BELL INC (CANADA)
“ Some of the ML use cases are not going to move mountains, but they are going to support much better – and faster – insights” GEORGE ISKENDERIAN
DIRECTOR, BIG DATA & AI BELL INC (CANADA)
And then, of course, you can enhance your algorithms. The paradigm is shifting from detecting issues to looking at the bigger picture and understanding why anomalies are happening.” Exterminating robocalls Another application for machine learning in telecoms is identifying robocalling and spam calls. “We’ve deployed an algorithm that detects the likelihood of a call being spam or robocalling. We did a trial and it was very successful. Now the trial is being expanded so that phone calls that go through the Bell network will be exposed to a machine learning algorithm that determines if a call is spam. And there’s lots of business logic to make sure there aren’t false positives. Especially in business critical applications, you can’t have false positives, so your threshold is really high.
“There are use cases that could not be done without ML, and there are lots of those, but there are also lots of use cases where machine learning is just going to be a tool set. They are not going to be making headlines. They are not going to move mountains, but they are going to support much better – and faster – insights. It’s not that sexy, and nobody’s going to make too much of a fuss about it, but the data guys who are creating these solutions will use AI/ ML as a toolset in their arsenal.” “AI is not the goal. It is the means to help us reduce operational costs achieve better customer experience and develop new solutions and services. The goal is always addressing a specific business problem, and the means could be machine learning.”
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