So that’s an important part of the mission, and we’re thinking about network architecture and design. It really doesn’t even in the next three years. We are thinking about the next 20 and 50 years. Network investments take a long time and we want to make those investments economically, but also very assured of providing the most reliable network.
Laurel: You mentioned artificial intelligence and machine learning in a previous answer. What are some ways that AT&T is using AI and ML or is thinking about implementing artificial intelligence?
Raj: Good question and also a very timely question. As a company, we have had researchers working on AI for many years. With the advent of more computing power and more fine-grained data, the opportunity has really opened up with the last chance, I would say 5 years. It plays a very important role at AT&T. Again, we’ve approached AI in an evolutionary way in terms of how we use it.
First, we think of AI as the engine and fuel as the data. It starts with how we want to collect data and learn from it. That’s where a lot of machine learning possibilities come in. We’ve invested in a lot of big data management capabilities over the past few years, making sure those capabilities are well exposed to our AI engine. In particular, our chief data officer worked very hard to establish a democratizing ecosystem for both data and AI capabilities. Here comes a complex function as the amount of data increases especially with 5G and we get kind of better particle visibility and we have more smart controls to apply the decisions. So we’re taking those steps in that evolutionary way.
Internally, we have a variety of use cases, including how we can use AI for planning, functionality, AI for design decisions, and in real time to help our customers. we, like the network, in different scenarios to deliver better performance, better customer experience, detection of security threats, threat analysis, and how to use rings feedback to continuously optimize the network. So a lot of lifecycle use cases.
Laurel: I’m talking about a focus on security, which is of primary concern to most executives today. But not only security, AI and automation also play a really important role for 5G functionality. What other ways are currently working with 5G capabilities?
Raj: Again, this is very timely and a very dynamic area of work. Let me give you some context on how we are structured. When we think about 5G, we think of it as day zero, day one, day two. Day zero is for planning and forecasting activities. I can see some natural ways AI and machine learning can help you through your forecasting. Have your day, which is actually building and designing your network. You want to work at maximum efficiency. Again, feedback loops and reinforcement learning will help you do that, as well as using deep learning to analyze maps and geospatial data, to determine where you want to bury your fibers. optical and where you want to place a small cell relative to the macro cell. So there are a lot of construction techniques that we mainly rely on AI, deep learning and neural networks.
Then there is a life cycle, which we call day two. In that, there are opportunities, things like energy saving, where we are trying to optimize the energy footprint of our devices. Again, both a corporate priority but also a societal priority on carbon emissions. We see great opportunities for the economy but also for the planet.
