# 6G to Take the Digital Divide by Storm: Key Technologies and Trends to Bridge the Gap
## 5 Artificial Intelligence: The Ultimate Breakthrough? AI plays a role of paramount importance in the evolution process that wireless networks are experiencing. Its use can enhance the performance of many applications by providing a wide range of beneficial properties. In particular, ML is a branch of AI that is considered a top solution in many tricky 6G applications [32]. The ML technology allows us to train systems that, by the processing of collected data, can learn patterns by experience and, consequently, improve the performance and quality of the offered services. In particular, following the recent progress in deep learning as well as the advent of smart devices that are capable of processing ML algorithms on the edge, the wireless community has gained a renewed and huge interest in such technologies, which can now be leveraged in use cases that were unable to support them before. Now, with edge AI and ML we can envision networks of heterogeneous objects that are self-organizing and can meet high KPIs even in harsh scenarios via, e.g., reinforcement learning [7]. In this context, we are witnessing the shift of AI and ML components closer and closer to the edge, to the point that ML is projected to be an actual part of 6G technologies, rather than something that builds on top of them as it was with previous generations. Another remark is that this transition is expected to take place transversely, which means that potentially all 6G technologies will be affected at once. This section aims to bring forth the potential achievable by applying AI to the purpose of lowering the gap brought by the digital divide. Current trends suggest that both the service-delivery gap and the service-fruition gap would greatly benefit from embedding AI into shared resources. In such a context, any single node of the network will produce data about connectivity, environment, and such, and the collection of big data from IoT scenarios is a key enabler to better understand the challenges of various nature in Internet access. Specifically, data is then analyzed through ML to create more inclusive and scalable networks. For example, data could be gathered and investigated to comprehend which categories of people make better use of the benefits provided by ICT and which ones find difficulties in doing so. In [10], a predictive ML technique is implemented to analyze the primary socioeconomic factors that cause the digital gap in Spain. According to [33], ML can improve the network performance through the undertaking of _adaptive network optimization actions_, enabled by the ability to learn from the wireless environment that ML \begin{table}
\begin{tabular}{l l l} \hline \hline
**Technology** & **Service-Delivery** & **Service-Fruition** \\ \hline Affective Computing & ✗ & ✓ \\ Artificial Intelligence (AI) & ✓ & ✓ \\ Brain-Computer Interface (BCI) & ✗ & ✓ \\ Device-to-Device (D2D) & ✓ & ✗ \\ Exploiting Higher Frequencies & ✓ & ✗ \\ Extended Reality (XR) & ✗ & ✓ \\ Multi-access Edge Computing (MEC) & ✓ & ✗ \\ Non-Terrestrial Networks (NTN) & ✓ & ✗ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification of technologies based on the mastered type of Digital Divide. provides to the network infrastructure. In such a sense, there are a lot of potential usages that meet the purpose of bridging the digital divide. Certain types of network traffic, if their nature is well understood by an AI engine, could be privileged (e.g., remote health diagnosis, online lectures) in contrast to others (e.g., entertaining). Historical network parameters observed by edge nodes can also feed a fog/edge ML model so that the distribution of network resources could be locally automated. Moreover, _planning_ capabilities could be introduced to lower the risks of shortages under normal resource usage. This is also crucial to coordinate with mobile and on-demand network resources, as in NTNs, forming a real \"collective network intelligence\" [7] to overcome the service-delivery gap and bring resources where and when they are most needed.