet-based communications providers are also looking at instrumenting their services to collect data, and optimise for multiple parameters. For example, Facebook (which is a "new telco") is improving its voice/video Messenger app, by collecting data from its 400 million users. This involves not just call "quality", but maps codec use to battery levels on mobile devices, and various other measureables. Potentially this allows a much broader type of optimisation than just network-centric, by considering the other trade-offs for users such as length of call vs. power consumption vs. video quality.
The key for all of this is collection of operational data in the first place, whether that is from network elements, end-user devices - or even external data sources like weather or traffic information.

I'll be digging into this in various future posts - but I'll also be speaking at various conferences and panel sessions about Telecoms & AI in coming months.

In particular, I'm on a Mobile & AI Panel at AI World in Boston, which runs from Dec 11-13. Details are at https://aiworld.com/ - and if you want to attend, I have a code for a $200 discount for 2 and 3-day VIP Conference Passes: AIW200DA

In January, I'll also be covering AI at the PTC'18 event in Honolulu from Jan 21-24 (link here).

And in April, I'll be at the AI Net event in Paris (link here) moderating a panel and also talking about AI in smartphones.

Overall - I'm expecting a huge #TelcoFuturism push around all aspects of AI in telecoms in 2018, but it's especially the operational and network-management functions that I think will make a big difference. It also coincides with the arrival of both 5G and large-scale NFV, and the intersection points will have a further multiplicative effect.