## 1 Introduction
Such an approach pursues the dynamic control of the radio propagation itself by creating SRE [11]. The latter is not static, but it can be transformed into such ways to facilitate communication between the transmitter and the receiver. Furthermore, it can be programmed according to ever-changing needs [12]. The possibility of controlling the signal after it leaves the transmitter and before it reaches its destination has for years been the holy grail of wireless communication engineers. The main objective is to introduce wireless communications into the new era of Smart Wireless Communications [13], that of Wireless 2.0 [14]. The concept of SRE, enabled by RISs, represents a paradigm shift in wireless communications. Although there have been solutions in the past (e.g., multi-antenna relays and phased antenna arrays) in order to control the transmitted signal along its route to its destination, such approaches are associated with many drawbacks (significant increase in the initial implementation cost, energy and maintenance costs, etc.). Thus, it is expected that the novel paradigm of RISs [15] may provide a preferable and cost-effective approach for the control of the radio propagation environment in a smart way. Major benefits of RISs, as evidenced by the research results of the last few years, are related to cell coverage, communication data rates, physical security, etc., as well as to promising key use cases of RISs in various frequency bands. Thus, considering the 6G capabilities defined by the ITU-R [16], Table 1 depicts a selection of the foreseen 6G capabilities (compared to 5G) that will be positively influenced by the exploitation of RISs. It should be noted, however, that the (range of) values given for the capabilities in Table 1 are estimated targets for research and investigation of IMT-2030 (6G). RIS technology offers the ability to manipulate electromagnetic waves in real-time, effectively turning the wireless environment into a programmable space. This capability, when combined with AI, creates a symbiotic relationship where the physical layer of communication becomes adaptive and intelligent. AI algorithms can optimize the configuration of RIS elements to enhance signal quality, mitigate interference, and improve overall network performance. For instance, deep RL may be employed to dynamically adjust RIS configurations based on changing channel conditions and user requirements, leading to significant improvements in spectral efficiency and energy consumption [17]. In this context, the integration of AI in wireless communications, particularly in the realm of 6G, becomes crucial. AI techniques, ranging from supervised and unsupervised deep learning to reinforcement learning (RL) and FL, offer powerful tools to address the complex challenges posed by next-generation networks. Supervised deep learning, for instance, may be employed for precise channel estimation and beamforming in high-frequency bands, while unsupervised learning algorithms may identify patterns in network traffic and optimize resource allocation dynamically. RL holds promise for adaptive network management, allowing systems to learn optimal policies for routing, spectrum allocation, and power control in real-time. FL, a decentralized machine learning (ML) approach, emerges as a key technology for preserving user privacy while enabling collaborative learning across distributed devices, crucial for the IoE paradigm [12; 13; 14; 15; 17]. In essence, the realization of SRE in the Wireless 2.0 era will be enabled by the synergistic integration of AI within the 6G technology, in line with the \"Integrated AI and Communication\" usage scenario of the IMT Framework for 2030 (6G) [16]. In such a framework, the evolution of ML models in a RIS-aided 6G communication system along with their critical analysis concerning their strengths, limitations, and challenges presented in this paper, is of great interest for future research efforts.