Deloitte Global predicts speed up of machine learning 'on the go'

Machine learning will revolutionise how we conduct simple tasks like translating content, but it also has major security and health consequences that can improve societies around the worldPaul Sallomi, Deloitte Global

Deloitte Global predicts that over 300 million smartphones, or more than one fifth of units sold in 2017, will have machine learning capabilities within the device in the next 12 months.

Mobile devices will be able to perform machine learning tasks even without connectivity which will significantly alter how humans interact with technology across every industry, market and society, notes Deloitte Global in its 16th Technology, Media Telecommunications (TMT) Predictions.

Over time, machine learning on-the-go will not just be limited to smartphones, the report states.

These capabilities are likely to be found in tens of millions (or more) of drones, tablets, cars, virtual or augmented reality devices, medical tools, Internet of Things (IoT) devices and unforeseen new technologies.

“Machine learning is fascinating as it will revolutionise how we conduct simple tasks like translating content, but it also has major security and health consequences that can improve societies around the world,” says Paul Sallomi, Deloitte Global TMT industry leader, in a statement.

“For example, mobile machine learning is a strong entry point to improve responses to disaster relief, help save lives with autonomous vehicles, and even turn the tide against the growing wave of cyberattacks.”

Smartphones, for instance, are increasingly becoming a critical tool in disaster relief.

With machine learning, smartphones can be used by foreign aid workers to translate languages or assess medical emergencies in real time. Currently, the mobile machine learning device must be connected to far off data centres and can only do so provided the cellular network is working.

In emergencies such as these, mobile devices able to perform machine learning tasks without connectivity would be a significant gain, the report states.

Autonomous vehicles, on the other hand, will need to have machine learning capacity all the time, not just when cell signals are strong.

“At the speed cars travel on highways, making decisions on board would offer vitally lower latency...every millisecond counts.”

In the cybersecurity arena, the report notes that IoT devices are not regularly scanned for malware nor are they easily patchable.

In late 2016, chip vendors have already suggested that on board machine learning could detect zero day or previously unknown malware as well as detect or classify suspicious or anomalous behaviour.

On board machine learning, therefore, has the potential to protect these devices and might even help turn the tide against the growing wave of cyberattacks, the report points out.

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