2017 is expected to be a big year for artificial intelligence, with use cases across a wide range of connected use cases and network applications.
AI is largely seen as the full realization of what big data, analytics and automation have been moving towards — a fuller implementation of machine learning. It is being explored in a variety of verticals. Ford announced last week that it is investing $1 billion in startup Argo AI aimed at developing a brain for connected, autonomous vehicles. Consumers are getting comfortable with the concept of AI in their homes. Amazon has said that it sold millions of its Echo devices, which rely on Amazon’s Alexa AI for user interactions. AI research is also flourishing. As CIO Magazine noted, “Apple, Facebook, Google and Microsoft all open-source or share their latest research in AI to advance developments in the space. These moves from such notable organizations also meet the collective interests of scientists and researchers who prefer to share their findings with the larger community, instead of limiting access to a select group.”
On the telco side, work exploring AI’s role in the network has been focused on more intelligent orchestration with an eye toward AI-assisted implementations of 5G. In early 2016, KDDI R&D Laboratories and partners Wind River KK, Hewlett-Packard Japan, and Brocade K.K., developed an AI-assisted automated network operation system for virtualized networks; the proof-of-concept was aimed at exploring automated network operation with AI-based failure prediction.
In identifying key big data trends for 2017, Ovum said that it has “seen machine learning proliferate, from consumer services to enterprise applications and tooling; for instance, machine learning has become table stakes for data preparation and other tools related to managing curation of data for data lakes.”
Ovum went on to say that “machine learning will be the biggest disruptor for big data analytics in 2017.”
Nokia plans to boost its NFV orchestration capabilities through its recently announced purchase of Comptel for 347 million euros ($370.8 million). While Nokia’s initially announced plans for Comptel focused on NFV orchestration and rebalancing its focus on software versus hardware, Comptel launched late last year a suite of AI-powered applications for telcos with customer engagement and “next best actions” in mind.
Mikko Jarva, CTO of the intelligent data business unit at Comptel, said that AI applications, such as the new FasterMind suite of applications, is the next logical step for big data.
“If we think about big data applications, they have been around and related to data storage and data discovery, reporting and also interesting predictive analytics use cases,” Jarva said. “Going to the next step is when you start making the data actionable and extracting value out of it. You need to come up with a capability that can take the insights and make them work for you.”
FasterMind focuses on recommendations, predictions and automation of real-time customer engagement for cross-selling and upselling. Jarva said that since the applications need to work much faster than typical big data applications, they work with a smaller, more focused data set that is part of an overall “data fabric.” Comptel’s approach, he said, is to leverage AI as part of helping people design use cases and offers, identifying the customer journey and the points at which end users are most open to new offers, and improving the customer experience with contextual information and AI-powered chatbots or real-time data for customer service reps — all on a shorter time-scale that is more relevant to both the operator and the consumer.
“This is part of a bigger trend we have seen in the move toward real-time,” he added. “I think there is more interest in moving from after-the-fact to deciding in real-time what to do with the data and the insights.”
