It is not yet clear how big a role mobile networks can play in cloud computing infrastructure and services built on edge computing and artificial intelligence. It does seem clear that to the extent real estate (facilities at the edge, power and physical security at the edge) and artificial intelligence as a service are important, and if AI itself becomes a cloud service, some new role is conceivable.
If the AI operations have to be done externally to any specific appliance, it is clear that cloud computing assets will be involved.
If many new apps require low latency response and use of artificial intelligence and machine learning, computing at the edge is almost necessary.
Also, as AI becomes a standard cloud service, or a feature of other cloud services, it will be possible for many new app providers to build services and apps on AI and machine learning, buying such capabilities as they buy compute cycles or storage.
If artificial intelligence becomes a big part of the next big wave of growth for cloud computing, that should therefore allow firms of all sizes to use advanced machine-learning algorithms just as they today buy computing or storage. And that obviously will be possible for app units of mobile operator businesses.
In other words, though it has not been so clear what natural advantages telco networks could bring to cloud computing infrastructure, there might be a different potential for mobile networks.
That seems especially true for apps that require both AI and low latency, such as automated vehicles.
We can assume that cloud workloads of the future likely will include AI capabilities. “We believe AI will revolutionize almost all aspects of technology, making it easier to do things that take considerable time and effort today like product fulfillment, logistics, personalization, language understanding, and computer vision, to big forward-looking ideas like self-driving cars,” said Swami Sivasubramanian, Amazon AI VP.
“Today, building these machine learning models for products requires specialized skills with deep Ph.D. level expertise in machine learning,” he said. “However, this is changing.”
Amazon Web Services has added predictive analytics for data mining and forecasting, to its cloud services, opening up machine-learning algorithms first developed for internal use, to customers of AWS.
Google application program interfaces are being made available to its cloud services customers to support translation, speech recognition and computer vision.
Microsoft likewise talks about “conversation as a platform,” where voice-responsive systems use artificial intelligence to handle simple customer requests.
Over time, though, that capability will extend, allowing the AI-enhanced interfaces to integrate information from different sources, allowing more complicated transactions to be supported.
AI will be democratized, some would say.
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