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Categories: AI ARTIFICIAL INTELLIGENCE Aruba Edge Computing HPE IoT

As more devices come online, each generating data, the way information is analysed and used to facilitate machine learning will have to change.

Data is key to improving the accuracy and predictions of machine learning and artificial intelligence (AI) systems–the more images of apple and oranges it is fed, the better it will be at distinguishing the two.

The “smartness” of a machine was data-driven, said Goh Eng Lim, vice president and CTO of high performance computing and AI, Hewlett-Packard Enterprise (HPE), who was speaking to media at the vendor’s Reimagine Summit in Singapore.

However, he stressed, companies should not be focusing only on data retention as establishing volume alone was not adequate. Data also needed to be curated, labelled, and federated.

For one, Goh noted, it would not be feasible to push all the data generated by every connected device back to the data centre to be analysed.

“The network can’t keep up,” he said. “Therefore, you’ll need the IoT device to be smarter so it can make smart decisions at the edge, for instance, only sending back information that’s needed back to the network.”

The IoT device could ascertain if the data was of high quality and should be pushed back to the network to facilitate deep learning, or to process the learning at the edge and send back only the knowledge–rather than pure data.

The edge or IoT device would need to acquire more intelligence in order to carry out such decisions and tasks, Goh said.

HPE has been touting the importance of edge computing, peddling its range of HPE Edgelinesystems, which it said would be necessary to support more compute and better manage data at the edge of the network.

Putting IoT compute at the edge also addressed latency as well as data sovereignty issues, said Mark Verbloot, HPE Aruba’s Asia-Pacific Japan director of systems engineering, who was speaking at the summit.

It would enable data insights to be processed and accessed more quickly, Verbloot said.

In a 2016 ZDNet report, researchers at A*Star’s Institute for Infocomm Research in Singapore said they had begun exploring technologies–specifically, distributed data analytics–that would enable data to be analysed more efficiently within the limited size and computational power of IoT devices.