It’s 2022 and Artificial Intelligence (AI) is everywhere. From our phones, watches, cars to speakers, we see AI breaking the shackles of conventional technologies. It makes it possible for machines to learn from experience and adjust to new inputs and perform human-like tasks with more precision.
AI is conventionally hosted on cloud and the devices are powered to draw the juice from AI run on cloud. While this has worked till now, the increased data volumes, advanced algorithms, required computing power and elongated storage is pushing the developers and manufacturers to achieve more.
With challenges like latency, higher operational costs, dependency on connectivity and need of higher processing power on hardware, developers and companies realized that they needed a way to locally process some of the functions that have been done on cloud. This would cater to all the above challenges and also provide a power up to achieve higher precision and performance.
Hence, Edge Computing was born. It is a distributed computing paradigm that brings computation and data storage closer to the devices where it’s being gathered. And Edge AI or Ai on Edge is basically having AI in the Edge where faster, better and more computation can be done locally enabling smart capabilities on the devices itself.
Imagine scenarios where:
- Your surveillance camera can recognise the friendly faces and based on the movement determine threats and burglars to take necessary action.
- Your car can detect sudden obstacles and take necessary actions to avoid accidents and save lives.
- Your phone can understand your voice commands better and give faster responses.
- Machines can understand the lags by learning the ergonomics and help in improving the manufacturing process.
The applications of Edge AI can be very wide and diverse in the near future. In this post, we will discuss some of the traits of Edge AI that can change the technology as we know today and fast track the development exponentially.
1. Address Latency
With the internet traffic growing exponentially, the network congestions are natural and the device experiences latency. Sometimes this might be very noticeable and take long times for response. With Edge AI in place, this latency can be addressed at a faster, more swifter way.
For example, a surveillance camera which uploads an average of over 5GB every hour might not adequately be able to provide actionable responses instantaneously considering the amount of data to be analysed and ramification to be provided. Edge AI can help in processing this data and provide ramification instantly because it will be able to compute locally.
2. Accelerate Performance
The ability of AI outwits the conventional computation. With the raising demand and user practices, the need for better processing and easier computation is in the play today. Edge AI can help in accelerating the performance of the devices without compromising on the accuracy.
For example, a smartphone that can run perform 100 tasks simultaneously while connected to internet will be able to do a multitude of tasks higher than previous scenario with Edge AI.
3. Tighten Security
With growing number of users, devices and manufacturers, it becomes very difficult to keep up with all the security traits being connected to the cloud and saving all sensitive data there. With Edge AI, all these sensitive data can be protected by encrypting them on the local device.
For example, consider all your credit card data that is stored in the database of your favorite shopping site being encrypted and stored in your mobile device and nobody has access to it. This will tighten the security and provide a layer of protection from being exposed to any threat or hacker.
4. Lesser Bandwidth
With the usage of digital media and content radically growing everyday, the bandwidth needed to get them delivered on multiple devices and keep them synced increases exponentially. Edge AI can deliver the need and address this by only calling out to the cloud when its needed and process other computing locally.
For example, if recently used application data is stored in the device locally and delivered when it is needed, the application will not have to call it out from the cloud every time which will save a lot of data and bandwidth.
AI on Edge or the Edge AI can improve our lives drastically and bring in a new reformations to the way our technology works today. AI at the cloud can process the edge insights to extract lessons from larger data sets. Over a longer period of time, these lessons can help organizations plan out their operations in an effective manner.
According to Research & Markets, the Edge AI industry will grow to US$1,954.244 million in 2026 from US$569.194 million in 2019. Edge AI will allow us to access the untapped power of the devices, unleash the potential impact of AI, and establish its usage in true sense.
If you are not already in the race of Edge AI adoption, it’s time to power up. Get started today with ENAP Studio. It is a one-stop Edge AI Solution that can help you train, optimize & deploy AI Models on all major Edge Hardware.
Sign up for a free beta access – https://edgeneural.ai/beta-access/
