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Talent for tomorrow - Phase II

URN C23.0.597
Topics Autonomous networks, Revenue assurance

Revenue has increased by 1739M RMB (238M USD) and the network service operation cost has been reduced by 895M RMB (123.08M USD).<br>

Challenge * Targeting all operational scenarios of Autonomous Networks, the Expert Robot Family is created to possess three core capabilities: Intent-Based, Task-Based, and Knowledge-Based Expert Systems. The aim is to facilitate systematic AI capability development, sharing, and reuse in a production environment. * The Expert Robot Family plays roles analogous to those of four talented family members in the Intent-Based, Task-Based, and Knowledge-Based closed loops across the network layer, service layer, and business layer. This approach is designed to elevate the performance of Autonomous Networks to a higher level. * In the era of AI, where expert robots are replacing, and in some cases, surpassing humans, traditional human positions are being widely questioned. Solution The Talent for Tomorrow Phase II project aims to explore and implement "Human-Robot Collaboration" and "Robot-Robot Collaboration" in production environments. This initiative introduces talent transformation through the evolution of Autonomous Networks with the Large Language Model (LLM). In Phase I of the project, we introduced three methodologies for transforming humans and robots, and we designed four new role families to achieve "Human-Robot Collaboration." This achievement aligns with the maturity model's concept of "Robot Assisting Human." For Phase II of the project, we built upon the methodologies developed in Phase I. Our solution optimized the Phase I architecture to create Knowledge-Based, Task-Based, and Intent-Based Closed Loops. Within the three layers of Autonomous Network architecture, our Phase II project establishes a Knowledge-Based Closed Loop with network AI as the core capability, a Task-Based Closed Loop with network digital twins as a necessary component, and an Intent-Based Closed Loop that spans all three architecture layers. This approach drives Autonomous Networks to adapt to business requirements. We introduced LLM technology to enhance expert robots within the Expert Robot Family, enabling them to reach maturity level 2, "Robots replacing Humans."



C23.0.597 Talent For Tomorrow Phase II Main Slide Deck

C23.0.597 Talent For Tomorrow Phase II Pitch

Inform feature

Helping humans and robots work together on autonomous networks


Be Universe Ahead


Phase I Recap Video


White Paper

China Unicom Autonomous Networks White Paper 3.0

中国联通自智网络白皮书 3.0

Contact team

Email the members of the Catalyst team to request more details.


Team members

BroadTech Technology Co., Ltd logo
China Information Technology Designing Consulting Institute Co., Ltd. logo
China Telecommunications Corporation logo
China Unicom logo
Huawei Technologies Co. Ltd logo

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