A team of researchers and experts at Nvidia have submitted a paper to IEEE that talks about the theory and practical steps to deliver useful Generative AI, or GenAI, models for telecoms networks. The team uses the real-life example of a chatbot which can answer questions on O-RAN which was demonstrated to the O-RAN Alliance (and is now available on GitHub).
The paper, called “A Primer on Generative AI for Telecom: From Theory to Practice” aims to solve a key problem. As the authors note, “existing literature primarily focuses on the theory or vision of GenAI for telecom, often overlooking the implications and challenges that exist in practice. This article aims to address this gap by bridging between the intricacies of theories and their manifestation into use case enablement in practice.”
In particular the authors use a specific technique called retrieval augmented generation [RAG], to take a generic AI and give it deep domain-specific information. As well as providing a better set of data for domain-specific questions, the authors also claim that this reduces the incidence of AI hallucinations; which makes sense, since ‘hallucinations’ in AI seem to be the equivalent of people making things up to cover holes in their knowledge.
The telecoms industry is facing a growing challenge with skills, insofar as the variety of disciplines involved in making telecoms systems work is increasing (on which, see this recent webinar). Making knowledge more available to people who haven’t yet been able to develop domain expertise can only be a good thing for the industry.
