## 5 Conclusions  
The ongoing deployments of 5G wireless networks mainly support eMBB services, but it is envisaged that 5G/6G networks would penetrate across various vertical industries and offer many more customised services and applications. On the one hand, verticals have already identified several profitable use cases that make use of the unprecedented high data rates low latencies and high number of connected devices offered by emerging networks. On the other hand, communication service providers have identified that AI/ML-based network orchestration is the only way to efficiently and dynamically support stringent and diversified technical requirements of various use cases over the same unified physical infrastructure. Note that deploying dedicated networks to each vertical is a wasteful usage of scarce and limited resources such as frequency spectrum and core network hardware components. Also, reactive hand-engineered policies and static thresholding rules for network slicing, resource allocation and RAN management are not tailored to heterogeneous networks and diversified use cases resulting in sub-optimal operation and inefficient use of the available resources. Our literature review reveals that network monitoring, extensive data collection, and AI/ML will become the main tools to optimise, manage and orchestrate current and future networks. Ongoing work in 5G standardisation includes the network data analytics function (NWDAF) and the management data analytics function (MDAF) developed by 3 GPP, and the RAN intelligent control (RIC) recommended by the O-RAN Alliance for coordinating the data collection, training, and inference of ML models at the core and the RAN, respectively. We have found in the existing literature several studies on real-time RIC and federated learning at the core network. These studies are important preliminary steps towards RAN control at longer time scales and distributed connected intelligence at the edge and the core network, which can enhance the performance in a multitude of use cases. Innovations in data science and communication technologies could evolve independently and be disentangled from one another, as suggested by ITU-T FG-ML5G, using digital twins and the concept of ML sandbox to compare ML models before deployment. This will ease the integration of newly developed AI/ML models into future networks. Finally, closed-loop controls, developed by the ETSI ZSM committee, will go beyond local network processes, e.g., congestion control at the transport layer, and we think they will become the prevailing solution for zero-touch network automation. In this survey, we have also reviewed the major and most impactful large-scale research projects towards intelligent communications & networking. Intelligent real-time RIC, e.g., intelligent channel coding, can bring significant benefits to network operators [136],and help address significant challenges such as urban V2X communication using software-controlled reflecting surfaces. Nevertheless, the focus of the telecommunication industry has been so far data to collect data at the RAN and the core network for intelligent control at timescales larger than 1000 ms. Apart from network management and orchestration, other representative examples at this timescale include AI/ML-based E2E network slicing, dynamic content selection for edge caching, and distributed learning such as forming federations between several NWDAF entities. To conclude, we believe that, ultimately, AI/ML would become the lifeblood of future networks and be vital in shaping the orchestration of available technologies, bandwidth, and computational resources to meet the customer intents, given energy and cost constraints. Supervision, M.H., J.E., M.A.I. and R.T.; Writing--original draft, K.K. ; Writing--review & editing, K.E.H. and M.D. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The authors declare no conflict of interest.