...

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

Since publishing  LFN published the seminal Intelligent Networking, AI and Machine Learning While Paper in 2021, the telecom industry has seen tremendous growth in both interest and adoption of the Artificial Intelligence and Machine Learning (AI/ML) technologies.  While it is still early days, the industry is now well past the tire kicking, lab testing phases that was the state of the art in 2021.  Intelligent networking is coming into its own as telecoms increasingly use it for operational support, whither ; whether that means deploying intelligence into their next generation 5G XG networks, where it is used for ?? and it continues its spread in operations.

Key Takeaways

or for automation of network management tasks such as ticket correlation and predictive maintenance.  The LFN and Open Source have a pivotal role to play in fostering and developing intelligent networking technologies through the continued support of key projects, ranging from building a common understanding of the underlying data models to developing infrastructure models and integration blueprints. Link to some of the projects like Anuket Thoth, Super Blueprint and others that are related to AI/ML

The future of OSS Networking AI is in the hands of the individuals and organizations who are willing and able to contribute to new and existing projects and initiatives. If you are involved in building and operating networks, developing network technology or consuming network services, consider getting involved. Engaging with the OSS communities is a way to shape the future of Intelligent Networking. 

Key Takeaways

• Intelligent networking is rapidly moving out of the lab and being deployed directly into production
• Operational maintenance, and service assurance are still a priority, but there is increasing interest in using AI/ML to drive network optimization and efficiency
• More research and development is needed to establish industry wide best practices with a shared understanding of intelligent networking to support interoperability.
• There has been some work on developing common or shared data sources and standards, but it remains a challenge
• LFN and the Open Source community is key in furthering the development of intelligent networking now and in the future
• Some intelligent networking has been deployed by Telecoms, but more research and development is needed to establish industry wide best practices.
• Currently the industry is more interested in using artificial intelligence and machine learning to address operational, maintenance, and service assurance issues over network optimization. 
• The Telecom industry needs to develop common AI platforms and intelligent networking frameworks and methodologies to support the delivery of new services quickly and efficiently. 
• A shared understanding of intelligent networking will help to support interoperability.
• Creating commonly understood sources of reliable data is difficult, both within a company across business units and across the Telecom industry
• The Open Source community can play a key role in furthering the development of these frameworks and best practices. Some projects that look promising include, Anuket Thoth, O-RAN, 3GPP SA5 and ITU-T standards organizations, as well as the ONAP, O-RAN and TIP open-source projects.

2  Background Intro/History Beth Cohen 

0.5 page

At their hearts, telecoms are technology companies driven by the need to scale their networks to service millions of users, reliably, transparently and efficiently.   To achieve these ambitious goals, they need to optimize their networks by incorporating the latest technologies to feed our connected world's insatiable appetite for ever more bandwidth.  To do this efficiently, the networks themselves need to become more intelligent.  At the end of 2021, over 2.5 years ago, LFN published its first white paper on the state of intelligent networking in the telecom industry.  Based on a survey of over 70 of its telecom community members, the findings pointed to a still nascent  

Telecoms need to be able to incorporate new technologies and next-gen connectivity such as 5G, to customers and end users.  To achieve these ambitious goals, they need to optimize their networks – make them more intelligent if you will.  Some of the tools needed include artificial intelligence (AI), machine learning (ML) and artificial intelligence operations (AIOps). This document will explore what intelligent networking means to telecoms, vendors and customers, and how AI and ML technologies and tools can be used, the cultural shifts the industry needs to make it a success, and what to bear in mind when deploying machine learning across a telecom network.

...