  Recent advances in programmable metasurfaces, also dubbed as reconfigurable
intelligent surfaces (RISs), are envisioned to offer a paradigm shift from
uncontrollable to fully tunable and customizable wireless propagation
environments, enabling a plethora of new applications and technological trends.
Therefore, in view of this cutting edge technological concept, we first review
the architecture and electromagnetic waves manipulation functionalities of
RISs. We then detail some of the recent advancements that have been made
towards realizing these programmable functionalities in wireless communication
applications. Furthermore, we elaborate on how machine learning (ML) can
address 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 is
presented, highlighting their potentials as well as challenges. Finally, the
paper concludes by offering a look ahead towards unexplored possibilities of ML
mechanisms in the context of RISs.
