Edge Machine Learning (Edge ML) is one of the most talked-about tech advancements since the Internet of Things (IoT), and for a good reason. With the rise of IoT came an explosion of Smart Devices connected to the Cloud, but the network was not yet ready to support this surge in demand. Cloud networks were congested, and companies overlooked key issues with Cloud computing, such as security. The solution: Edge ML.
So, what is Edge ML anyway? Edge ML is a technique by which Smart Devices can process data locally (either using local servers or at the device-level) using machine and deep learning algorithms, reducing reliance on Cloud networks. The term edge refers to processing that occurs at the device- or local-level (and closest to the components collecting the data) by deep- and machine-learning algorithms.
Edge devices do still send data to the Cloud when needed, but the ability to process some data locally allows for screening of the data sent to the Cloud while also making real-time data processing (and response) possible.
Artificial intelligence, defined broadly, is the field of training machines to autonomously perform tasks normally thought to require intelligence. Beneath that umbrella is machine learning, in which machines autonomously learn new tasks. Deep learning is a subcategory of machine learning. It involves training machines to process information in a way that mimics the way the human brain learns new things.
Edge ML relies on both machine learning and deep learning algorithms to locally process data, depending on the application.
How Edge ML Works
Before Edge ML came about, smart devices would send all data to the Cloud (see IEEE arXiv:200317172v2). You’ve probably heard the term Big Data. Named after the massive influx of datasets that resulted in part from the IoT, Big Data has become a growing field that attempts to structure and make sense of massive datasets. The processing of this data, such as critical datasets in the medical and industrial sectors, will vastly improve things like the ability to predict and respond (almost) immediately to emergencies. Much of the data collected, however, is superfluous.
Unlike traditional machines, Edge ML devices will analyze and process incoming data at the source and determine what needs to be processed by more powerful algorithms in the Cloud, versus what can be processed locally. For example, if you tell the Amazon Echo, “Alexa, let’s play a game,” or “Alexa, tell me a joke,” the games and jokes available are stored in and processed by the device’s local hardware. This will not require sending data to the Cloud. The device can execute the function (and keep the user happy) without bogging down the Cloud-network. If instead, you ask Alexa about the weather, the device will need to search an external source (in the Cloud) for that data.
Benefits of Edge ML
Edge ML is revolutionary. It solves both security concerns pertaining to storing personal user information in the Cloud and also reduces strain on Cloud networks by processing data locally. It also enables the processing of data in real-time, currently not possible with traditional, cloud-powered smart devices, but critical for technologies like autonomous vehicles and medical devices.
Current and Future Applications
Edge ML is still a newer technology, and while it’s getting a lot of attention, innovators are still determining how to implement this technique across various platforms.
A few existing platforms include smart speakers like Amazon’s Echo and Google’s Home. In the energy and industrial space, some companies have developed systems with predictive sensors and algorithms that monitor the health of the components to notify technicians when maintenance is required. Other systems monitor for emergencies like machine malfunctions or meltdown.
In the future, there is talk about developing Edge ML-based systems in hospitals and assisted living facilities to monitor things like patient heart rate, glucose levels, and falls (using cameras and motion sensors). These technologies could be life-saving and, if the data is processed locally at the edge, staff would be notified in real-time when a quick response would be essential for saving lives.
Edge ML is an exciting new technology that continues to be talked about and developed. It will only be a matter of time before Edge ML-powered devices (like the IoT) becomes a way of life. And what an exciting time that will be.
Sources:
https://arxiv.org/pdf/2003.12172.pdf
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7273223/
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