What is Edge AI: its Benefits and Use Cases
Edge AI is the use of artificial intelligence in an edge computing environment, allowing calculations to take place near where data is generated rather than at a centralized cloud computing facility or an offshore data center.
This localized processing enables devices to make choices in milliseconds without requiring an internet connection or the cloud. Essentially, when the gadget generates data, the algorithms on board may put it to use instantly.
What is edge AI?
Edge AI refers to artificial intelligence algorithms that are executed locally on a physical device, such as a computer, smartphone, or Internet of Things (IoT) device, rather than through a centralized server or cloud-based processing. It is generally seen as the next step in AI since it provides AI power directly to the devices, allowing for quicker processing, reduced latency, and more privacy.
While it might not garner as much attention in the general media as other AI subjects such as deep learning or reinforcement learning, it is gaining traction in a variety of sectors due to its potential to transform how devices work and interact with their surroundings.
As edge computing puts data storage closer to the device, AI algorithms process data produced on the device, whether or not there is an internet connection. This permits data to be analyzed in milliseconds, resulting in real-time feedback. Edge AI enables replies to be sent very quickly. This can be more secure since some critical data never exits the edge.
Statistics of the edge AI market
The worldwide edge AI market was valued at USD 14,787.5 million in 2022 and is predicted to expand to USD 66.47 million by 2023, according to Grand View Research, Inc. research. This fast spread of edge computing is being driven by an increase in demand for IoT-based edge computing services, as well as the inherent benefits of edge AI.
How does Edge AI Technology Work?
Edge AI allows Machine Learning Algorithms to function at the network’s edge, where the data and information necessary to utilize the system are generated, which might be an edge computing device or an Internet of Things device. Edge AI devices use embedded algorithms to collect and process data, as well as monitor behavior. This enables the device to make decisions, resolve difficulties autonomously, and estimate future performance.
Edge AI may work on a variety of hardware, including current central processing units (CPUs), microcontrollers, and cutting-edge neural processing devices. Some of the most common edge computers are made by large technology firms like Qualcomm, NXP, and Intel.
What Are the Benefits of Edge AI?
Real-time Data Processing
Extension of high-performance processing capabilities to the edge, where sensors and IoT devices are deployed, is the main advantage of Edge AI. Artificial intelligence (AI) edge computing allows AI applications to run directly on field devices, evaluating and executing machine learning (ML) and deep learning (DL) tasks on field data. Data processing in the cloud takes seconds. In contrast, edge data processing can take milliseconds or less.
Reduced expenses
Installing devices with pre-loaded algorithms eliminates the need for a complicated, Internet-connected infrastructure, which may be expensive and time-consuming to create and install. Furthermore, without the requirement for enormous volumes of data flowing at all times, the cost of data transfer decreases.
Furthermore, the autonomous nature of edge AI reduces the need for continuous monitoring by data scientists. While human interpretation will always play an important role in deciding the eventual value of data and the innovation it creates, edge AI technologies assume part of that duty, lowering a company’s bottom line.
Security and privacy
Running AI at the edge makes it possible to keep user data private as sending sensitive data via networks exposes it to theft and manipulation. With edge computing, it is feasible to ensure that personal information is never sent from the local device (on-device machine learning). Edge devices can be used to remove personally identifying information before data transfer in situations where data must be handled remotely, improving user security and privacy. I suggest reading our piece on privacy-preserving Deep Learning for Computer Vision if you’d like to learn more about data privacy with AI.
Less latency
Compared to conventional kinds of data processing, which send data to faraway data centers or the cloud for processing, edge AI is more flexible and nimble, providing quicker, localized processing with lower latency than typical cloud computing. Without as many bandwidth and data transmission constraints, faster reaction times can contribute to a better user experience (UX), particularly in wearable and mobile technologies where speed is critical. The capacity to obtain valuable answers, produce insights, and speed transactions in seconds (or less) might result in consumer preference and other competitive benefits.
Edge AI applications
Edge AI applications span a wide range of industries and use cases, leveraging the power of artificial intelligence directly on edge devices. Here are some notable applications:
Autonomous Vehicles:
Edge AI is crucial for the real-time processing of sensor data in autonomous vehicles. It enables quick decision-making for tasks like object detection, lane tracking, and collision avoidance without relying on a constant internet connection.
Smart Surveillance:
Edge AI allows for intelligent video analytics in surveillance systems, enabling features such as facial recognition, object detection, and behavior analysis. This improves the accuracy and efficiency of security monitoring while reducing the need for constant streaming of video data to centralized servers.
Industrial IoT:
In industrial settings, Edge AI enables predictive maintenance, quality control, and process optimization by analyzing sensor data locally on machinery and equipment. This minimizes downtime, improves efficiency, and reduces the risk of equipment failure.
Healthcare:
Edge AI can be used for remote patient monitoring, medical imaging analysis, and personalized healthcare applications. Processing data on wearable devices or medical equipment enables real-time monitoring of vital signs, early detection of health issues, and timely intervention without compromising patient privacy.
Retail:
In retail environments, Edge AI powers applications like inventory management, customer analytics, and personalized shopping experiences. Analyzing data from cameras, sensors, and IoT devices in stores, enables retailers to optimize operations, enhance customer engagement, and improve sales.
Smart Home:
Edge AI is increasingly used in smart home devices such as voice assistants, security cameras, and smart appliances. By processing data locally, these devices can respond quickly to user commands, detect anomalies, and automate tasks without relying on cloud connectivity.
Agriculture:
Edge AI helps farmers optimize crop yield, monitor soil conditions, and manage resources more efficiently. Analyzing data from sensors, drones, and satellite imagery directly on the farm, enables precision agriculture techniques such as targeted irrigation, pest detection, and yield prediction.
Energy Management:
Edge AI is used in smart grid systems to optimize energy distribution, monitor power consumption, and predict equipment failures. Analyzing data from smart meters and sensors at distribution points, helps utilities improve grid reliability, reduce energy waste, and enable dynamic pricing models.
These are just a few examples of how Edge AI is being applied across various industries to improve efficiency, enable new capabilities, and drive innovation at the edge of the network. As technology advances and more powerful edge devices become available, we can expect to see even greater adoption of Edge AI in the future.
How Is Edge AI Shaping the Future of AI?
Edge computing and Edge AI are on their way to becoming critical technologies as a result of the massive increase in data that we are now witnessing. Enterprises are investing heavily in artificial intelligence (AI), even though the global market capitalization of edge computing infrastructure is estimated to exceed $800 billion by 2028.
While many organizations engage in Edge-related technologies as part of their digital transformation process, forward-thinking enterprises and cloud providers see new opportunities by merging Edge computing with AI (Edge AI), cementing Edge AI’s place in the future of Edge computing.
