Year 2021 saw massive growth in the demand for edge computing and Edge AI — driven by the pandemic, the need for more efficient business processes, as well as key advances in the Internet of Things, 5G and AI itself.
The growth of Edge AI has exponentially increased last year enabling many developers, companies and manufacturers to break the barriers and advance in edge computing. In a study by IBM, 94% of the surveyed developers said that they will adopt Edge AI in the next 5 years.
The Edge AI market is predicted to grow from $355 Million in 2018 up to $1,120,000 Million by 2023. This surge opens a huge market and also possibilities to serve customers and users better.
What is Edge AI?
Edge AI essentially means AI models are processed locally, on devices at the edge of a network and not a cloud. In contrast to other AI processes that are carried out via cloud-based data centers, Edge AI hosts the AI model on the edge device.
So, in a nutshell, Edge AI enables abilities in devices to run data processing and data analytics locally on the device using data that they create so that they can make decisions in the form of output without the need to take the data to the cloud each time.
Why Edge AI over Cloud?
Cloud is a revolutionary tech that broke many barriers in computing. While it brings a lot on the table, in recent times due to the increasing number of devices and users, it has its fair share of limitations including latency issues, reduced performance, constrained data processing, etc.
AI Developers and manufacturers realized that there needs to be a solution to handle this and deliver better experiences. That gave birth to Edge Computing and then to AI on Edge. Edge AI brings a more sophisticated system that offers superior scalability and flexibility. With Edge AI, the devices have an optimized infrastructure for the edge devices that can handle more AI workloads.
Many businesses are already reaping the benefits of Edge AI. From improving the vision tech for surveillance cameras to driving autonomous vehicles, Edge AI can benefit various industry verticals.
Some of the benefits of Edge AI include:
- Reduces costs and latency times for an improved user experience. This enables AI developers to build faster, efficient and more productive applications for different devices and hardware like smartphones, smartwatches, drones, surveillance cameras, etc.
- Increases the level of security in terms of data privacy through local processing. This will help manufacturers and developers to build more secure devices where all the sensitive data can be stored locally instead of centralized systems.
- Reduced costs due to conservative usage of bandwidth. Since the data is stored locally, the applications can be built where they do not need to interact with the cloud and have network connectivity all the time.
Why is Edge AI important?
Edge AI has an array of applications and does not limit itself to a particular vertical. Some of the applications now include facial recognition, object detection, proximity detection, etc. which can be used in self-driving cars, surveillance cameras, smartwatches, smartphones, robots, smart speakers, video games, etc.
While the Edge AI is fairly a new technology, all the companies are jumping in the race to adopt it. Some of the real-world applications from Edge AI will include:
- Smart AI Vision that includes computer vision, video analytics, real time detection and classification, etc.
- Smart Energy applications such as wind farms, smart meters, smart grids, smart solar systems, etc.
- AI Healthcare applications like remote surgery and diagnostics, monitoring of patient vital signs, smart tracking devices, etc.
- Entertainment industry like virtual reality, augmented reality, mixed reality, video streaming devices, etc.
- Smart Factory applications like smart machines, safety and productivity tracking devices, facility movement tracking, etc.
- Intelligent Transportation Systems like self-driving cars, autonomous vehicles, automatic trains, etc.
Conclusion:
Edge AI is necessary for real-world AI applications because the traditional cloud computing model is not suitable for AI applications that are computationally intensive and require massive amounts of data.
Edge AI is here and will be the frontier of technologies ahead. There’s a race to get first amongst the competition today. This is so because as the amount of data generated by devices continues to grow, there’s a growing need for the data to be processed at the edge. Also, concerns over privacy favor having data processed locally, which eliminates the need to send sensitive personal information for processing at the cloud.
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