AI News, Telecom Machine Learning Applications – Comparing AT&T, Verizon, Comcast and More

Telecom Machine Learning Applications – Comparing AT&T, Verizon, Comcast and More

In the digital era, the telecom industry has shifted from basic phone and Internet service to a sector that is going high-tech and constantly evolving into a more mobile, wearable and automated environment.

The most popular AI applications in the six top telecom leaders appear to be: While the largest firms (AT&T and Verizon) seem to be farthest along in their predictive maintenance applications, chatbots and voice interfaces seem to be nearly ubiquitous as major initiatives across the firms analyzed.

The first generation encompasses “voice apps and speech recognition technology” while the second generation focuses on the company’s software-centric approach towards a “self-healing and self-learning” network fueled by artificial intelligence.

Art Pregler, Unmanned Aircraft Systems (UAS) Program Director, has stated that the company is exploring ways to incorporate AI and machine learning for the analysis of video data captured by drones for tech support and infrastructure maintenance of cell towers.

Dr. Gilbert explains that the signals and behavior of different “nodes” within AT&T’s network (say, a cell tower) can tip the company off to an impending problem, and they can send a human along to repair it in advance so that customers never notice an outage at all.

and balance their traffic volumes across the network- may benefit from listening to our interview with Jay Perret of Aria Networks.) In 2016, the company closed the year with reported consolidated earnings of $126 billion which reflects a decrease of approximately 2.4 percent in total revenue from the previous year.

In his  letter to stakeholders, CEO Lowell McAdam, references Verizon’s focus on increasing network usage and monetization through the development of “platforms, content and applications.”   While we weren’t able to find any resources about Verizon’s own predictive maintenance efforts, we can assume that they are in use today based on the fact that Verizon offers such services (which they call “condition-based maintenance”) to other carriers.

The company does a nice job of summing up the functions of predictive maintenance for heavy industry in the short video below: In February 2017, Verizon announced the launch of a new “business and technology venture” called Exponent, a set of B2B services that it is offering to other global carriers.

artificial intelligence platform, described as “ready out of the box” (which must be taken with a grain of salt), offers users the ability to take the data they currently collect and use it towards activities such as “personalized marketing campaigns, laser-targeted advertising, and deep customer engagement.” Comcast has recently introduced its first XI Talking Guide.

This sounds an awful lot like what Netflix does to optimize and recommend it’s content for each user (by looking at what the user clicks on, where they pause, what kinds of content they like to watch in sequence, what factors make them “binge”

(We’ve written a good deal about recommendation engines in our article on everyday uses of artificial intelligence.) Comcast Ventures is part of a consortium of investors that have reportedly invested $56 million in Interactions, a tech company that focuses on artificial intelligence ventures.

Not surprisingly, the technology has a long way to go before it becomes a viable option to fully handle customer questions (even the chatbot efforts of the “big 4” tech firms haven’t been able to make chat interfaces seamless and delightful yet).

The AI-driven assistant named Angie was designed by Conversica, a company touted as “the only provider of artificial intelligence-based lead engagement software for sales and marketing.” The Harvard Business Review reports that Angie averages an estimated 30,000 emails each month and analyzes the responses to identify “hot leads” (who we can assume are routed swiftly to salespeople without much further waiting).

Companies with huge B2C operations (millions or tens of millions of customers) are most suited to benefit from both text and voice applications, for a number of reasons: Customer service is a massive expense for any company with over a million customers, and chatbots hold a promise of significantly improving efficiencies.

Companies with massive volumes of incoming customer support requests have the most data available to train chatbot of speech recognition systems, allowing them to “drink in” the huge wave of support and use it as fuel for better interfaces with customers.

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