AI, Deep and Machine Learning Matter, But How Much For Typical Telecom Professional?

It is difficult to explain why deep learning, machine learning or artificial intelligence is relevant for nearly all who work in the telecom industry, beyond the ranks of data scientists working at or with chip, app and platform suppliers creating services and products used directly by network operators.

That is not terribly unusual. Machine learning already is an underpinning of most major consumer internet apps (content, social networking, search) as well as a growing range of business apps.

That is similar to the way consumers have learned to use cloud computing, without knowing how their apps are based on use of the cloud. But that “invisibility” also makes hard the challenge of figuring out whether “most in the industry” actually need to know very much about machine learning, deep learning or AI.

“I have very little to say that would be of interest to a telecom audience,” one data scientist working on machine learning and deep learning told me, for example. He probably is quite right.

People who work in outside plant can do their jobs quite well without understanding much of anything at all about marketing, billing systems, business strategy or industry trends.

Executives who work in the undersea networks segment of the business really do not need to know much at all about consumer demand for retail apps.

Most people who work for fixed network firms do not encounter any real need to understand mobility issues and vice versa.

That arguably is less true for C-level executives, though, who arguably might have to understand AI implications for their cost structures and capabilities.

At a practical level, a goodly number of people might already have job responsibilities where machine learning already is at work. That might already be operationally correct for people who work with software defined networks, network virtualization, customer service and analytics, for example.

That might be the “problem” with general purpose technologies: they are ubiquitous (like electricity, batteries, steel, airplanes, voice or other communications), and hence are invisible. People only need to know how to use product built on them. They rarely, if ever, need to know how they work.

For “C” title execs, AI might actually matter, in terms of understanding how AI might help create and change revenue models, operating costs, product value and “up the value chain” opportunities. That is what general purpose technologies generally enable: disruptions of whole economic systems. I,