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• l  Radio Access Network ChangJin Wang 
• l  Core Network Hui Deng ChangJin Wang 
• l  Bearer & DC Network - Cisco?
• l  Cross domain network AI platform Lingli Deng //moved to 4.3.1
• l  Telecom Cloud Hui Deng 

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• High quality structured data Communication networks are different from general human-computer interaction in that a large number of interactions between systems use structured data. However, due to the complexity of network systems and differences in vendor implementation, the degree of standardization of these structured data is very low, causing them to be divided into "information islands" that cannot be uniformly "interpreted". The data in each "island" There is a lack of "standard bridge" to establish correlation between them, and it cannot be used as effective input to incubate modern data-driven AI/ML applications.
• AI trustworthiness Different from traditional AI application scenarios, in order to meet carrier-level reliability requirements, communication network operation management needs to be strict, precise, and prudent. Although operators have introduced various automation methods to the greatest extent in the process of building autonomous networks, legal person organizations composed of human units are still responsible for ensuring the quality of communication services. In other words, AI is still assisting people and cannot replace people. Not only because the loss caused by the AI algorithm itself cannot be defined as the responsible party (the developer or the user of the algorithm), but also because the deep learning model based on the AI/ML algorithm itself is based on the mathematical characteristics of statistics, resulting in uncertainty in its behavior, its credibility is difficult to guarantee.
• Non-economic margin cost According to the analysis and research of the previous white paper [a the reference to the previous white paper], there are a large number of potential AI technology application scenarios in communication networks, but to independently build a customized data-driven AI/ML model for each specific scenario, would be uneconomical and hence unsustainable for both the R&D stage and the operation stage. How to build an effective business model between basic computing power providers, general AI/ML capability providers and algorithm application consumers in specific communication network scenarios will be the prerequisite for its effective application in the field of autonomous networks.
• Contextual data sets 
• l  TBA

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4.3.1       issues solved/mitigated Lingli Deng Andrei Agapi 

• high quality structured data Large language models can be used to understand large amounts of unstructured operation and maintenance data (for example, system logs, operation and maintenance work orders, operation guides, company documents, etc., which are traditionally used in human-computer interaction or human-to-human collaboration scenarios), from which effective knowledge is extracted to provide guidance for further automatic/intelligent operation and maintenance, thereby effectively expanding the scope of the application of autonomous mechanism.
• AI trustworthiness moved to 4.3.2
• non-economic margin cost Although equipment manufacturers can provide many domain AI solutions for professional networks/single-point equipment, these solutions are limited in "field of view" and cannot solve problems that require a "global view" such as end-to-end service quality assurance and rapid response to faults. . Operators can aggregate management and maintenance data in various network domains by building a unified data sharing platform, and based on this, further provide a unified computing resource pool, basic AI algorithms and inference platform (i.e. cross-domain AI platform) for various scenario-specific AI for end-to-end scenarios and intra-domain scenarios. Applied reasoning platform.
• TBA

4.3.2       issues remained/added/aggravated

• high quality structured data
• AI trustworthiness
• non-economic margin cost
• l  TBA

5  How could Open Source Help?

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