Traditional machine learning models are specifically cloud-based, allowing the Machine Learning and AI (Artificial Intelligence) models to be initiated and trained over a cloud architecture. The predictive model is developed on the device which sends the request to cloud-based models and receives the response at the end device through cloud APIs over a network connection. This data transmission causes latency over larger datasets and is bound to an internet connection. Edge Artificial Intelligence, however, refers to processing such ML and AI algorithms and processing on edge devices locally with the lowest possible latency, putting and cloud usage to a limit.
Technological innovations over the past few years have led to overwhelming development in the domain of the Internet of Things (IoT) and other cloud-based technologies. Autonomous vehicles, augmented reality, IoT devices and other intelligent mobile devices need data processing, caching, and storage in real-time. This demands a large volume of data to be processed at the nearest possible location to the user i.e., at the edge, to overcome the latency and bandwidth problems. Intelligent devices are traditionally based on cloud computation and such a large amount of data processing consumes an immense amount of bandwidth resulting in higher latency to the end-user. To overcome such problems, Artificial Intelligence at the Edge presents the truly efficient way.
The excessive intelligent innovations, most importantly IoT-based technologies, are supposed to be more precise and responsive, and deep learning and machine learning algorithms comprised with edge computation, make it achievable. IoT devices, facial recognition, semi, and fully automated vehicles, etc., require real-time request/response data computation to function in real-time. AI at the edge allows inferences to operate at the edge in real-time which might be cached and processed at the edge device, aiding the end-user with real-time responsiveness without the need for network connectivity. Whereas cloud computation is necessary to compute along with edge AI as these devices are not scaled to process big data solely. So, an Edge-cloud system is considered, where the processed data is uploaded to the cloud via edge nodes.
Edge AI is invaluable for Computer Vision-based applications where large datasets, including videos and images, along with Natural Language Processing (NLP), require real-time data processing. The practical applications of Edge AI and federated learning technologies include IoT sensors such as developing smart environments to be utilized in industry and homes, and autonomous vehicles such as Tesla cars that implement Edge AI to perform semantic segmentation and object detection in automated vehicles in real-time, and other applications in healthcare sectors like high precision thermal scanners, etc.
Edge AI provides leverage over cost-effectiveness, as edge-cloud systems allow reduced bandwidths obtained by the devices due to their availability at the edge, which ultimately reduces data transmission and network costs. Secondly, they offer data security and privacy, as Edge AI secures personal data due to remote data processing at the edge. It discards personally identifiable information before transmission, aiding in security and privacy protection. Thirdly, it delivers highly available AI modules due to data decentralization and limited network usage, making Edge AI important for technology-oriented industries.
Edge Artificial Intelligence is slowly becoming a necessity as it has many applications in the real world. In the era of automation, the usability of Edge computation combined with ML/DL algorithms is in great demand. Considering this, most artificial intelligence-based computation is already heading towards the edge and increasing technological innovations simultaneously. It increases the trends of edge computations, becoming a crucial part in terms of automation and growing AI developments.
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