Some of the world's first practical expert systems were employed to improve operations and maintenance of telecommunication networks and services. Currently big data analysis and machine learning are widely used in telecommunication to predict customer behavior and trend analysis, to increase network productivity, in customer touch point management, or threat analytics.
Mobile operators are constantly under attacks, and the battle here is around time windows. A cyber gang can set up, go to work, and disappear in 24 hours or less – before an operator knows the attack is happening. Modern, sophisticated cyber attacks mutate, evolve, and arbitrage faster than an analyst can write rules to detect them. This type of mutating attack has a cloak of invisibility that is impervious to detection with traditional methods, but latest machine learning techniques allow to discover fraud threats and profit threats faster by detecting and visualizing sophisticated attacks – for both known and new, unknown threats.
With the growth in complexity of networks, there will be ample opportunity for the application of AI to this future infrastructure. Future telecommunication services are very complex, and if they are to be promulgated to a wide audience they will require a much easier user interface. One of the most required implementations of AI techniques is to overcome the difficulties that different languages present. The future promise of translation of language as part of a communication network has profound implications on the world.