Abdulrahman Yarali - Intelligent Connectivity

There have been many advantages in technology in the last decade breakthroughs. Smartphones, the IoTs, and the introduction to Artificial Intelligence (AI) and its usage within different aspects of our lives have paved the digital era's road to pervasive connectivity. With such great technological changes and their adaptation in different aspects of life, enhancement and increased productivity have been the result. However, humanity did not stop there as we aimed for higher ground with technology, which resulted in more intelligent connectivity. This included the fusion of different technologies, which fostered a path towards much more advanced outcomes. To explore and realize the full potential of the IoT future to capture massive data from the pervasive connectivity of devices, will require investment and coexistence of new technologies. Fourth generation (4G) technology has been around for almost a decade since it was first introduced commercially in December 2009 (Telia Company 2019). With greater advancements, significant outcomes and improvements are being made. This includes the fusion of 5G, IoT, and AI. It focuses on fostering a future that is much more intelligent and will result in a better life where everyone is connected in a much more advanced and intelligent way. AI, ML (Machine Learning), Blockchain, and IoT are the top technologies currently used by businesses to increase efficiency, productivity, and increase their competitiveness in this fast‐paced digital era transformation. Figure 2.1shows companies' investment details on the implementation of these new technologies (Vinugayathri n.d.).

It has been found that IoT now has almost 25 billion devices being connected, with a prediction of 80 billion connected devices by 2025. AI has led to the computer IQ level of 10 000+ to enhance performance and efficiency, and 5G interoperable, flexible, high‐speed, reliable, high capacity with almost zero delay network connection will reach 1.2 billion by 2025 worldwide (Sterlite Tech 2019).

These technologies will form the basis for what is being colloquially defined as Intelligent Connectivity. Through these technologies, it will become possible to transition to a digitally driven, sustainable world. Figure 2.2depicts the detail of AI, IoT, and 5G Intelligent Connectivity (Sterlite Tech 2019).

The future of technology relies on Intelligent Connectivity. High‐speed 5G and sixth generation (6G) networks with IoT technology and AI make up Intelligent Connectivity. A fact that is gradually becoming a reality as IoT technology is improved upon, 5G becomes the new communication standard for mobile devices and AI becomes increasingly commonplace in businesses. The applications for business, agriculture, education, transportation, and public safety is already showing promise as a concept. As it becomes commonplace, it could lead to a revolution that is as big – if not bigger – than the concept of personal computers.

At its most basic, AI refers to machines that can compute to extend their functionalities to such an extent that they can “learn,” displaying the presence of “intelligence” among such artificial constructs (Katsaros and Dianati 2017). This fundamental difference is significant to consider as it should contrast with human intelligence at large. Current AI literature shows the importance of creating “intelligent agents” as a practical manifestation of the technology wherein the specific machines could perceive everything is happening in the environment and successfully capitalizing upon it to reach their own goals. Moreover, a higher consideration is that, apart from learning, the machines should become able to “mimic” human cognition in the most accurate way imaginable.

Figure 2.1 Investment and usage of the most popular technologies.

Figure 2.2 The fusion of 5G, AI, and IoT (GSMA International 2018).

However, it is essential to highlight what these programs may learn in general because “mimicking” human cognition is extremely hard to process many different things associated with the different subject‐based ideas since they have not been explored yet. That specifically brings in necessities that could essentially address the most prescient challenges standing in front of all humankind (Siau and Wang 2018). One of the most important among them is communication, which has become unimaginably fast over time. However, by all indications the 5G network technology straightforwardly points towards the fact that creating these networks could present remarkably prescient challenges (Pagé and Dricot 2016). This fact is most evident by the routines that prominently manage and direct pathways among the immense complexities of what makes a complete and comprehensive network at large. However, the motivation behind such a high=performing network requirement is also an issue that needs addressing.

In terms of 5G networks, it is notable that the primary motivation for the development and advancement is mainly the strain of technologies, such as the IoT. This correctly points towards the mode of innovation of giving everyday commonplace objects and mechanical devices communication and processing capabilities. In almost all of these cases, human intervention needs these object operations and their functionalities. That means these objects or “things” would have to execute their functions with no or minimum possible human intervention (Siau and Wang 2018). Thus, IoT development could lead to a drastic increase in computing devices or specific nodes across the network at large. It is entirely plausible that the current versions would not support billions of such devices, which would also increase dramatically with every passing moment (Yazıcı, Kozat, and Sunay 2014). This issue explicitly highlights the need to implement 5G networks in terms of expectations and objectives. However, what these specific standards imply is altogether different. First of all, it is essential to focus on the present perspectives about AI, alongside all of their critical factors and commonalities.

It is apparent that the case of “learning” is most definitely an essential way of directing what needs to be an intrinsic guiding factor across all forms and manifestations of AI technology. The learning algorithm is mostly used in a sub‐domain of techniques known as machine learning. The technology innovation happens to operate apropos to neural networks, at which point the sophisticated manifestations of execution are significantly reflective of important aspects of a notable issue (Andrieu et al. 2003). Many different learning algorithms are continually being developed with a definite outlook on autonomy and decision‐making. Some notable examples of basic learning algorithms include logic regression, linear regression, decision trees, random forests, etc. It is essential to note that all of these program commonalities involve extrapolation from data obtained through testing and training, so that projections or build models can be manifested automatically (West 2016).

Moreover, these are notable tools that help pull data points together from a confusing and significantly large repository of variable qualities of data. The potential that these learning algorithms hold is quite apparent. They serve as essentially theoretical guides that provide effective solutions all across the board. However, it is also vital to address what their actual application looks like.