Edge AI refers to AI technology that is implemented at the network's edge, generally near where data is collected. This form of deployment enables real-time data processing and can be used to serve low-latency applications such as industrial IoT, autonomous vehicles, and smart cities.
Edge AI is well suited for IoT applications because it can process data locally, without sending it to the cloud. This reduces bandwidth requirements and can improve privacy since data never leaves the device.
What are the benefits of Edge AI?
Edge AI can be used for a variety of applications, including object recognition, image classification, facial recognition, and anomaly detection. It can also be used to process sensor data from IoT devices to detect patterns and trends. Edge AI can reduce latency and improve responsiveness by bringing the AI closer to the data. Another benefit is that it can reduce bandwidth requirements by processing data locally instead of sending it back to a centralized server. And finally, edge AI can improve security by keeping sensitive data local and reducing the attack surface.
Edge AI can provide the real-time processing power needed to support applications that require low latency. For example, in the industrial IoT, edge AI can be used for condition monitoring and predictive maintenance. In autonomous vehicles, edge AI can be used for object detection and classification. Edge AI can operate offline, which is important for applications that need to function even when there is no network connection. Finally, edge AI can improve privacy and security, since data never has to leave the sensor device.
What can Edge AI do for Manufacturing?
Edge AI is particularly well-suited for applications in manufacturing, where real-time data from sensors and other IoT devices are used to make decisions about production processes. For example, edge AI can be used to monitor production line equipment for faults, and to automatically adjust production settings to improve quality or efficiency.
Several auto manufacturers, such as Tesla and Google use sensors to collect information from sensors on their vehicles, which are then transmitted to the cloud for processing and machine learning. Microsoft has been actively developing and testing new AI capabilities on its Azure cloud platform.
Amazon already uses AWS IoT Edge to power a series of products that detect objects, detect movement and monitor plants in outdoor settings, etc. Amazon has also developed a software development kit (SDK) for edge devices and processes that lets developers build Internet of Things applications using their connected sensors and devices.
Apple’s Sense ID is a technology that can unlock devices using face recognition. The company has also unveiled a range of HomePod smart speakers and wireless headphones designed to work with its Siri voice assistant. Google, meanwhile, has worked with chipmaker Qualcomm on Project Jacquard to turn smartwatches into touch interfaces. Google also worked with Levi's to design Levi's Commuter Trucker Jacket, which has touch-sensitive buttons, displays, and an inductive charger.
What are the benefits of Edge AI in Healthcare?
Healthcare providers and patients alike can find it to be beneficial. Here are some compelling reasons why Edge AI is the obvious choice for the healthcare industry.
Internet of Things (IoT) devices can provide real-time data to healthcare providers. It can help them to deliver personalized healthcare at a lower cost and improve patient outcomes. In the healthcare industry, this means that data can be analyzed in real-time, with the focus being on preventative measures. For example, IoT devices can detect a spike in the body temperature of a patient, which could indicate an oncoming infection.
AI algorithms can also be used to identify skin conditions and anomalies. Recently, Singapore's Ministry of Health announced that it has launched an AI-powered app for skin screening. The data is automatically collected in the background, and it is believed that this app will help save the lives of over 10,000 patients every year in Singapore.
Improving patient care
AI systems can be used to process complex medical data faster and, hence, provide faster treatment to patients. This is possible through AI's ability to interpret large volumes of unstructured data.
AI and ML can be used to enhance the efficiency of data storage. The traditional way of storing data with batch processing, which can take days, is replaced by instantaneous processing of medical images.
Data can be processed in near-real-time to generate better predictive insights. This is possible because AI can analyze and analyze vast amounts of unstructured data, and for example, this can be used to predict the likelihood of developing a heart attack.
Finally, the biggest benefit of Edge AI is that it can help to deliver personalized and more relevant healthcare. Personalization helps deliver better-tailored healthcare.
Conclusion - What will Edge AI do for your business?
Edge AI is a term for artificial intelligence (AI) algorithms that are run on edge devices, such as sensors, embedded systems, and other devices that are located at the edge of a network. These devices are often connected to the Internet of Things (IoT), and they collect and process data from the physical world. Edge AI can reduce latency, use less bandwidth, and enhance security.
Edge AI allows us to process data in real time. This makes our devices more productive. When processing IoT data, for instance, machine learning can be used to classify data, determine the context, and predict what can happen next. It can also be used to discover patterns in data and learn new behaviors.