This track is designed considering the use cases of AI/ML in IMT-2020 networks.
In this track, participants will build, train and deploy ML models for use cases in the network. Problem statements and data sets will be geared towards the challenges of distributed ML Pipeline as described in ITU Y.3172, e.g. optimization techniques, distribution mechanisms, federated learning mechanisms etc.
The Network-track will make sure that the use cases involving AI/ML in 5G networks are covered. Problem statements in this track may mostly use real data depending on the nature of the problem statement.
ML models alone are not sufficient to integrate intelligence in future networks. Training, evaluation, deployment, inference, and application of ML output in the network requires enabling technologies and tools in the network. An end-to-end solution may therefore comprise of an ML model, a set of APIs, data, metadata and other resources to realize the full capabilities of the models in a network.
In this track, participants will design and implement toolsets that can help in an end-to end implementation of ML model deployment in a real network. These toolsets consist of APIs, metadata, and other software such as Adlik, Acumos, ONAP, O-RAN OSC.
The Enablers-track will make sure that the end-to-end 5G solutions are covered and not just the ML models. Problem statements in this track may mostly use no data depending on the nature of the problem statement.
In this track, participants will apply ML/AI in 5G networks to other verticals such as manufacturing, education, health, public safety, transportation/automotive, finance, government, retail, agriculture, energy, smart cities, and media and entertainment. This track allows the combination of verticals and 5G to exploit the green-field opportunities for AI/ML applications. The key drivers of implementing 5G within these industries are the potential revenue growth opportunities for mobile operators and new business models.
One of the flagship events of ITU in collaboration with other UN bodies is The AI for Good Global Summit. It aims to bring forward AI research topics that help achieve the Sustainable Development Goals (SDGs). This is done in order to find practical applications of AI and support strategies to improve the quality and sustainability of life on our planet.
In this track, participants/teams will identify and solve problems whose solutions are aimed to provide socially relevant applications (“AI for Good") in 5G using AI/ML. Some examples of the solutions are advancing education, healthcare and wellbeing, social and economic equality, space research, and smart and safe mobility. Selected teams will be invited to participate in the AI for Good Summit in May 2020.