From cloud to the edge: On-device artificial intelligence boosts performance
If artificial intelligence (AI) goes according to plan, we’ll barely notice it taking hold. As a result, and despite the hyperbole, AI may be the quietest major computing revolution the world has ever known. What’s happening at one of the world’s leading children’s hospitals is a great example.
Great Ormond Street Hospital (GOSH) clinicians see more than 300,000 children every year, many of them with critical care needs. To ensure its patients receive the best possible care in a safe and secure environment, GOSH began testing an AI-based person recognition system where medical staff, patients, and authorized visitors receive access to certain secure areas of the hospital while any unauthorized entrants are either stopped or flagged by the system. The solution uses a network of AI-enabled smart cameras to examine each person’s face, body structure, and gait. The system then automatically cross-checks facial features against a database of registered people. The system has increased hospital security and has clinical benefits, too. For example, if a child requires immediate care, an emergency room doctor can quickly be located and notified, ensuring the team is ready to spring into action when needed.
In the past, coping with such a sophisticated system would have required a sprawling data center and its associated costs. But the AI revolution has sparked a movement to perform AI computing differently. Instead of a cloud link, data generated by GOSH’s innovative cameras is processed locally on the cameras themselves using a tiny chip. Not only does this “AI at the edge” system process data faster and more cost efficiently, it never leaves the confines of the hospital.
A branch of AI, machine learning (ML) uses sophisticated algorithms in models that can learn from data and identify important patterns. By uncovering connections, ML helps businesses make better decisions without the need for human input.
Today, ML is powering all kinds of applications, many of which are mobile, as smartphone users climb to an anticipated 3.8 billion by 2021. Examples range from fingerprint recognition and photo-sorting to more innovative use cases, including:
Smart inhalers: AI-powered inhalers run real-time ML algorithms that calculate a patient’s lung capacity and breathing patterns. This data is then interpreted on the device itself and sent to a smartphone app, enabling healthcare professionals to personalize regimens for asthma sufferers based on detailed sensor data.
Robot companions: An AI-driven social robot for senior citizens uses ML to understand the preferences, behavior, and personality of its owner. Based on these interactions, the robot can automatically connect older adults to stimulating digital content, such as music or audiobooks, as well as recommend activities, remind the user about upcoming appointments, or connect to family and friends through social media. And unlike most AI systems, which require voice activation, the robot proactively communicates with its user. For example, if a senior citizen has been sitting for an extended period of time, the robot can automatically recommend calling a friend or taking a walk.
Reindeer cam: A smart camera system detects herds of reindeer through ML algorithms as they approach train tracks in remote parts of Norway where the animals are often needlessly killed. By processing information on the device itself, the system can warn train operators in real-time to reduce speeds when the animals are present, thereby preventing accidents and train delays.
Hardware vendors are taking note and increasingly equipping devices with ML-capable chips. As a result, these devices are capturing and processing data in real time, providing instantaneous situational analysis, identifying patterns, and supporting quick AI-enabled decision making.
Edge AI devices are mainly running ML inference workloads—where real-world data is compared to a trained model.
