One size doesn’t fit all. The need for intelligent, personalized experiences powered by AI is ever-growing. Our devices are producing more and more data that could help improve our AI-powered experiences. How can we learn and efficiently process all this data from edge devices? On-device learning rather than cloud training can address these challenges. In this blog post, I’ll describe how our latest research is making on-device learning feasible at scale.
What is on-device learning?
In the past, AI was primarily associated with the cloud. We have moved from a cloud-centric AI where all the training and inference occurs in the cloud, to today where we have partially distributed AI with inference happening on both the device and cloud. In the future, we expect not only inference but also the training or adaptation of models to happen on the device. We call this fully-distributed AI, where devices will see continuous enhancements from on-device learning, complementing cloud training. Processing data closest to the source helps AI to scale and provides important benefits such as privacy, low latency, reliability, and efficient use of network bandwidth.