“Artificial Intelligence is good at describing the world as it is today with all of its biases, but it does not know the world should be.” – JOANNE CHEN
In recent years the application of artificial intelligence is proliferated around the world. With the growth of digital technology in various industries, the integration of machine learning and IoT technology has become a central part of AI evolution. And all of the above businesses are becoming more aware of the technology that keeps them closer to their customers to monitor business-specific needs and trends. So, the growth of the computing market will continue significantly.
According to the marketsandmarkets global forecast report by 2026, the size of the edge AI software market is expected to reach USD 1,85 million from USD 590 million in 2020. With a growth rate of 26.5% CAGR, edge AI computing will be the new fuel to explore the hidden potential for many businesses marching towards the revolution of Industry 4.0.
During the digital transformation journey, we have seen the rational shift from mainframe to computers to the cloud, and now, the cloud is moving to the edge, and so is AI. This disruption doesn’t mean either cloud or AI is becoming irrelevant. It is a fact that now on edge we can experience and offer a whole lot of new possibilities. In this article, we will explore what is Edge AI and how it will add value to the digital transformation journey.
What Does Edge AI Means?
Edge AI is also known as tiny ML. Edge AI can be described as a class of ML architecture in which AI algorithms are processed locally on devices (at the edge of the network), and can process data without a connection. This means that operations such as data creation can occur without streaming or storing data in the cloud and which subsequently reduces the communication cost from the cloud model. In simple terms, Edge AI takes the data and processes it with the closest point of interaction with the user, whether it is a computer, an IoT device, or an Edge server by cutting out the requirement of an internet connection to process data and generate useful results.
Some of the main advantages offered by Edge AI are:
- Reduce costs and latency times
- Improved user experience
- Enhanced data privacy
- Edge AI technology devices do not require special maintenance
- Real-time data processing
The prominence of AI operating at the edge
- Edge AI eliminates the privacy issue of transmitting millions of data and storing it in the cloud, as well as the bandwidth and latency limitations that reduce the data transmission capacity.
- Edge AI can collect and store a vast amount of data generated by various IoT devices by allowing improved data processing and infrastructure flexibility.
- There are mountainous applications of Edge AI. The real-world use cases of Edge AI are chiefly comprised of two areas; industrial machinery and consumer device. Facial recognition applications, smart devices, wearable health monitoring devices, video games, smart speakers, autonomous drones, security, and surveillance systems are a few areas where edge AI technology is successfully embraced.
- Edge AI chips accelerate machine learning tasks on-device by offering lots of improvement over conventional ML architectures. This eliminates the latency involved with any network transfer.
- Edge AI is not exclusive to remote locations.
What Next With Edge AI
Edge AI is growing at a pace, and companies that are investing and embracing this technology are also growing subsequently. Tech giants like Google, Amazon, Apple, and Konduit AI are making it a key part of their AI strategy. The forecasted market growth for this technology is significantly growing. In 2020, Deloitte predicted that the consumer device market will likely represent more than 90% of the edge AI chip market, both in terms of numbers sold and their dollar value.
The two imparts factors that bloating the Edge AI usage across various industries are;
- IoT Devices
By using edge AI chips, companies can greatly increase their ability to collect and analyze data from connected devices and turn the analysis into actionable results in real-time, while avoiding the cost, complexity, and security challenges associated with sending huge amounts of data into the cloud.
- 5G Networking
5G is indispensable for the growth of IoT and edge computing because when IoT devices transmit the data, data volume swells and drops transfer speed, and eventually creates latency. With 5G networking, the speed of transfers is improved with the increase in the number of simultaneous connections and creates the biggest value when it comes to real-time processing.
The applications of edge computing across consumers level and enterprise interfaces are exploring progressive growth. Embracing edge AI on consumer devices is facilitating better engagements and acceptance and on the enterprise level, edge AI is disrupting the IoT-enabled devices and helping the enterprise to explore the next level of IoT potential. By building smarter data processing frameworks edge AI is leading the user convenience and satisfaction. The future of edge AI computing, in large part, depends on the possibilities of bringing intelligence to the device.
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