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Intel software looks ahead to cloud-native digitisation and generative AI

Software developed by Intel is set for a focus on cloud-native network digitisation and modernisation as well as the potential for generative artificial intelligence (AI), if February’s announcements by the chipset giant are any guide.

Alex Quach, vice president and general manager of Intel’s wireline and core network division, presented results at MWC Barcelona this year from a survey of 300 communications services providers on mastering cloud-native network architectures for agility, with 5G at its core.

“Progress is being slowed by a range of challenges, including unscalable proprietary solutions, lack of in-house expertise, and an inability to find the right technology partners with interoperable solutions,” Quach reported.

“This is in addition to the ongoing pressure for increasingly lower latency, lower cost of ownership, security and better, more power-efficient network performance.”

Supporting the launch of 4th-gen Intel Xeon Scalable processors, the vendor is working to deliver differentiated power management capabilities for lower power consumption, opex, costs, and Scope 2 carbon emissions.

“Networks transitioning to cloud-native architecture can take advantage of microservices based resource management of network functions to utilize compute resources in the most energy efficient way,” wrote Quach.

Software for responsible AI

Intel is also working in 2023 to develop responsible perspectives and principles for AI models, including generative AI, according to Ilke Demir, a senior staff research scientist at Intel Labs.

For example, integrating deepfake detection algorithms into its real-time platform.

“FakeCatcher, the core of the system, can detect fake videos with a 96% accuracy rate, enabling users to distinguish between real and fake content in real time,” Demir wrote in an Intel opinion piece.

Intel’s ‘Trusted Media’ team are also working on what it describes as “responsible generators”.

For example, instead of creating images by ‘training’ on actual individual faces, one mixes and matches parts of a face — such as the nose of person A, mouth of person B, eyes of person C, and so on — to create a new face that does not already exist in a data set, Demir added.

“While some have (expressed) concerns about the potential of generative AI to threaten jobs, there is a greater opportunity to responsibly use generative AI to improve people’s efficiency and creativity,” Demir wrote.

By ‘training’ generative-AI algorithms with existing content and data, applications like natural language processing, computer vision, metaverse and speech synthesis can be further developed.

“In the past few years, generative AI has become more powerful – and therefore more capable of doing problematic things in a more convincing and realistic manner,” Demir said.

( Photo by DeepMind on Unsplash )

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