During the last decades, we have seen how data is migrating, first from on-premises to cloud data centers and now, from cloud to the “edge” points closer to the source, where it’s being generated.
What is Edge?
Edge or Network Edge is where the data resides and is collected. Scalability issues, excessive power consumption, connectivity and latency are some of the many factors that are driving the demand for edge infrastructure in the form of micro data centers or distributed computing architecture.
A recent study finds that the edge computing market is expected to value at USD 3.24 billion by the year 2025.
Few factors that are driving the growth of edge computing include:
- Real-time customer engagement: Edge lets you connect in real-time with your customer, no matter what their location is. This is evident with the changing customer demands, where they want to simply tap their Apple watch to make payments or ask Siri or Alexa to brief them with to-do-list of the day or to track their exercise, sleeping and eating patterns.
- Sensor data analysis and IoT: While interacting with an IoT enabled equipment, the data from the sensors or similar IoT devices need to be analyzed and processed in milliseconds, without having to send them to an off-shore cloud location.
The unprecedented growth in edge computing will pave the way for a host of services and applications at the edge, that can be optimized with the application of artificial intelligence.
While cutting-edge companies like Amazon, Apple and Tesla are already betting big on Edge AI, other companies are yet to embrace it fully.
What is Edge AI, and does it really exist?
First, we saw the shift from mainframe to computers to cloud, now, the cloud is moving to Edge, and so is AI. But, it doesn’t imply that cloud is becoming irrelevant. No, it is relevant, in fact, disruptive technologies like IoT will act as the smart extensions of cloud computing. And now, AI on Edge, can offer a whole lot of new possibilities.
In Edge AI, the AI algorithms are processed locally on a hardware device, without requiring any connection. It uses data that is generated from the device and processes it to give real-time insights in less than few milliseconds.
Coming back to our question – Does Edge AI really exist? Yes, definitely. Say for an example, your iPhone has the ability to register and recognize your face to unlock your phone in fractions of seconds. A more complex example would include self-driving cars, where the car drives on its own. In both the examples, we see that complex algorithm are used to process data right there in the car or in your phone, because there’s no time sending this data to the cloud, process it and wait for the insights.
There are numerous other examples where we are un-knowingly using Edge AI. From Google maps alarming you about bad traffic to your smart refrigerator reminding you to buy some missing dairy stuff, AI is everywhere around us.
The potential of Edge AI is vast. As per a report by Tractica, AI edge device shipments are expected to increase from 161.4 million units in 2018 to 2.6 billion units by 2025. Here, the top AI-enabled edge devices include smart speakers, mobile phones, head-mounted displays, PCs/tablets, automotive sensors, robots, drones and security cameras. Wearable health sensors will also see an increased application of AI.
Advantages of AI-enabled decision making at edge:
- Highly responsive: Edge AI-enabled devices process data really fast as compared to centralized IoT models.
- Greater security: With a processing time of less than few milliseconds, the risk of data getting tampered during transit is very less. These devices also include enhanced security features.
- Greater customer experiences: Edge AI gives a solution to one of the most prevailing problems – latency. Low latency and real-time insights help in building great customer experiences.
In an interview at Edge AI Summit, fellow VP and CTO Watson, IBM, said that traditional IoT devices are getting powerful. New technologies like 5G networks are enabling new possibilities. With all this, the consumers are getting more demanding in terms of data protection and improved experiences. This, in turn, is driving the need to move AI and other analytics closer to where data is created, and user experience is delivered.
Edge AI Summit took place last year in the month of December at San Francisco, USA. The event discussed the challenges and opportunities of distributed intelligence and explored the newly found AI adoption across the globe.
With AI on the edge, we can expect a lot of changes underway. This includes growth and demand for IoT devices, the emergence of 5G networks, smart devices etc. As the companies increasingly make their systems ‘smart’, the market will make significant gains to keep up with the computing needs of these smart platforms.
Coming back to the edge, it is no longer in a proof-of-concept phase. Entering in the mainstream adoption, the edge will grow at a CAGR of 41%. Businesses will keep adopting edge strategies to make their operations better and enable real-time performance of their networks.
The Edge AI concept is much wider and will drive customer experience in coming times. This is why companies are including edge in their technology roadmaps for 2019.