The modern world is surrounded by technologies and we are spending more time on our devices. It is increasing the need to bring essential computation on our devices to help businesses serve more technology users. Artificial Intelligence (AI) evolution is taking place and cloud computing is one of its essential elements. It is also accelerating the market for edge computing, which is expected to reach 1.12 trillion by 2023.
Edge AI is still evolving but has a wide range of potentials. It is a technique to proceed AI algorithms on a hardware device locally without any connection for a device with Edge AI to operate. It enables devices to process data and make decisions independently. Real-time operations like data creation, decision and superfast still hassle-free actions are prime features of Edge AI. Its local data processing avoids the risk of cloud data storage and streaming thus protecting the privacy of the data.
Edge AI provides a new way of performing AI and Machine Learning (ML). Generally, models are trained for a specific task in a typical ML setting. It is programmed to find patterns, evaluate and validate the test dataset with similar properties of the trained dataset model. The cloud infrastructure with computing and data storage resources comes with the data transfer challenges and at the cost of latency because of bandwidth limitations.
The real-time prediction needs are increasing for smart and sustainable developments. Industry 4.0 trends having Edge AI technology has autonomous systems and smart Internet of Things (IoT) devices. The ML model with Edge AI in the edge device does not need connection to the outside world. Though time for training a model on a dataset and deploying it to production remains the same for cloud computing, it reduces the latency cost and maintains the data safety.
As technologies are emerging with new features and advantages, they are accepted based on what important role they play in continuing business safely, smoothly and in a protective environment. Some of the important factors of Edge AI are:
● Edge AI provides intelligence to sensors. Surveillance cameras to record and store images for hours and use them while necessary. But Edge AI algorithms allow these systems to proceed real-time data, detect and process suspicious activities in real-time. It provides inexpensive as well as efficient service to users.
● Edge computing increases the system’s capacity to process images and data in real-time. For example, the Edge AI system can process traffic signs, road directions, pedestrians, and other vehicles for driverless vehicles and improve secure transportation levels.
● Smartphone users are increasing exponentially. Edge AI solutions incorporated in smartphones allow analyzing the images and videos to generate audiovisual stimuli and detect scenes and spaces.
● Industrial IoT is facing security issues that can be improved inexpensively with Edge AI.
● The network connection speed is increasing with developments in communication technology. 5G technology is hyped but to bring it to beneficial applications, it should have great speed and very low latency in data transmission, which is possible with Edge AI.
● Privacy: With the increased concerns of users about their private data location, companies are offering AI-enabled personalized features in their applications. It enables users to understand how their data is being collected.
● Security: Cloud data is increasingly becoming sensitive due to distributed architectures. Edge AI nodes in different AI-enabled devices like phones, speakers, robots, and tablets can find the suitable security mechanism for each device.
● Latency: Edge AI is chosen due to reduced latency. Both network level and device level services are getting more distributed, and thus latency concerns are increasing while sending data across devices and networks.
● Load balancing: Edge AI is crucial to load balance multiple endpoints to maintain end-to-end application resilience.
● Cost reduction: Edge AI-enabled devices such as wearable technologies improve user experience by reducing the cost and latency. It interacts in real-time to make payments or monitor sleep patterns, and so forth. Moreover, it reduces the required bandwidth that leads to a reduction of contracted internet service costs.
● Low maintenance: Edge technology devices have automatic monitoring and do not need specialized care by AI developers or data scientists.
Though Edge AI technology is still in the development process, it has gained popularity in different sectors. It has potential applications in various industries; some of them are autonomous vehicles, smart cities, industrial manufacturing, healthcare, and the financial industry. It is also applicable in AI virtual assistants and augmented reality devices.
● Autonomous vehicles: Though we are not expecting driverless cars immediately, the automotive industry has already invested billions of dollars in the development of autonomous vehicle technology. These vehicles should operate safely and based on real-time data analysis regarding their directions, surroundings, communication with other vehicles on the road as well as weather conditions. Moreover, manufacturers should receive their data regarding usage, maintenance alerts, and interface with local municipal networks.
New computing solutions need to be adopted by the autonomous industry to avoid interference caused to these vehicles due to the data traffic produced by personal computers, cell phones, and other connected devices. Edge computing enables autonomous vehicles to collect, process, and share data between vehicles as well as to other broad networks in real-time without latency. When driverless vehicles are combined with edge data center networks to collect and relay crucial data to emergency response services, municipalities, and auto manufacturers, they provide unparalleled reliability disabling network infrastructures.
● Smart cities: The development of smart cities is becoming a reality with a massive sensor network collecting data on utility, critical infrastructures, services, and traffic patterns every day. The collected data is stored and analyzed to get meaningful information. It allows city management officials to solve problems faster. There are limitations to conventional cloud solutions as they respond slowly to a multitude of devices in operation outside of the network. On the other hand, edge AI allows regulating device utilities and public services while responding to conditions in near real-time. The integration of edge computing with a connected network of the IoT in smart city infrastructure has the potential to transform the way people live and utilize services in the city.
● Industrial Manufacturing: One of the most benefited industries from the technological revolution is none other than the manufacturing sector. The integration of edge computing and data storage into industrial manufacturing equipment enables manufacturers in energy efficiency, better predictive maintenance, and reduced manufacturing cost with reliability. Edge AI promotes smart manufacturing practices.
● Finance: The importance of customer experience is increasing with growing competition in the marketplace. Edge AI computing integrated with smartphone apps allows banking institutions to target their services to customers better.
● Healthcare: The integration of the healthcare industry with IT solutions has faced various challenges, but edge AI offers new opportunities to healthcare professionals in delivering patient care. To provide appropriate treatment, healthcare professionals should be able to access patients' critical health information in real-time.
Edge AI-enabled medical devices gather and process patient health data and help throughout the course of treatment and diagnosis. Patients in rural areas are miles away from the nearest healthcare provider. Edge computing solutions collect, store and deliver real-time information remotely. It allows professionals to access vital medical records and process capabilities to recommend treatments.