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Intel: AI everywhere, from the edge to the cloud

A conversation with Cristina Rodriguez at Intel on AI-driven automation as an end-to-end game changer in wireless networks

AI-driven automation will change how we run our networks end-to-end, from the access in the RAN all the way to the core, from the edge infrastructure to the cloud. To get there, we need the right silicon and platforms. Are we there yet?

In this conversation, Cristina Rodriguez at Intel and Monica Paolini at Senza Fili talked about the network transformation that AI and automation enable and how the industry ecosystem is already working on multiple use cases to make wireless networks more efficient and sustainable.

  • Are we ready to fully embark on AI-driven automation?
  • How crucial is automation as we move to cloud-native, software-defined, disaggregated and virtualized networks?
  • Is AI going to accelerate the deployment of open and virtualized RAN?
  • What steps and best practices will take us to AI-driven automation and, eventually, to autonomous networks?
Cristina Rodriguez, VP Network & Edge Group and GM Wireless Access Network Division, Intel
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Conversation timestamps

  • 00:04 AI applications across the network from edge to cloud
  • 04:11 AI applications in wireless networks, including power management, predictive maintenance, and network slicing, with examples of successful demonstrations and potential for AI. Intel projects with Vodafone, Deutsche Telekom and Ericsson, demonstrating Xeon’s capabilities
  • 14:36 Using AI in telecom networks to improve efficiency, reliability, and security
  • 21:22 Using AI to improve network efficiency and predict future issues
  • 25:08 AI in 5G networks used for network slicing and conflict resolution, to lower TCO, ensure SLSs, reduce costs, and improve efficiency. Role of AI to manage complexity
  • 33:05 AI in Open RAN increases efficiency and accelerates innovation
  • 37:23 Intel’s role in providing AI solutions, Xeon, AI built-in acceleration, reduction in power consumption, scalable architectures, importance of software
  • 44:41 AI for video processing, power consumption, and deployment options (cloud or edge?)
  • 49:02 AI at the edge for latency-sensitive applications
  • 54:02 Trust in AI models for network optimization
  • 58:24 Data sources for AI models and long-term goals

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