How companies and consumers benefit from AI-powered networks

As it has more than 12,500 patents, eight Nobel prizes, and a 140-year history of field-testing crazy ideas, it should be surprised that AT&T would be an important player in artificial intelligence.

“AT&T is a backbone of the internet,” explains Nadia Morris, head of Innovation at the AT&T Connected Health Foundry. The company manages wireless, landline, and even private secure networks to power connectivity for both individuals and corporations. All these networks generate incredible volumes of data that is ripe for machine analysis.

AT&T has built AI and machine learning systems for decades, using algorithms to automate operations such as common call center procedures and the analysis and correction of network outages. On the entertainment side, AT&T’s DirecTV division leverages users’ rating histories, viewing behaviors, and other factors to anticipate the next films they’ll watch.

Modern AI algorithms have enabled the telecom company to tackle even more complex tasks, such as optimizing the rollout of their 5G network. Traditional cell towers are usually suboptimally placed near urban centers and form an imperfect grid, leading to gaps in coverage. They’re also expensive to put up and maintain and incur challenges with real estate and property ownership.

Small cells are less expensive and more compact and can be installed on inner city buildings on a much finer grid. Their role is to repeat the signal from the main cell towers to bring it closer to end users. By crunching mobile subscriber data, well-calibrated AI can help create spatial models to hone in on ideal spots to build small cells and ensure maximum 5G signal strength for customers.

Designing the right 5G infrastructure is critical, especially given the rapid rise of video. “Video is more than half of our mobile traffic,” explains Chris Volinsky, who leads big data research at AT&T Labs. “Video traffic grew over 75 percent and smartphones drove almost 75 percent of our data traffic in 2016 alone. We expect video traffic growth to outpace overall data growth in 2020.”

Infrastructure is an enormous investment, even with small cells, so accurately modeling trends and usage growth is key to success. Demographic trends can cause previously underutilized areas to suddenly become hot traffic generators. While statistical models are useful for identifying trends in customer movement and throughout, AI and machine learning techniques create future projections from current data.

“We need to visualize billions of data points in a spatiotemporal fashion,” Volinsky elaborates. No tools existed previously to address AT&T’s unique data challenges, so the company built and open-sourced custom tools, such as Nanocubes, a data visualization tool that can map out millions of connections of individual mobile phones and connected devices to cell phone towers. The tool has been used outside the company to characterize sports fans in real time and to analyze crime rates and history.

Algorithms and tools are not the bottleneck in solving problems. Volinsky clarifies that “the challenge is in the data and the data pipeline.” Modern data-hungry AI approaches require a centralized data source, but gathering one across myriad networks with idiosyncratic standards is no trivial task. Each small cell collects cellular data differently. Some track 4G but not 3G. Some don’t get iPhone data. If variations are not taken into account, bias will appear in the data and the results.

“There is no world expert in data munging,” Volinsky bemoans. “To succeed, you have to figure out organizationally how to access data in different silos, technically how to integrate with it, and ensure the formats are in line.” Data scientists often discover that they can’t solve problems because the fundamentals of managing data is difficult and time-consuming. “This is not the stuff people learn in grad school,” Volinsky warns.