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Title: Intelligent Networking, AI and Machine Learning for Telecommunications Operators

Challenges remain, but making progress in all areas


Table of Contents:

1  Executive Summary - Key Takeaways and Overview

0.25 page – Beth Cohen  will fill in when the paper is mostly complete.


2  Background

0.5 page


2.1 Autonomous Network Beth Cohen 

2.2 LLM & GenAI Sandeep Panesar 

2.3 LFN & Previous AI WP Beth Cohen 

2.4 LF Data (Thoth)- Sandeep Panesar Beth Cohen 

3  Challenge Statement and Motivation Beth Cohen 

1 page

Focus on Telco pain points only, 

3.1 traditional CSP pain points - Beth Cohen 

  • Operational Efficiency –   reduce costs and errors, potentially increase margins
  • Network Automation - reduce expenditure

3.2 Emerging CSP Opportunities Sandeep Panesar 

  • Converged Infrastructure for the era of AI
  • Converged Service for the era of AI
  • Business Innovation - New revenue streams (data monetization?)

4.1 network AI explorations so far

1.5 page

4.2 challenges towards fully autonomy Lingli Deng Andrei Agapi 

0.5 page

  • 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 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

4.3 Network LLM: the game changer?

Jason Hunt (at least on how foundation models can be applied to network data)

1 page

4.3.1       issues solved/mitigated Andrei Agapi 

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

4.3.2       issues added/aggravated

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

5  How could Open Source Help?

2-3 pages

5.1 Related Open Source Landscape

1-2 pages

  • l  Network communities
  • l  AI communities Andrei Agapi 
  • l  Integration BPs
  • l  Verification programs

5.2 Common Vision: intelligence plane for XG

1 page

  • l  natural intent interaction
  • l  cognitive smart orchestration
  • l  real-time meta-network verification
  • l  built-in knowledge open loops

6  Call for Action Beth Cohen 

0.25 page



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