Artificial intelligence explained

With the launch of ChatGPT by OpenAI in November 2022, AI went mainstream, and started a very public debate around its use. ChatGPT is a Large Language Model (LLM) powered by the Generative Pre-trained Transformer (GPT) architecture. LLMs are in the category of AI algorithms called “generative AI” that are capable of extraordinary new content creations. In the case of ChatGPT this means primarily text, but similar models also generate audio, code, images, and videos. What grabbed the public’s attention is the uncanny ability of ChatGPT to mimic human language use and respond appropriately and at length to questions on almost any subject. It appears at times to have human-like intelligence.

This has brought into the open concerns around the use of AI. What worries people is the way generative AI can veer into “hallucinatory” states, where it generates disinformation with the same confidence and conviction as it imparts useful, true information. This triggers a long-standing counter-narrative about AI, where upon reaching a generalized intelligence, it is feared that AI might be able to assume agency, pursuing its own ends, not our own.

Despite this uncanny facility to mimic intelligence, however, general intelligence currently remains in the realm of science fiction. Generative AI’s mistakes are usually due to the quality of, and bias in, the data and algorithms which they have been trained on, and the kinds of open-ended questions that users prompt. The ease with which ChatGPT can generate disinformation, nonetheless, reminds us that this amazing technology needs to be developed and used responsibly.

The AI/ML applications used in networks fall into the fixed-purpose classification and many have been around for decades. The concept of self-organizing networks (SON), for instance, was introduced with LTE in 3GPP Release 8 in 2009. With SON, AI/ML is used to automate planning, configuration and management of mobile networks. A leader in SON applications, Nokia research on SON is ongoing, with the goal of automatically optimizing and healing networks before customer-affecting issues occur.

AI is also playing a big role in network security as it can analyze large volumes of data in real-time to identify subtle patterns and anomalies that are often missed, improving accuracy and speeding up detection. Nokia’s telco-centric eXtended Detection and Response (XDR) solution NetGuard Cybersecurity Dome uses AI/ML algorithms to analyze and identify potential security threats, enabling real-time detection and response to security incidents.

Nokia also uses AI in communications networks to optimize power consumption of both active and passive network equipment. Nokia AVA Energy Efficiency enables operators to achieve as much as a 30% reduction in power use. And research into further uses of AI in future networks includes the possibility of predicting usage patterns and turning on and off components even at the processor level to optimize energy use.

In the planning for future networks such as 6G, we envision AI/ML will be embedded in end user devices, radios and radio access networks (RANs). Central to our research on these future use cases is the responsible use of AI. This is a concern across the industry and among regulators and standards bodies with whom we work closely, which is why we developed our Responsible AI principles and encourage the industry to adopt them. We believe these principles should be applied the moment any new AI solution is conceived and then enforced throughout its development, implementation and operation stages. Rather than being perceived as a set of rules and guidelines that limit innovation, these six pillars should be seen as an opportunity for creating the collaboration that AI requires.

AI will be a critical technology in the future as our world becomes increasingly complex. Many of the technologies that will be key to water management, autonomous transportation, smart cities, clean energy and communications will rely on AI. Narrowly framed, fixed-purpose AI applications have proven their usefulness and safety over many decades. Used responsibly, they will be a critical technology in future networks and will help to make our world more sustainable, safe and secure.