Training a machine-learning model on an intelligent edge device allows it to adapt to new data and make better predictions. For instance, training a model on a smart keyboard could enable the keyboard to continually learn from the user's writing. However, the training process requires so much memory that it is typically done using powerful computers at a data center, before the model is deployed on a device. This is more costly and raises privacy issues since user data must be sent to a central server. To address this problem, researchers at MIT and the MIT-IBM Watson AI Lab developed a new technique that enables on-device training using less than a quarter of a megabyte of memory.

The topic of AI is not new and each one of us is benefiting from AI every day, transforming many aspects of our lives. This trend is fueled by edge computing which is providing opportunities to move AI workloads from the Intelligent Cloud to the Intelligent Edge for improved response times and bandwidth savings. In combination with Digital Twins and IoT, there is a strong trend not only in manufacturing but also in other industries to leverage AI/ML analytics for getting better and faster insights for improved Predictive Maintenance and more. The benefit of edge deployments is especially strong when it comes to computer vision models that take large data streams like images or live video as input. With edge computing, these large data streams can now be processed locally at the device / client, eliminating the need for significant bandwidth or privacy concerns associated with streaming into a cloud data center. Edge video analytics systems can execute computer vision and deep-learning algorithms either directly integrated into the camera or with an attached edge computing system.

Elevators that respond to voice commands, cameras that notify store managers when to restock shelves and video streams that keep tabs on everything from cash register lines to parking space availability. These are a few of the millions of scenarios becoming possible thanks to a combination of artificial intelligence and computing on the edge. Standalone edge devices can take advantage of AI tools for things like translating text or recognizing images without having to constantly access cloud computing capabilities. At its Ignite digital conference, Microsoft unveiled the public preview of Azure Percept, a platform of hardware and services that aims to simplify the ways in which customers can use Azure AI technologies on the edge – including taking advantage of Azure cloud offerings such as device management, AI model development and analytics. Roanne Sones, corporate vice president of Microsoft's edge and platform group, said the goal of the new offering is to give customers a single, end-to-end system, from the hardware to the AI capabilities, that "just works" without requiring a lot of technical know-how.

DeepCube, the award-winning deep learning pioneer, today announced that its software-based deep learning acceleration platform has been recognized as a winner in several recent, prominent AI awards programs. These awards celebrate the top AI innovations and leaders across the globe. DeepCube's inclusion validates the immense potential of its patented deep learning acceleration platform that dramatically improves performance, latency and usability of deep learning on intelligent edge devices and in data centers. "Enterprises across industries are enticed by the potential for AI to unlock business impact and efficiencies; however, real-world, edge and data center deployments of deep learning remain out of reach, due to the immense size, processing power and memory requirements of these models," said Dr. Eli David, Co-Founder and Chief Technology Officer, DeepCube. "It's a difficult technical challenge, but it's one we're committed to solving at DeepCube. In 2020, we've made significant strides – both for our business and for the industry as a whole."

Dr. Eli David is a leading AI expert specializing in deep learning and evolutionary computation. He is the co-founder of DeepCube. Over the last several years, deep learning has proved to be the key driver of AI advancement and improvements. Drawing from how the human brain operates, deep learning is responsible for advancing AI applications from computer vision to speech recognition to text and data analysis. Deep learning models are trained in research labs using large amounts of training data to demonstrate how the technology could manifest in real-world deployments.