Automating increasingly sophisticated telecommunications networks is increasing in both importance and difficulty. The evolution, experts say, is toward autonomous networks.
Despite their label, autonomous networks don’t operate on their own. The transition is to a deeper reliance on AI management and monitoring, but one in which humans remain in charge, according to consulting firm Capgemini.
Generative AI will be key to managing and monitoring autonomous networks that don’t disenfranchise humans. This form of AI has gained a tremendous amount of attention with the introduction of ChatGPT and other platforms. It differs from earlier iterations of AI in that it creates text, video, images and other content.Â
In the autonomous network use case, generative AI would work in partnership with humans and with other forms of AI.
“AI is key to moving from human-managed networks, supported by insights from data, to AI-managed networks,” Capgemini Telco Leader Yannick Martel wrote in a blog post. “Generative AI can thus complement other AI models, such as anomaly detection and classification.”
Humans remain firmly in charge. They configure the network to achieve the desired goal, ensure compliance, and monitor safety and quality of service.
In practice, generative AI can create readable summaries of network status, activity and goal fulfillment that can be understood by the humans in charge. Managers would be able to investigate, question and receive responses.
Humans would define how the generative AI platform manages network reactions to alarms or anomalies. Older generations of AI require development and testing of scripts to do this. In the era of generative AI, natural language can be used and remediation procedures can be extracted from process documents for review and revision by humans, Martel said.
Enabling network monitoring with AI also may be helpful in combatting AI-enabled cybersecurity attacks, according to recent research from cybersecurity firm ReliaQuest.
