Recent strides in the efficacy of AI, the adoption of IoT devices and the power of edge computing have come together to unlock the power of edge AI.
This has opened new opportunities for edge AI that were previously unimaginable — from helping radiologists identify pathologies in the hospital, to driving cars down the freeway, to helping us pollinate plants.
Countless analysts and businesses are talking about and implementing edge computing, which traces its origins to the 1990s, when content delivery networks were created to serve web and video content from edge servers deployed close to users.
Today, almost every business has job functions that can benefit from the adoption of edge AI. In fact, edge applications are driving the next wave of AI in ways that improve our lives at home, at work, in school and in transit.
Learn more about what edge AI is, its benefits and how it works, examples of edge AI use cases, and the relationship between edge computing and cloud computing.
What Is Edge AI?
Edge AI is the deployment of AI applications in devices throughout the physical world. It’s called “edge AI” because the AI computation is done near the user at the edge of the network, close to where the data is located, rather than centrally in a cloud computing facility or private data center.
Since the internet has global reach, the edge of the network can connote any location. It can be a retail store, factory, hospital or devices all around us, like traffic lights, autonomous machines and phones.