Technology is gaining momentum at a fanatic pace but with consequences. Once you get to know a new technology, the decision to adopt it becomes the next challenge in-line. Unfortunately, most of us get into a dilemma at this point. Artificial Intelligence, Machine Learning, Edge Computing are one of those techs where the decision to change never comes lightly. What is Edge Computing? You might think of the term as an emerging buzzword but what if I say the tech is not new. Much like artificial intelligence invented in the 1950s but took ample years to bring to practice, edge computing has been in talks since at least the 90s. And since we are dealing with computationally intensive and process massive amounts of data day in day out, we have reached a place where computing at the edge is a must-have. With more and more AI systems running in data centers that are miles away from places where the action takes place. Several devices like smartphones, self-driving cars, and drones are found relying on these brains in the cloud to perform the necessary complex computations. In simple words, edge computing is where you generate, collect and analyze data on edge- the cloud, a place where data is generated instead of being in centralized servers and systems. Why it matters the most? Much to surprise, clouds have reached the earth. And smart devices are found taking some of the processing workloads. But the question is can’t these clouds handle everything why there is a need for edge computing?

#1 Internet of Things (IoT) is the Edge

#2 The network

#3 Cloud & Edge

AI in Edge Computing

1. It is highly responsive- Unlike any typical centralized IoT model deployed to date, edge-based AI ones are highly responsive and closer to real-time. Here you can receive insights on the instant basis and processed, most likely within the same hardware or devices.
2. It ensures greater security- Do you know that sending data back and forth with Internet-connected devices might result in data to tampering and exposure? Whether you are aware of it or not this can definitely worsen the situation. Processing at the edge not only minimizes this risk but even these edge-based AI-powered devices can offer something beyond such as enhanced security features.
3. It is highly flexible- Smart devices support the development of industry-specific or location-specific requirements, from building energy management to medical monitoring.
4. It doesn’t require a Ph.D. to operate- It may quite interest you to know that they can be self-contained, this means gone are the days when devices required data scientists or professionals to monitor or maintain at regular intervals. With AI-based edge, everything is changed! In case, if anyone wants insights they can be delivered when needed, or visible on the spot through highly graphical interfaces or dashboards.
5. It provides for superior customer experiences- Do you know that AI helps companies build trust and rapport with their customers? Yes, it does! All you require to do is enable responsiveness through location-aware services, or rerouting travel plans in the event of delays.

A Few AI Edge Computing Use Cases to Consider

1. Consumer IoT

2. Industrial IoT

3. Embedded computing and robotics

4. Smart Grids

5. Intelligent Traffic Management

What the future holds?