Machine learning is going mobile Computers today are eager to learn—and with new cognitive technology, they can do so even when offline. A new generation of applications and devices that can sense, perceive, learn from, and respond to their users and environment will reshape how many businesses engage with customers—and the world.

Machine learning—the process by which computers can get better at performing tasks through exposure to data, rather than through explicit programming—requires massive computational power, the kind usually found in clusters of energy-guzzling, cloud-based computer servers outfitted with specialized processors. But an emerging trend promises to bring the power of machine learning to mobile devices that may lack or have only intermittent online connectivity. This will give rise to machines that sense, perceive, learn from, and respond to their environment and their users, enabling the emergence of new product categories, reshaping how businesses engage with customers, and transforming how work gets done across industries.

Pushing machine learning onto mobile devices

Firms are starting to outfit smartphones, drones, and cars with chips based on new designs that can run neural networks efficiently while consuming 90 percent less power than previous generations. Research efforts at MIT and IBM suggest that we will soon see more chips on the market that excel at running neural networks at high speed, in small spaces and at low power.  Because of this, mobile devices are becoming increasingly capable of performing sophisticated feats that take advantage of neural networks, such as computer vision and speech recognition, once reserved for powerful servers running in the cloud.

This article originally appeared in Deloitte.University Press .  To read the full article, click here.