Edge AI Technology
Edge AI: What is it?
The direct application of AI models and algorithms to adjacent edge devices, such as sensors or Internet of Things (IoT) devices, is referred to as "edge AI." This makes it possible to handle and analyze data in real time without always relying on cloud infrastructure.
To put it simply, edge AI, sometimes referred to as "AI on the edge," is the use of edge computing and artificial intelligence to machine learning activities that are carried out directly on networked edge devices. While edge computing allows data to be stored close to the device location, AI algorithms allow data to be processed directly on the network edge, with or without an internet connection. This enables real-time feedback provisioning and data analysis in milliseconds.
It is being more widely used in more industries to solve latency, security, and cost-savings while automating corporate processes, streamlining workflows, and fostering creativity.
Edge AI Benefits for Final Users
According to Grand View Research, Inc. (link outside of IBM.com), the global edge AI market is expected to reach USD 66.47 million by 2023 after being valued USD 14,787.5 million in 2022. The rapid spread of edge computing is being driven, not only by the inherent advantages of edge AI, but also by the increasing demand for IoT-based edge computing services. The primary benefits of edge AI are as follows:
Decreased latency
Full on-device processing allows users to take advantage of fast reaction times without waiting for data to return from a distant server.
Decreased bandwidth
By processing data locally, Edge AI reduces the amount of data transferred over the network, preserving internet bandwidth. When less bandwidth is needed, the data link may be able to handle more simultaneous data transmission and reception.
Real-time analytics
By integrating data without requiring users to engage with other locations physically, users may save time by processing data in real-time on devices without requiring system integration or connections. Edge AI could be necessary to manage the vast amount and diversity of data needed by certain AI applications in order to fully use the resources and capabilities of cloud computing.
Data privacy
Privacy is increased since data is not sent to another network where hackers may access it. Edge artificial intelligence reduces the likelihood of data handling mistakes by evaluating data directly on the device. Edge AI might enable businesses that must comply with data sovereignty regulations stay compliant by processing and storing data locally within authorized jurisdictions. However, since attackers may target any centralized database, edge AI is not completely protected from security risks.
The capacity to ascend
AI extends systems via the use of cloud-based platforms and the inherent edge capabilities of original equipment manufacturer (OEM) technologies, including both software and hardware edge capabilities. The system can scale more easily now that these original equipment manufacturers (OEMs) have begun to include native edge capabilities into their devices. Furthermore, this enhancement allows local networks to function even in the event that nodes downstream or upstream experience unavailability.
Reduced costs
Cloud-based AI services might be quite expensive. With Edge AI, it is possible to employ pricey cloud resources as a repository for post-processing data collection, with the intention of utilizing the data for analysis down the road rather than for real-time field operations. As a consequence, cloud computers’ and networks’ workloads are lighter. Edge AI is the most cost-effective option since it significantly reduces the use of CPU, GPU, and memory when the workloads are distributed across edge devices.
A significant amount of work is placed on the central location when cloud computing is used for a service's whole computation. Networks need to be able to handle a lot of traffic in order to convey data to the central source. Networks come back online when computers finish their jobs and return information to the user. Edge devices do away with this back-and-forth data transfer. Consequently, networks and robots experience less stress when they are relieved of the burden of overseeing every aspect.
Moreover, the autonomous capabilities of edge AI eliminate the need for data scientists to continuously supervise operations. While human interpretation will always be essential to determining the ultimate value of data and the outcomes it generates, Edge AI technology relieves some of this load, resulting in cost savings for businesses.
What Is the Process of Edge AI Operation?
Edge AI trains models that accurately detect, classify, and describe objects in the given data using neural networks and deep learning. To manage the massive volume of data needed for model training, a centralized data center or the cloud are often used in this training process.
After being deployed, edge AI models improve over time. When an AI problem occurs, the problematic data is often uploaded to the cloud so that further training of the original AI model may take place. In the end, the inference engine at the edge is replaced by the cloud-based AI model. This feedback loop is essential to raising the model's performance.
Use Cases for Edge AI
Sector-specific applications of Edge AI
Current examples of edge AI include real-time traffic updates on self-driving vehicles, connected devices, wearable health monitoring accessories (like smart watches), and smart appliances. Furthermore, a lot of industries are using Edge AI apps more often in an attempt to optimize operations, save costs, automate processes, and improve decision-making.
Medical Care
Healthcare providers are seeing a major change due to the introduction of cutting-edge devices and the practical use of Edge AI. When combined with state-of-the-art advancements, this technology offers the ability to build more intelligent healthcare systems while safeguarding patient privacy and accelerating response times.
Wearable health monitors use locally integrated AI algorithms to measure metrics including blood pressure, heart rate, glucose levels, and respiration. Furthermore, a number of well-known smartwatches on the market now come with wearable edge AI devices that may detect when a patient falls suddenly and alert caretakers.
Rapid data processing systems installed in emergency vehicles allow paramedics to communicate with physicians and get information from health monitoring devices in order to develop effective patient stabilization programs. Employees in the emergency room may prepare in parallel to meet the unique demands of each patient. In these cases, using edge AI will help to facilitate the exchange of critical health data in real-time.
Producing
Global manufacturers have begun transforming their manufacturing processes using Edge AI technology, which has resulted in an increase in efficiency and productivity.
Sensor data may be used to provide predictive maintenance, also known as proactive abnormality detection and machine issue predictions. Equipment sensors detect defects and promptly notify management of required repairs, enabling prompt resolution and reducing downtime.
In this industry, edge AI may also enhance supply chain analytics, yield optimization, worker safety, quality control, and floor optimization.
Retail
eCommerce and online shopping have benefited companies. Establishment physical shops need to be creative to attract customers and improve the shopping experience. This trend has given rise to "pick-and-go" companies, sensor-equipped shopping carts, and smart checkouts. By using Edge AI technology, these solutions enhance and expedite the conventional in-store experience for customers.
Smart Homes
These days, "smart" appliances include thermostats, doorbells, refrigerators, entertainment systems, and lightbulbs. Smart homes use ecosystems of connected devices and edge AI to improve the lives of its occupants. Edge AI technology can swiftly evaluate data on-site, eliminating the need to transfer it to a centralized remote server. This enables homeowners to do activities like adjusting the temperature of their house or confirming who is at their door. This enhances resident privacy and lessens the likelihood of unauthorized access to personal data.
Protection and Monitoring
In the realm of security video analytics, velocity is paramount. Since many computer vision systems lack the speed required for real-time analysis, security camera images and videos are sent to a cloud-based device with high-performance processing capability for analysis rather than being processed locally. If the data is not processed locally, latency issues, which are characterized by delays in data uploading and processing, present difficulties for these cloud-based systems.
By using computer vision apps and Edge AI technology, smart security devices can identify suspicious activity, notify users, and trigger alarms. These characteristics provide the residents a greater sense of security and comfort.
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