Edge computing is changing the landscape of artificial intelligence (AI). Unlike traditional cloud computing, which relies on central data centers, edge computing processes data closer to its source. This shift is leading to faster responses, reduced bandwidth usage, and improved efficiency for various applications. \n\nConsider a smart city where traffic signals are connected to sensors. These sensors collect vast amounts of data on traffic patterns. With edge computing, data can be analyzed right at the intersection, allowing for real-time adjustments. Instead of sending this data to the cloud and back, decisions can be made instantly, leading to smoother traffic flow. \n\nMoreover, edge computing enhances privacy and security. By keeping sensitive data closer to the source, there's less risk of it being intercepted during transfer. For businesses, this means they can protect customer information more effectively. \n\nApplications of edge computing in AI are varied. In healthcare, for instance, wearable devices can process health data on the spot, alerting medical professionals if there are anomalies. In manufacturing, machinery can monitor performance metrics in real time, increasing productivity and reducing downtime. \n\nAs we move forward, the combination of AI and edge computing is likely to create exciting innovations. With advancements in IoT (Internet of Things) devices, the potential for smarter homes, cities, and industries is becoming a reality. This technology reduces latency, making AI applications more responsive and efficient, ultimately leading to better user experiences. \n\nIn summary, edge computing represents a significant trend within AI technologies. By processing data closer to its source, we can achieve faster responses, better security, and more efficient applications across various sectors. Keep an eye on this trend, as it will undoubtedly shape the future of technology.