00:00 – Intro
02:10 – Guest intro (Dan Warren)
03:22 – Samsung Network’s categorization of the role of AI – Networks for AI, AI for networks
04:44 – Current interesting use cases of AI – RAN energy efficiency, complex capacity, and coverage optimization
06:40 – The current status of use of AI in telecom, major industry focus areas – optimizing opex and capex, experience enhancement
09:15 – Critical role of software-based networking and virtualization in enabling AI
12:48 – Are legacy networks w/o software-based networks out of luck for AI?
15:40 – How to decide where to run AI workload – where is the AI compute needed?
19:05 – Who will develop AI models for telecom? – proprietary vs. standard, differentiation etc.
24:35 – Dichotomy between AI differentiation, open networking, and offering AI as a service layer
26:55 – Samsung’s approach to AI as a service/software leveraging its software-based networking legacy. Might be different for more established players?
29:06 – Does today’s architecture (e.g., SMO) have hooks for managing AI end-to-end?
30:01 – Data challenge of operators for AI – different forms, formats, sources, granularity, etc. Will things like NWDAF solve it?
31:50 – Operator understanding of the AI challenges ahead – transforming from a hardware operator to a software management company
34:36 – Is the investment needed for AI an impediment to its adoption? Worries of the fast-moving and changing AI landscape
38:46 – AI on RAN – Samsung Network’s views
40:43 – AI on RAN – operators moving from telecom service providers to AI (edge) Data Center/infra providers – Does it make sense?
43:19 – AI timing – Will large-scale deployments wait for 6G, or can start with 5G?
45:16 – Closing
