| Sumario: | In recent years we witnessed a growing interest towards leveraging Artificial Intelligence (AI) tools to innovate network operations at all layers, domains and planes. Yet, if, what and where we need to integrate intelligence in networks and how to (re)design networks for the native support of AI is still largely under debate. This is due to the multi-faceted nature of the challenges behind such integration: on the one hand, network architectures must be updated to accommodate AI models and their lifecycle by design (e.g., collecting and provisioning data in real-time, balancing centralized versus distributed computing approaches, empowering low latency requirements for fast closed-loop decision-making and network function automation); on the other hand, the design of AI models shall improve to better align with the myriad of requirements of production network systems (e.g., inference latency, computational complexity, trustworthiness of AI decisions); finally, operational procedures in research must be enhanced for verifiabilty, reproducibility and real-world deployment (e.g., establishing reference datasets or sharing trained models without sacrificing model explainability, robustness or safety). Pragmatic answers to all these points are paramount to enable a transition of the current large body of literature on AI for networking from academic exercises to solutions integrated in production systems. This workshop aims to bringing together researchers from academia and industry who are committed to making AI in networks a reality.
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