The power of AI combined with IoT devices adoption and edge computing power have come together recently to unleash the edge strength of edge AI. Edge AI has opened up new realms and opportunities which previously unthinkable. It covers various operations, from assisting radiologists in recognising pathologies to helping pollinate plants to driving automated vehicles down the highway.
Many businesses and analysts are discussing it and how edge AI works. The traces of this innovative technology goes back to its origin in the 1990s when delivery networks for content were introduced to serve video and web content from edge servers deployed close to users to reduce latency.
According to GlobeNewswire, the market size of edge AI has been increasing drastically over the years. As of 2021, the market size value of edge AI amounted to approximately $1459.8m (US), while the expected CAGR growth is 29.8%. Today almost all organisations have job responsibilities which can heavily benefit from edge AI adoption. It is because of this, that the market size of such a technology is enhancing in lightning bolt speed.
The application of edge AI is helping in driving Ai’s next wave, which can better human lives in different ways, at work, school, transit, and home. In order to understand the greatness of this advanced technology, it is important to know what edge AI is and how edge AI works.
The concept of Edge AI
Edge AI, also known as ‘Edge artificial intelligence, is an archetype for crafting artificial intelligence workflows that stretch the cloud (centralised data centres) and systems external to the cloud closer to physical things and humans on edge. Edge AI stands opposed to popular practice, where the complete running and development of artificial intelligence applications occur in the cloud, referred to as cloud AI. This is also different from the older approaches of AI development, where on desktops, people used to craft the AI algorithms and, after that, would deploy them on systems or any special hardware for different tasks like reading check numbers.
The edge concept is often considered as a physical artefact, like a smart router, network getaway or a smart 5G cell tower. This concept overlooks the value edge AI proposes to devices like cellphones, robots and autonomous cars. A helpful way to understand how edge AI works is to acknowledge its importance as a means to extend digital transformation practices, which are innovated within the cloud and extended out in the world.
Edge AI helps improve performance, efficiency, security, management, and operations of vehicles, smartphones, appliances, computers, and other systems that use the best practices of the cloud. Edge AI concentrates on the best architectures, processes and practices to extend machine learning, data science and artificial intelligence beyond the cloud.
Digestive Read: ‘Edge AI: The future of AI is here!’
How Edge AI works?
Recently, leading AI applications are being developed with hard-coded algorithmic rules within the system. In some cases, a new AI technique for training systems, known as “neural networks”, has come into existence. With time, researchers have discovered ways to harness the power of “neural networks” for training AI models and generating responses based on the set of inputs; this is also known as inferencing. Edge AI takes this concept and deploys it outside the cloud and on a physical device.
In simple words, in Edge AI, the machine learning algorithms are executed on-site, i.e. the edge (also known as the computing device). This is an excellent development as Edge AI enables quick computing on the data site instead of waiting for the responses from the cloud.
Why use Edge AI now?
Now we know what Edge AI is, but before migrating from the standard cloud-based approach, one needs to know why to switch. Moreover, since Edge AI has come into existence, it has piqued the interest of almost all industries. The reasoning behind their piqued interest can be a result of how edge AI works. Since many IoT implementations and devices have sensors on site, with the help of edge AI, these can be utilised to do more than send and receive data for the algorithms to understand. However, if the same algorithms are executed on the device, it shortens the timing and improves the model’s efficiency as it would receive real-time data inputs for processing.
Why choose Edge AI over cloud AI?
After knowing how edge AI works and its capabilities, let us see a few issues it tackles where cloud AI falls short.
- Low Latency and Higher Speed: As the inferencing process gets done on the device itself, eliminating the waiting time for a response like it does on cloud AI
- Cost-Effective: Unlike cloud AI implementations, edge AI configurations involve micro sensors and parts which require less financial stakes.
- Increased data security: Data is sensitive, and exposing it to the cloud can jeopardise sensitive data such as fingerprints, facial recognition, etc. On Edge AI, data is secure as it is stored locally, making it difficult to access.
- Self Reliability: Processes running on Edge AI devices can perform well even if the cloud servers go down, as they are self-sufficient to generate computed data from the collected input.
- Power efficient: Quite a lot of complicated AI tasks can be performed with low energy on edge devices instead of wasting more energy performing the same on cloud computing.
Interesting Read: ‘Cloud AI vs Edge AI- Everything you need to know’
How Edge AI works in real-life
Edge AI is a game-changing technology which has revolutionalised industries all across the globe. How edge AI works has helped optimise the operations of every sector. It has been implemented in different sectors to enhance its functions.
Manufacturing: Edge AI offer a quick accumulation of data and analysis manufactured by edge-based sensors and devices. Such permits manufacturers to better execute control on critical assets and apply anticipative maintenance protocols.
Energy (Gas and Oil): Often, it is witnessed that gas and oil plants are located in remote areas. Powerful edge computing features such as real-time analytics and effective information processing of the assets help minimise the need for better quality connectivity.
Autonomous Vehicles: One of the important use cases of Edge AI is its deployment in autonomous cars, in which real-time analysis is highly crucial. The concept and reality of autonomous vehicles would not be possible without real-time data processing. Autonomous cars’ dependency on information processing on the cloud would make the cars slow and take a few seconds to function. This would result in increasing possibilities of accidents and collisions, as even milliseconds count in the operations of a car.
Industrial IoT: Edge Ai is vastly implemented in assembly line automation and AI for visually inspecting the defects of products. Machines or device inspections are done with the help of AI algorithms in place of humans doing the inspections to save money and time.
Smart Homes: Smart homes heavily depend on IoT devices to gather and process home information. This collected data is then forwarded to a remote centralised server in which the data is processed and kept. Such an architecture might face challenges like latency, higher cost and privacy/security threats. How edge AI works to reduce the data movement timing, and sensitive data is processed on edge exclusively.
Healthcare: Edge AI helps the healthcare sector immensely with patient monitoring. This offers distinctive benefits as opposed to the conventional cloud-based framework. Often, in hospitals monitoring systems such as cardiac trackers, sensors for blood pressure, glucose monitors and so on are not connected. Even though they are connected, a huge amount of unprocessed information from systems is required to keep on multiple servers or in the cloud. An application of edge AI permits the medical provider to process every patient monitoring system data locally. It also helps in analytics in real time to record the behaviours of patients as well as view their dashboards.
Also Read: ‘Edge AI in Practice & Some Real Life Application’
Conclusion
Edge AI is a field which has not fully developed, yet it has been able to impact the ways of functioning of humankind. It is an emerging field which is developing rapidly. According to GlobeNewswire, edge AI’s market outlook for 2027 is predicted to be around $8049.8m (US). This shows that technology is expected to grow further and has the capacity to shape human lives.
Recently, consumer devices such as wearables, intelligent appliances and smartphones constitute the bulk of the edge of AI’s use cases. However, apart from these, the edge AI enterprise seems to develop quicker with its diversification into cashier-less checkout, smart cities, intelligent hospitals, automation supply chain and industry 4.0.
How edge AI works is the main reason behind its increasing implementation. It has brought the processing and storage of data from a centralised data centre to a network edge which has helped the users save time and money. Its vast list of benefits, such as removing latency, enhancing data security, reducing power as well as reducing cost and bandwidth requirements, has helped the technology grow faster. Edge AI is not only the future of AI but the future itself, which can help simplify human lives to the next level.
