Cloud-based artificial intelligence (AI) is not suitable for real-time operations, such as autonomous vehicles or video surveillance. It also does not work for applications of artificial intelligence where personally identifiable information cannot be transmitted to the cloud due to privacy regulations. Similarly, cloud-based AI will not work in remote locations with limited or no internet connectivity.
For example, due to its remote location, an offshore oil rig that generates over 1TB of data every day cannot send that much data over a satellite link because it is too expensive and time-consuming. Applications for off-shore rigs require high availability and low latency and data must be processed locally and acted upon in real-time. Artificial intelligence at the edge enables real-time monitoring for abnormalities and event notification.
As artificial intelligence programming increases, edge computing is becoming a necessity. AI at the edge solves many problems by working locally and in real-time.
Benefits of AI at the Edge
There are multiple benefits to bringing artificial intelligence to the edge. First, it allows for the deployment of AI applications in a variety of settings, such as process control, industrial safety systems, autonomous vehicles, etc. Second, AI at the edge allows for real-time responses in different environments like an immediate response to a smoke or fire alarm. It is more reliable in settings were connecting to the internet is intermittent or impossible. With no internet connection, edge computing can still monitor and enable decisions.
Computational intelligence means nothing if people are scared to use it. Fortunately, AI at the edge has high privacy and user acceptance because consumers and companies do not have to worry about their information being stored in a cloud. Edge computing meets privacy regulations for storing user data locally, such as in-country containment and the EU General Data Protection Regulation (GDPR).
Another important benefit of AI at the edge is low latency, as there is no need to send information to the cloud.
AI at the edge has numerous use cases such as surveillance and monitoring, self-driving cars and expression analysis. For instance, smart cameras with edge AI can detect suspicious activities faster to alert authorities and audio sensors with edge AI can accurately recognize the sound of a gunshot among other city noises. The diversity of these applications shows that edge computing can be useful in multiple fields and industries.
Benefits of Using Pixeom
Pixeom’s software offers cloud functionality on-premise and also makes it easy to deploy and manage geographically distributed edge infrastructures at scale. It can be deployed in small foot-print edge devices all the way up to Xeon class edge servers.
Pixeom’s Edge Cores come with built-in machine learning. With Pixeom, the same infrastructure and services found in any cloud can be deployed on-premise to any hardware, with matching APIs like FaaS, ML, Pub/Sub and others. It allows the deployment of Google TensorFlow micro-services at the edge to enable a number of applications, including object detection, facial recognition, text reading, and intelligent notifications. Plus, there is no vendor lock-in when you choose Pixeom.