Intelligent networks: predictive AI between “digital inheritance” and ethical adaptation

Thanks to AI, networks are becoming “intelligent” – capable of anticipating problems, self-optimising, and making ethical decisions. Infrastructures such as telecommunications networks are becoming more reliable and more adaptive, just like living systems. 

In recent years, the evolution of artificial intelligence (AI) and telecommunications networks has reshaped the communication and automation landscape. The challenge today is not only to implement autonomous networks, but also to make them predictive and capable of evolving and cooperating effectively while upholding ethical standards. To meet these requirements, concepts drawn from such diverse areas as Mendelian genetics, fuzzy logic and evolutionary theory are being applied innovatively to the creation of autonomous, predictive networks with the ability to continually adapt and govern themselves ethically. 

Mendelian theory and evolutionary AI 

Mendelian theory was established by the botanist Gregor Mendel and introduced the concept of genetic inheritance as a process in which traits from preceding generations are passed on to their descendants through combinations of dominant and recessive genes. In the context of AI, this principle can be adapted to the creation of evolutionary machines, in which beneficial traits are “inherited” by consecutive generations of AIs. In predictive telecommunications networks, this concept of “digital inheritance” could be applied to the development of machines with hybridised traits optimised for specific tasks, such as traffic management, security, or quality of service (QoS). The result of applying Mendelian inheritance to AI in networks is a continual learning model, in which the network adapts and evolves with each iteration to create a system that is not only predictive but also resilient and autonomous. 

Fuzzy logic and managing the digital ego 

Fuzzy logic is an extension to classical logic introduced by the scientist Lotfi Zadeh, which allows intermediate values between true and false, and thus provides a framework for the management of uncertainty and ambiguity. In AI networks, fuzzy logic offers an innovative way to handle interactions between machines with separate or overlapping objectives, in a concept known as “digital-ego management”. One practical example would be a network congestion scenario, in which a machine responsible for quality of service (QoS) might prioritise its own “ego” to ensure that high-priority traffic continues to flow, while another machine focused on security might be temporarily demoted down the fuzzy scale to conserve resources.  

Predictive networks with continual learning and evolution 

The concept of predictive networks is based on the analysis of historic data and models to anticipate events and behave proactively. With genetic algorithms, it is possible to create a network able to continually re-evaluate and improve its own operating rules. For example, in a telecommunications network serving a town with a fluctuating population, data traffic models are constantly changing. In an adaptive network, AI machines could analyse and identify the most successful configurations by “inheriting” and “mutating” algorithms tuned to specific traffic needs. 

Adaptive governance and ethics in evolutionary AI 

With the introduction of evolutionary AI to telecommunications networks, it is essential to consider governance and ethical principles. Adaptive governance enables AI to evolve within a set of ethical principles, using fuzzy-logic rules to balance performance and ethical compliance. This new evolutionary AI paradigm brings in a network model capable of adapting and operating ethically and safely, fostering confidence in autonomous networks. 

“Network AI transcends the ability to predict and becomes a system that evolves, adapts, and makes decisions in keeping with ethical principles” 

This integrated approach reveals new potential for autonomous or predictive networks, in which evolutionary characteristics, digital inheritance and fuzzy logic facilitate adaptive and collaborative functionality. This network AI transcends the ability to predict and becomes a system that evolves, adapts, and makes decisions in keeping with ethical principles. 

The concepts of Mendelian inheritance, fuzzy logic and adaptive governance open the way to a new generation of AI networks that go beyond prediction, rather than being merely reactive. These networks can continually respond and adapt to changing conditions while remaining resilient and autonomous. By incorporating ethics and governance, these networks provide a robust model for sensitive and critical applications. 

22/07/2025