What is Edge Intelligence and Edge AI?
The combination of Edge Computing and AI has given rise to a new area of research called “Edge Intelligence” or “Edge AI”. Edge Intelligence makes use of pervasive edge resources to power artificial intelligence applications without relying entirely on the cloud.
While the term Edge AI or Edge Intelligence is completely new, practices have started early.
However, despite the early start of exploration, there is still no formal definition of edge intelligence.
Currently, most organizations and printers refer to Edge Intelligence as “the paradigm of running AI algorithms locally on an end device, with data (sensor or signal data) being created on the device.”
Here’s a curious fact…
Several major companies and technology leaders, including Google, Microsoft, IBM and Intel, demonstrated the benefits of edge computing. Their efforts include a wide range of artificial intelligence applications:
- Real-time video analysis
- Cognitive assistance
- Precision agriculture
- Smart home
- Industrial IoT.
Advantages of pushing deep learning to the edge
Thanks to the viso.ai article we were able to learn that the merger of artificial intelligence and edge computing is natural, as there is a clear intersection between them. Data generated at the edge of the network relies on artificial intelligence to fully unlock its potential and edge computing can thrive with richer application and data scenarios.
The advantages of implementing deep learning at the perimeter include:
- Low latency: Deep learning services are deployed close to the requesting users. This significantly reduces latency and the cost of sending data to the cloud for processing.
- Privacy preservation: privacy is enhanced as the raw data needed for deep learning services is stored locally rather than in the cloud.
- Increased reliability: decentralized, hierarchical computing architecture provides more reliable deep learning computing.
- Scalable deep learning: with richer application and data scenarios, edge computing can promote the application of deep learning and drive the adoption of artificial intelligence.
- Commercialization: diversified and valuable deep learning services extend the commercial value of edge computing and accelerate its deployment and growth.
Technology has always advanced by leaps and bounds. With the emergence of both Artificial Intelligence and IoT, the need arises to push the frontier of the former from the cloud to the Edge Computing device.
Edge computing has been a widely recognized solution to support compute-intensive machine vision and artificial intelligence applications in resource-constrained environments.
We believe that keeping up with technology will not only provide us with many benefits. This favors the top of mind of traditional sectors such as insurance, which has been one of the last to acquire technology.
Therefore, our promise has always been to pursue the technology of insurers with artificial intelligence, machine learning, deep learning, big data, the cloud, among others.