The Role of Artificial Intelligence in the SKA Era

Abstract

The Square Kilometre Array Observatory (SKAO) will usher in an era of unprecedented data complexity and scientific opportunity in radio astronomy, producing petabyte-scale datasets and terabit-per-second streams that challenge traditional analysis paradigms. Artificial Intelligence (AI) stands at the forefront of this transformation, offering scalable, adaptive solutions to the most pressing problems in radio astronomy and astrophysics. This chapter explores the pivotal role of AI in the SKA era, from real-time operations to scientific discovery. We examine how deep learning models enable automated source detection, radio-frequency interference mitigation, anomaly detection, and parameter inference, while generative approaches accelerate sky simulations, calibration, and imaging. Reinforcement learning promises dynamic scheduling and autonomous system control, and federated learning could address the distributed nature of SKA data. Beyond performance, we emphasize the necessity of explainability, uncertainty quantification, and physics-informed inductive biases to ensure scientific integrity. By mapping SKAO's core challenges - data volume, complexity, and interpretability - onto modern AI methodologies, we review how deep learning, self-supervised frameworks, and probabilistic models can unlock new frontiers in cosmology, galaxy evolution, and time-domain astrophysics. AI is not merely an automation tool for coping with scale. It is a catalyst for discovery, redefining how we observe, model, and understand the Universe.