At AT&T, AI and machine learning are woven into customer interactions, SDN network and next-gen technologies.
Our work in AI began in 1952 with Claude Shannon's invention of an electric mouse that learns how to move through a maze. Over the past 5 decades, we've built on his expertise, researching and developing numerous AI capabilities in areas of language translation, speech recognition, computer speech, image recognition, fleet management, sentiment analysis, etc.
Acumos is our open source platform for distributing and chaining different AI tools and microservices, giving developers and businesses access to sophisticated capabilities they wouldn’t otherwise have.
Machine Learning for 5G
To bring 5G to life, we need to deploy hundreds of thousands of cell sites. Current manual processes require in-person site visits, but we’re using machine learning to create a “virtual world” that describes its environment – poles, buildings, building materials, foliage – to help operators determine where cell sites can be placed without requiring a site visit. This technology also helps us identify faults in our towers.
Machine Learning for Security
The sophistication and volume of cyber activity has been increasing dramatically. This requires a change in traditional cyber analysis approaches that use static signatures and manual analysis. With machine learning, automated analysis is used to build patterns of normal and abnormal activity based on subtle characteristics that escape the human eye. The increasing wealth of anomalies can be analyzed using machine learning to proactively detect emerging trends before the network is compromised.
Predictive Customer Care
Machine Learning algorithms help us anticipate when customers are experiencing a service issue, and allow us to potentially address the problem before customers even reach out.
Content Personalization and Recommendation
We use advanced recommender system algorithms to provide our customers with the most personalized content and experience for our DirecTV and U-verse services.
Geotagging IP Packets for Location-Aware Software-Defined Networking in the Presence of Virtual Network Functions
Tamraparni Dasu, Yaron Kanza, Divesh Srivastava
ACM SIGSPATIAL, 2017
Autonomous Model Management via Reinforcement Learning: Extended Abstract
Elad Liebman, Eric Zavesky, Peter Stone
International Conference on Autonomous Agents and Multiagent Systems, 2017
AESOP: Automatic Policy Learning for Predicting and Mitigating Network Service Impairments
Supratim Deb, Zihui Ge, Sastry Isukapalli, Sarat Puthenpura, Shobha Venkataraman, He Yan, Jennifer Yates
ACM KDD, 2017
Bridging Heterogeneous Domains with Parallel Transport for Vision and Multimedia Applications
UAI'16 Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016
Cristian Borcea, Manoop Talasila, Reza Curtmola
Chapman and Hall/CRC, 2016