In recent years, there has been a growing trend towards the deployment of artificial intelligence (AI) at the edge of the network. This is known as edge AI.
Edge AI is a distributed computing paradigm that brings AI processing closer to the source of data. This is in contrast to traditional cloud-based AI, where data is sent to a central server for processing.
There are a number of advantages to using edge AI. First, edge AI can reduce latency. This is because data does not have to travel to a central server for processing. This can be critical for applications that require real-time responses, such as autonomous vehicles and industrial automation.
Second, edge AI can improve privacy. This is because data is not stored on a central server. This can be important for applications that deal with sensitive data, such as healthcare and financial services.
Third, edge AI can reduce bandwidth costs. This is because only the processed data is sent to a central server. This can be significant for applications that generate large amounts of data, such as video surveillance and IoT sensors.
The rise of edge AI is being driven by a number of factors. First, the increasing availability of low-power AI processors is making it possible to deploy AI on edge devices. Second, the growing demand for real-time AI applications is creating a need for edge AI. Third, the increasing concerns about data privacy are driving the adoption of edge AI.
Edge AI is a rapidly growing field with a wide range of potential applications. Some of the most promising applications of edge AI include:
- Autonomous vehicles
- Industrial automation
- Healthcare
- Smart cities
The rise of edge AI is a significant development in the field of artificial intelligence. Edge AI has the potential to revolutionize a wide range of industries and applications. As the technology continues to mature, we can expect to see even more innovative and groundbreaking uses for edge AI.