A team of about 30 computer scientists, software engineers and research interns at Microsoft’s research labs in Redmond and Bengaluru are developing a new class of machine-learning software and tools to embed artificial intelligence onto bread-crumb size computer processors.
The project is part of a paradigm shift within the technology industry that Microsoft CEO Satya Nadella recently described during his keynote address at the company’s Build 2017 conference in Seattle, by saying that “We’re moving from what is today’s mobile-first, Cloud-first world to a new world that is going to be made up of an intelligent cloud and intelligent edge.”
According to researchers, pushing machine learning to edge devices reduces bandwidth constraints and eliminates concerns about network latency, which is the time it takes for data to travel to the Cloud for processing and back to the device.
On-device machine learning also limits battery drain from constant communication with the Cloud and protects privacy by keeping personal and sensitive information local.
The team of scientists are utilising top-down and bottom-up approaches to the challenge of deploying machine-learning models onto resource-constrained devices.
The researchers imagine all sorts of intelligent devices that could be created, smart soil-moisture sensors deployed for precision irrigation on remote farms to brain implants that warn users of impending seizures so that they can get to a safe place and call a caregiver.
The top-down approach involves developing algorithms that compress machine-learning models trained for the Cloud to run efficiently on devices such as the Raspberry Pi 3 and Raspberry Pi Zero.
As many of today’s machine-learning models are deep neural networks, a class of predictors inspired by the biology of human brains, researchers are using a variety of techniques to compress deep neural networks to fit on small devices.
The bottom-up approach starts from the tiny end of the spectrum, where team members are focused on building a library full of training algorithms, each tuned to perform optimally for a niche set of scenarios: one for applications such as the brain implant, for example, and another to detect anomalies such as in a jet engine to predict when maintenance is required.
A prototype device in development to showcase the potential of this research is an intelligent walking stick that can detect falls and issue a call for assistance. Another potential application is an intelligent glove that can interpret American Sign Language and voice the signed words through a speaker.
The drive to embed AI on tiny devices is part of a broader initiative within Microsoft’s research organisation to envision technologies that could be pervasive a decade from now.
