Every sector is seeking to enhance its operations, productivity, and safety by implementing more automation. Computer programs must be able to discern patterns and perform jobs reliably and securely in order to assist them.
However, the world is unstructured, and the spectrum of jobs that humans execute encompasses an endless number of scenarios that are hard to adequately express in programs and rules.
Edge AI advances have made it possible for computers and gadgets to work with the “intelligence” of human cognition, regardless of where they are. Smart AI-enabled apps learn to do comparable tasks in a variety of situations, just like humans do in real life.
We’ll take a deep look at Edge AI, its benefits, use cases, and much more in this post.
What is Edge AI?
Edge computing allows users to have easier access to data storage and processing. This is accomplished by executing processes on local devices such as laptops, IoT devices, or specialized edge servers.
The latency and bandwidth concerns that sometimes stymie cloud-based operations are not an issue for edge functions.
Edge AI blends artificial intelligence and edge computing (AI). This entails executing AI algorithms on local devices with processing power at the edge.
Edge AI eliminates the need for system connectivity and integration, allowing users to process data in real-time on their devices. Although AI operations need a lot of computational power, the majority of them are now carried out in cloud-based centers.
The disadvantage is that service interruption or considerable slowness might occur due to connection or network difficulties.
By integrating AI processes into edge computing devices, edge AI overcomes these concerns. By collecting data and servicing users without having to communicate with other physical sites, users can save time.
How does Edge AI technology work?
Machines need to be able to see, identify objects, operate automobiles, comprehend speech, speak, move, and execute other human-like tasks. In order to duplicate human cognition, AI uses a data structure known as a deep neural network.
These DNNs are taught to respond to certain sorts of queries by being shown several samples of that question along with accurate replies.
Due to the large quantity of data necessary to train an accurate model and the requirement for data scientists to cooperate on building the model, this training process, known as “deep learning,” is generally performed in a data center or the cloud. The model develops into an “inference engine” that can answer real-world problems after being trained.
The inference engine in edge AI deployments works on a computer or device in a remote location, such as a factory, a hospital, an automobile, a satellite, or a person’s house.
When AI encounters an issue, the problematic data is frequently transferred to the cloud for additional training of the original AI model, which eventually replaces the edge inference engine. Once edge AI models are implemented, they only become more and wiser, thanks to this feedback loop.
AI algorithms are particularly beneficial in locations frequented by end-users with real-world issues because they can interpret language, sights, sounds, scents, temperature, faces, and other analog kinds of unstructured information.
Due to concerns with latency, bandwidth, and privacy, some AI applications would be impractical or even impossible to implement in a centralized cloud or business data center.
The following are some of the advantages of edge AI:
- Real-time insights: As edge technology analyzes data locally rather than in a distant cloud that is delayed by long-distance connectivity, it responds to user requests in real-time.
- Intelligence: AI applications are more powerful and adaptable than traditional programs, which can only respond to inputs that the programmer has predicted. An AI neural network, on the other hand, is trained not to answer a specific question, but rather to answer a specific sort of question, even if the question itself is novel. Applications would be unable to process endlessly various inputs such as text, spoken words, or video without AI.
- Privacy Increased: AI can study real-world data without ever exposing it to a human, considerably boosting privacy for anybody whose look, voice, medical image, or other personal information must be studied. Edge AI improves privacy even further by storing data locally and transferring just the analysis and insights to the cloud.
- Cost Reduced: By moving computing power closer to the edge, applications require less internet bandwidth, resulting in significant savings in networking expenses.
- Consistent improvement: As AI models are trained on more data, they become more accurate. When an edge AI application encounters data that it is unable to handle precisely or confidently, it often uploads it so that the AI can retrain and learn from it. As a result, the longer a model is in production at the edge, the more accurate it will be.
Edge AI use cases
Industrial machinery and consumer gadgets are the two main segments of the edge AI market. Demonstration tests are showing improvement in areas such as regulating and optimizing equipment and automating skilled labor skills.
