In the race for automation, industries are looking for ways to improve processes, efficiency, and safety. Hence, computer programs are pushed to recognize the patterns and execute tasks repeatedly and safely. Recent marches in the efficiency of AI, the adoption of IoT devices, and edge computing have unlocked new opportunities for edge AI. Today, almost every business has job functions that can benefit from adopting edge AI.
Understanding What Is Edge AI?
Nowadays, smart devices are everywhere, from our wrists and homes to our cars. They can perform autonomous computing and exchange data with each other – or a concept commonly known as the Internet of Things, IoT. Research organization IDC forecasted that there will be 41.6 billion connected IoT devices generating 79.4 zettabytes of data in 2025. All those data exchanges put a heavy load on the data centers. Hence, edge computers are utilized to move some of the processing power closer to its point of origin (devices).
By extension, edge AI is a combination of edge computing and artificial intelligence. The computer gathers, processes, and understands data based on AI computation at the network's edges rather than the centralized cloud computing facility. This allows secure, real-time decision-making at the edge, and independent of a connection.
How Edge AI Benefits Industry 4.0?
Overall, the edge AI is growing alongside the increasing ubiquity of artificial intelligence. And despite its technological complexities, the ultimate goal of edge AI is to get closer to devices themselves, thus reducing the amount of data that needs to be moved around. That brings several advantages for business:
Reduced latency for real-time analytics:
The widespread adoption of the IoT has fueled the explosion of big data. With the sudden ability to collect data in every aspect of a business, data transfer back and forth from the cloud takes time. Edge AI reduces latency by processing data locally at the device level.
Decentralization and offline capabilities make edge AI more robust since internet access is not always required for every processing data.
Reduced bandwidth requirement and cost:
Processing the data locally on the device reduces the cost of internet bandwidth, energy, networking cost, and cloud storage.
Improved data security:
Edge AI systems process most data locally, uploading only the analysis and insights to the cloud. This greatly reduces the data load sent to the cloud and other external locations and is more secure. And, with improved security comes privacy, which has become increasingly important across the board, especially for IoT devices.
Intelligence and improvement:
Depending on its model, AI applications are more powerful and flexible than conventional applications that respond only to the programmer’s anticipated inputs. AI models grow increasingly accurate as they gain more data. When an edge AI application confronts data that it cannot accurately or confidently process, it typically uploads it so that the AI can retrain and learn from it. The longer a model is in production at the edge, the more accurate the model will be.
What Makes Edge AI Technology Work?
For machines to successfully perform object detection, data processing, understanding, converse with humans, etc., they must functionally emulate human cognition. That is, implement artificial intelligence. Many AI models are powered by machine learning, giving them the ability to learn and optimize processes without having to be specifically programmed to do so. Other models employ neural networks, trained to answer specific questions by being fed many examples of that question, along with correct answers. This training process is known as deep learning.
Edge AI devices use embedded algorithms to monitor the device’s behavior, collect, and process the device data at the edge of a given network. That is, close to where the data and information needed to run the system are generated, such as a machine equipped with an edge computing device or an IoT device. This allows the device to make decisions, automatically correct problems and make future performance predictions.
Edge AI can run on a wide range of hardware. Yet, the current market-available target devices for edge AI are often neither powerful nor durable enough to fully meet the edge's memory, performance, size, and power consumption requirements.
For nearly 30 years, Winmate has been a global leader in developing innovative, durable industrial computing technologies. Built on a foundation of operational excellence and innovative technology, Winmate offers edge computing technologies featuring advanced CPU and graphic performance with integrated IoT features and longevity support. All Winmate technologies are designed to be flexible to ensure seamless enterprise integration at any level. Winmate also offers OEM services to meet our customers’ needs. With our design-build expertise, we can integrate most of the specified hardware modules and sensors into our products to create a solution customized to your industry or application, all while reducing your cost and risk.
Need help determining what edge AI tech is suitable for your business? Or do you want to request a demo sample? Talk to our team and get your questions answered.