{"id":79720,"date":"2026-02-25T00:13:06","date_gmt":"2026-02-25T05:13:06","guid":{"rendered":"https:\/\/world.edu\/?p=79720"},"modified":"2026-02-25T00:13:06","modified_gmt":"2026-02-25T05:13:06","slug":"what-is-ai-edge-and-what-is-it-used-for","status":"publish","type":"post","link":"https:\/\/world.edu\/what-is-ai-edge-and-what-is-it-used-for\/","title":{"rendered":"What is “AI edge” and what is it used for?"},"content":{"rendered":"

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To analyze the enormous volumes of data, particularly that generated by the numerous sensors that now populate our lives\u2014from dishwashers to cars, not to mention our phones\u2014we send it to the cloud. To enable faster and more secure calculations, edge\u00a0<\/span>computing<\/span><\/em>\u00a0is developing. Its AI counterpart is\u00a0<\/span>edge AI<\/span><\/em>\u00a0, a way of doing AI without relying on the cloud. An expert explains.<\/span><\/strong><\/p>\n

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Sensors are everywhere we go: in homes, offices, hospitals, transport systems, and farms. They offer the potential to improve\u00a0<\/span>public safety<\/span><\/a>\u00a0and\u00a0<\/span>quality of life<\/span><\/a>\u00a0.<\/span><\/p>\n

The “Internet of Things” (IoT\u00a0<\/span>)<\/span><\/em>\u00a0includes temperature and air quality sensors that aim to improve\u00a0<\/span>indoor comfort<\/span><\/a>\u00a0, wearable sensors to monitor\u00a0<\/span>health<\/span><\/a>\u00a0,\u00a0<\/span>lidars<\/span><\/a>\u00a0and radars to\u00a0<\/span>improve traffic flow,<\/span><\/a>\u00a0and detectors that allow for\u00a0<\/span>rapid intervention in the event of a fire<\/span><\/a>\u00a0.<\/span><\/p>\n

These devices generate enormous volumes of data, which are used to train artificial intelligence models. These models learn a model of the sensor’s operating environment in order to improve its performance.<\/span><\/p>\n

For example, connectivity data from Wi-Fi access points or Bluetooth beacons deployed in large buildings can be analyzed using AI algorithms to identify\u00a0<\/span>occupancy and movement patterns<\/span><\/a>\u00a0at different times of the year and for different types of events, depending on the building type (e.g., office, hospital, or university). These patterns can then be\u00a0<\/span>used to optimize<\/span><\/a>\u00a0heating, ventilation, exhaust systems, and so on.<\/span><\/p>\n

Combining the Internet of Things and artificial intelligence comes with technical challenges.<\/span><\/strong><\/h4>\n

Artificial Intelligence of Things<\/span><\/a>\u00a0(\u00a0AIoT) combines AI and IoT. Its aim is to better optimize and automate interconnected systems, paving the way for intelligent decision-making. Indeed, AIoT systems rely on real-world, large-scale data to improve the accuracy and robustness of their predictions.<\/span><\/p>\n

But to extract information from the data collected by IoT sensors, this data must be collected, processed, and managed effectively.<\/span><\/p>\n

To do this, we generally use “cloud platforms” (e.g., Amazon Web Services, Google Cloud Platform, etc.), which host computationally intensive AI models \u2013 including recent\u00a0<\/span>foundation models<\/span><\/a>\u00a0.<\/span><\/p>\n

What are foundation models?<\/span><\/h3>\n