Machine Learning (ML) and Artificial Intelligence techniques promise new design methodologies and tools for architecting communication networks and automating network operation.  At the same time, the increasingly powerful and ubiquitous compute-communication capabilities of emerging networks, are driving AI/ML computations closer to the edge; a shift that promises to democratize AI services and enable a wide variety of new AI usages. 

AI/ML technology continues to evolve at a rapid pace, moving from a paradigm of supervised learning towards distributed self-learning, cognitive reasoning and general intelligence.  Although AI models are becoming increasingly powerful, several challenges in wide-scale deployment of AI/ML solution have been identified. They include growing computational complexity and power consumption of AI/ML models, access to sufficient real-world data to drive AI/ML research, robustness of learning solutions in practical deployments, susceptibility to adversarial attacks, and lack of explainability of AI models.

Exploiting the power of AI/ML solutions to address the growing complexity of communication networks, while addressing the aforementioned challenges in the context of resource constrained, dynamic and mission critical environment of modern networks is an important topic of current research. This panel will highlight the emerging trends in AI/ML technology and discuss their implications for design, deployment and operation of next generation communication networks.