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Recent advances in programmable metasurfaces, also dubbed as reconfigurableintelligent surfaces (RISs), are envisioned to offer a paradigm shift fromuncontrollable to fully tunable and customizable wireless propagationenvironments, enabling a plethora of new applications and technological trends.Therefore, in view of this cutting edge technological concept, we first reviewthe architecture and electromagnetic waves manipulation functionalities ofRISs. We then detail some of the recent advancements that have been madetowards realizing these programmable functionalities in wireless communicationapplications. Furthermore, we elaborate on how machine learning (ML) canaddress various constraints introduced by the real-time deployment of RISs,particularly in terms of latency, storage, energy efficiency, and computation.A review of the state-of-the-art research on the integration of ML with RISs ispresented, highlighting their potentials as well as challenges. Finally, thepaper concludes by offering a look ahead towards unexplored possibilities of MLmechanisms in the context of RISs.

An Outlook on the Interplay of Machine Learning and Reconfigurable Intelligent Surfaces: An Overview of Opportunities and Limitations