Consumer gadgets with AI-enabled cameras that automatically detect picture subjects are also making progress. The consumer device market is predicted to grow dramatically from 2021 onwards, owing to the fact that the number of devices is greater than the number of industrial equipment. We’ve listed some popular edge AI use cases below:
- Autonomous Drones – Drones have been losing control and disappearing while conducting remote flying tests, according to the news. The pilot of an autonomous drone is not involved in the flying of the drone. They keep an eye on things from afar and only use the drone when it’s absolutely essential. Amazon Prime Air, a drone delivery business that is developing self-driving drones to deliver items, is the most well-known example of this.
- Self Driving Cars – The most exciting use of edge computing is self-driving automobiles. Self-driving cars must make immediate evaluations of situations in many circumstances, which necessitates real-time data processing. Japan’s Road Traffic Act and Road Transportation Vehicle Law were revised in December 2019, making it simpler to get level 3 self-driving vehicles on the road. The safety requirements that autonomous cars must meet, as well as the locations in which they can drive, are among them. As a result, automakers are developing self-driving vehicles that meet these requirements. Toyota, for example, is putting the TRI-P4 through its paces with complete automation (level 4).
- Smartphones – This is the edge AI gadget with which we’re all most familiar. Siri and Google Assistant, which employ edge AI to power their voice user interfaces, are ideal instances of edge AI on smartphones. On-device AI eliminates the need to send device data to the cloud because processing takes place on the device (edge). This helps to protect privacy while also reducing traffic.
- Entertainment – Virtual reality, augmented reality, and mixed reality applications for entertainment include streaming video material to virtual reality glasses. By outsourcing processing from the glasses to edge servers near the end device, the size of such glasses can be minimized. Microsoft, for example, just unveiled HoloLens, a holographic computer fitted into a headgear that allows users to experience augmented reality. Microsoft plans to use the HoloLens to provide conventional computing, data analysis, medical imaging, and gaming-at-the-edge applications.
- Facial recognition – Facial recognition systems are an advancement in surveillance cameras that can learn to recognize individuals based on their faces. AI camera module that uses edge AI computer techniques to assess face characteristics in real-time. It can detect faces fast and precisely, making it ideal for marketing tools that target certain traits such as age, as well as facial recognition for unlocking devices.
5G & Edge AI
The vital requirement for 5G in high-growth areas such as fully self-driving cars, real-time virtual reality experiences, and mission-critical applications drives more innovation in edge computing and Edge AI.
5G is the next-generation cellular network that seeks to significantly enhance service quality, such as better throughput and reduced latency — giving 10x faster data rates than existing 5G networks.
Consider real-time packet delivery in self-driving automobiles, which demands an end-to-end delay of less than 10 ms to appreciate the requirement for rapid data transfer and local on-device computation.
The minimal end-to-end delay for cloud access is larger than 80 ms, which is unacceptable for many real-world applications. Edge computing meets the sub-millisecond requirements of 5G applications while reducing energy usage by 30-40%, resulting in up to 5x less energy consumption as compared to cloud access.
Edge computing and 5G boost network speed, allowing for the implementation and deployment of various real-time AI applications, such as AI-based real-time video analytics, which rely on low latency data transfer.
Edge AI is becoming more popular, and significant investments have been made in the field. For example, in January 2020, it was announced that Apple paid $200 million to purchase the Seattle-based AI firm Xnor.ai.
Edge processing is used by Xnor.ai’s AI technology to process data on the user’s smartphone. With built-in AI on smartphones, we should expect improvements in voice processing, facial recognition technology, and privacy.
With the introduction of 5G, we can expect lower prices and more demand for edge AI services throughout the world.
As people spend more time on their mobile devices, more businesses and developers are seeing the value of implementing Edge technology to deliver faster, more efficient service while increasing profit margins.
In terms of enterprise-level AI-based services, as well as consumer comfort and happiness, this will open up a whole new universe of possibilities.
Large firms like Amazon and Google have invested millions in the development of their Edge AI systems, thus taking the lead and investing in these technologies is the only way to stay competitive.
Increased demand for IoT devices, on the other hand, will make 5G networks and Edge Computing more widely used.