Edge AI is no longer in the blueprint phase. It has well entered into mainstream adoption, growing at a phenomenal rate but what really is Edge AI?
Edge is loved by enterprises worldwide, being a new age sensing technology, it has a huge ability to observe users in real-time to gain greater awareness for taking intelligent powerful actions. The debatable question remains, does Edge AI really exist? Experts say, yes! For instance, take your smartphone which has the ability to unlock your phone in a fraction of seconds simply by registering and recognizing your face. Self-driving cars, is another complex example where the car drives on its own without any human intervention. Data is right there in your car or in your phone, there is no time to send this data into the cloud and wait for insights.
Edge AI in the Industry
There are numerous other instances where we have been using Edge AI, both at enterprise and in personal level. From Google maps alarming you about the traffic conditions to speech to text algorithms, smart AI is everywhere. Edge AI holds tremendous potential, as per a report by Tractica, AI edge device shipments will set to increase from 161.4 million units in 2018 to 2.6 billion units by 2025. The popular AI powered edge devices include head-mounted displays, smart speakers, mobile phones, PCs/tablets, automotive sensors, robots, security cameras and drones. In addition, wearable health sensors will see a high adoptability.
Edge AI will most likely benefit industrial-heavy applications that includes supply chain and manufacturing lines. Particularly in the Industrial Internet of Things (IIoT), enterprises will see a more tangible RoI. For instance, manufacturing industries could use edge AI for predictive maintenance, troubleshooting and identifying issues within a complex physical system. Besides, Edge AI could also be used to automate product testing and inspection to increase the quality while reducing resource expenditure.
Harnessing Deep Learning and Edge AI
Another application where edge AI finds its application is the deep learning-enabled smart cameras which can process captured images to track multiple objects and people. Detecting suspicious activities directly on the edge node and not relying on the cloud which can at times prove to be time consuming. Smart cameras can minimize communication with the remote servers by streaming data aimed at the triggering even. This can also reduce the remote processing and memory requirements. The most talked about applications of deep learning and Edge AI include the intruder monitoring systems to secure homes against any intervention. This holds vitally important to safeguard homes and monitor elderly people.
Text to Speech (TTS) and Speech to Text (STT) are two examples which leverage the applications of AI and DL to bring the functionalities on the Edge. Examples include hands-free text read and write functions in automotive, where the driver can keep attention on driving the car while interacting with the infotainment system simultaneously.
With the shifting of AI on the edge, brace up for a number of changes underway. These tectonic shifts include the emergence of 5G networks, smart devices etc and the growth and demand for IoT devices. With the rapidly evolving future, enterprises will increasingly make their systems ‘smart ‘which means the market will make significant gains to keep up with the computing needs of the smart Edge AI platforms.