October 18, 2022Artificial Intelligence
Edge AI is the future of artificial intelligence. This blog post will explore what Edge AI is and why it could be such an important part of our lives moving forward.
What is Edge AI?
Edge AI is a new trend in machine learning and artificial intelligence that processes data generated by a hardware device at the local level using Machine Learning algorithms. It is a revolutionary new concept that combines AI and Edge computing. Edge computing puts computation and data storage as close to the point of request as possible to deliver low latency and reduce bandwidth usage. AI benefits from the Edge’s low latency and minimized bandwidth because it can run directly at the Edge, and data processing can happen directly onboard IoT devices rather than in a central cloud or private data center.
It uses data from the edge of the network to make real-time decisions. As more devices are connected to the internet, there’s an increasing demand for efficient ways to analyze data from these devices, which can have security implications if they aren’t handled properly.
Advantages of Edge AI
- Reduces latency: With Edge AI, the systems can send data to the cloud without having to wait for it to arrive at the device. This reduces lag and improves performance by up to 10 times.
- Improves performance: Using Edge AI increases the processing speed in both hardware and software, resulting in better user experiences that are more responsive than traditional approaches like real-time analytics or heavy machine learning models.
- Reduces cost: By operating on multiple levels at once, Edge AI allows companies to build systems faster while keeping costs down because they don’t need special hardware dedicated just for processing data from IoT devices
- Enhances data security: One of the most significant benefits of this technology is that transfer of data to and from external sources (e.g. cloud servers) is reduced. Fewer open connections mean fewer opportunities for hackers, which keeps devices safe without centralized storage. Since data isn’t stored in one location, the consequences are less severe if a single device is hacked.
- Improves reliability: Edge AI and edge computing leverage the distributed nature of the network. In other words, even if the centralized cluster or computer becomes unavailable, individual edge devices can maintain their functions. This is especially important for critical IoT applications in healthcare.
Applications of Edge AI
Edge AI is a technology that allows for the processing of massive amounts of data, enabling it to perform complex tasks at an unprecedented scale. Here are some examples:
- Retail: A positive customer shopping experience is crucial for all retailers because it determines customer retention which can be fulfilled by using analytical insights powered by artificial intelligence. A few of the numerous Edge AI applications in retail include:-
– assisting employees in their daily operations
– improving customer experiences
– determining whether products need to be replaced or replenished (mostly applicable to perishable goods)
Computer vision uses digital images obtained from videos and cameras in smart checkout systems to identify objects, classify them, and make deductions that will be used to react in an environment to ensure that the items identified by bar codes are the exact ones that are being scanned. By using video analytics, retailers can understand the preferences of their customers and make changes to their store layout for a more improved customer experience, analyze anonymized data from videos to understand customer shopping habits, perform data analysis of customer purchases, and so on.
- Healthcare: Artificial intelligence and edge computing in medicine will help to facilitate and promote patient care while also improving operational efficiency. Edge AI applications also aid in data security, which is required for smart hospitals to function effectively. Edge AI can be used by businesses to perform medical tasks such as high-precision thermal screening, inventory management, remote monitoring of patients, and even disease prediction. Smart hospitals could become the norm with the use of Edge AI applications.
- Telecommunications: Telecommunications companies stand to benefit greatly from the integration of Edge AI applications into their services, as it will undoubtedly provide new and unique experiences in 5G services with cameras and sensors for quality control/quality assurance inspection, in the way software-defined networks are used to automate self-checkout procedures in stores, and in consumer experiences powered by artificial intelligence. All of this will combine to provide network providers with new high-value revenue streams.
- Autonomous vehicles: Edge AI can be used in autonomous cars, allowing them to detect and avoid obstacles on their own without human intervention. This could mean the difference between a car driving itself over a cliff or making it home safely after hitting an animal crossing its path.
Edge AI Vs Federated Learning
Edge AI is a distributed model that uses edge devices and the cloud to learn from. Edge devices are usually IoT devices, like cameras or sensors, which communicate with the cloud through encrypted channels. The cloud then learns about these objects as they move around and provide updates to other nodes in real-time.
Federated learning is also based on edge devices but instead of using them directly, it uses them as part of an infrastructure that includes centralized servers with access to more resources than just one device can handle (such as processing power). This means that federated learning can be useful in large-scale deployments where there isn’t enough computing power available on each individual device – especially if you’re talking about self-driving cars.
Edge AI is the most powerful form of artificial intelligence and machine learning. The future of data center, edge processing, and machine learning are all based on edge computing. Combined with other recent advances such as cloud computing, big data analytics, 3D printing, and robotics — which all work together to create powerful new tools for solving real-world problems — it becomes clear why so much attention has been focused on edge AI over the past few years.
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