Lucy Lombardi, Director, Head of Technical and Operational Partnership and Contracts, TIM
Artificial Intelligence is not a new phenomenon. It has had a long gestation period going back to the 1950s and decades of exemplary research that followed. It wasn’t until 2005, however, that the pace of research and development picked up substantially thanks to important technological developments that led to an AI rebirth, especially with respect to machine learning (ML) and, more specifically, deep learning. Chips were faster and cheaper and could support the processing speeds needed by AI and cloud-based storage and compute capacity were increasingly available on an on-demand basis.
The most common form of AI today is machine learning. Rooted in statistics and mathematical optimization, ML is the ability for computer systems to improve their performance by exposure to data without the need to follow explicitly programmed instructions. ML is the field of study that gives computers the ability to learn without being explicitly programmed, sort of like teaching a dog how to catch a ball as opposed to teaching someone to follow a cake recipe. The first method is by trying, giving feedback and letting the dog figure it out; the second approach is based on creating a step-by-step set of instructions to be followed.
We are currently in yet another AI hype triggered by the release of large language models like OpenAI’s ChatGPT andGoogle’sBard.TelcosandOTTplayersareatthesame time focussing on the wonderful opportunities that all the data generated by the IoE (Internet of Everything) will produce and by the increased complexities that will be needed in telco infrastructure deployments: AI is consistently listed among the major technological trends alongside 5G. Here I would like to give you an overview of how AI is playing a fundamental role in the telcos’ digital transformation.1
AI Telco challenges
Besides effectively enabling a whole new stream of IoE, 5G also presents an important opportunity for telcos to drive digital transformation within their own infrastructure. 5G in fact, introduces innovative technological features that enable a more flexible network. The Radio Access Network (RAN) will be composed of a combination of small cells alongside macro cells, antennas will become active with the possibility of beam forming, virtualisation of the RAN will enable the separation of the antenna and the baseband unit with the possibility of centralising the latter. As a consequence, topologies will grow more complex with small cells and new antennas, usage patterns will become less predictable from humans alone, the radio propagation models will become harder to compute with new radio spectrum bands and denser topologies.
On the Core Network side, the SDN and NFV trends will develop further to mold into a new cloud native infrastructure based on micro-services and containers. Today’s networks are hugely complex already, and, going forward, they will acquire a whole new level of sophistication.
With 5G, topologies will grow more complex with small cells and new antennas, usage patterns will become less predictable for humans alone, the radio propagation models will become harder to compute with new radio spectrum bands and denser topologies. Future networks will have multiple technologies coexisting side by side, e.g., licensed and unlicensed wireless technologies, fixed mobile convergence (FMC) technologies, legacy and future technologies. Telcos will have to base their investment decisions on an increased number of variables and on very granular and complex return of investment assessments.
It is therefore likely that machine intelligence will play a key role in assisting operators in engineering and operating networks. More and more policies will be machine-learned, leveraging on constant measurements from the field and best-in-class simulators, together with a constant supervision and training by the best human experts.
AI will need to be at the heart of networks and will permeate many areas. AI will:
- help manage the increased complexity in infrastructure engineering and optimizing investments;
- facilitate and improve infrastructure operations;
- enable new approaches and opportunities by bringing and linking together different components;
- increase knowledge of your customers and the
- quality of the service provided; and
- enhance security.
Last but not least, AI is not the solution to all problems. There may be some instances where ML algorithms will not offer improvements with respect to traditional algorithms and it is not always obvious to predict when this will happen. It is therefore important to approach AI with an open mind, accepting the possibility that sometimes the outcome will not be the desired one. On the other hand, the lack of control and explainability of the results obtained may be deemed not acceptable, either from an organizational, legal or customer experience point of view.
Conclusion
For the telco industry moving towards 5G, AI adoption will be a necessity in order to drive increasingly autonomous and intelligent networks and improve customer experience through improved knowledge about customer behavior. As AI is based on data, networks will become studded with sources of data that may be very heterogeneous. Also, new ways of working will slowly spread, based on Design Thinking or its evolution. Networks will be interlaced with AI algorithms to help make sense of the increasing complexity and provide a better, more flexible infrastructure.
1 https://www.gruppotim.it/it/newsroom/notiziario-tecnico-tim/autori/l- m/lucy-lombardi.html
Lucy Lombardi is an executive with diversified professional experience in responsibilities and leadership roles in the Telco industry within technical, commercial (profit & loss) and business development/innovation departments.
She is experienced in open and ecosystem innovation, digital transformation, partnership development and scouting, industry influencing and standardization, startup acceleration processes, company-on-campus (academia) engagement, project management, technical mobile networks. Worked on 5G, big data analytic, machine learning, digital products, network sharing and roaming.
Lucy is a pragmatic and energetic leader characterized by clear critical thinking with a multicultural background and excellent skills in managing complexity, driving change, sustaining team energy and a proven track record of achieving results. She has extensive and on-going experience in international organizations (among which GSMA, 3GPP, WAC, OMTP) assuming leadership roles and developing a global network of top-level decision-makers.
