What Is Edge AI
Edge AI is the process of analyzing, processing, and managing data generated by a hardware device at a local level without the need for the internet. Edge AI utilizes advanced machine learning, deep learning, and computing techniques to make decisions in real-time. Many industries are now relying on Edge AI because of its speedy decision making, security & privacy, energy efficiency, and enhanced user experience.
Smartphones, surveillance cameras, autonomous vehicles, robots, drones, wearable devices, and much more works on the process of Edge AI. Data security and privacy are some of the main concerns that have been sorted through Edge AI as there is no need to send data to the cloud environment. The latency and bandwidth of the edge are lower as compared to cloud latency which improves data transmission.
How mobile applications are leveraging edge AI?
Integrating Edge intelligence in mobile applications can improve user experience and interaction with computers, an IoT device, or an Edge server. Edge AI is rapidly evolving in the following disciplines of mobile application development.
IoT integration
A mobile application with integrated Edge AI can collect and analyze a large amount of data locally. It automates the operational decision-making process. The application can itself learn customers’ behavioral patterns that are stored on the physical devices. Analysis of sensor data locally has brought a significant improvement in mobile application performance. IoT integrated mobile apps lead to better resource use with higher efficiency.
The data processing in the edge mobile app services takes place close to where the data originates. It ultimately reduces the cost of bandwidth and latency to transmit data. The data is then stored for later analysis at the cloud or near data centers. The networking cost is lower as it doesn’t require internet connectivity. An example of a self-driving car that has a lot of sensors to measure the position of the vehicle and the speed of tire rotation. Here we can see the integration of IoT computing that collects data from the sensors to automatically decide steering, braking, and the use of the throttle.
Voice-Powered Technologies
Edge AI differentiates and identifies users between voices, thus providing a more individualized experience to its users. EDGE AI-powered voice technologies eliminate the privacy and compliances issue by turning users’ speech into text on a device at the edge. Cloud when decoding all words can increase latency and slow down the responsiveness of the devices. The voice-powered devices now utilize specific keywords called “wake-word” that listen to voice commands. An automatic speech recognition system (ASR) removes the misinterpreted signals from the background for better communication.
The Google Home, Alexa, and Apple Home pod speakers are examples of this technology, as they have learned words and phrases using Machine Learning and then stored them locally on the device. When a user speaks with an app like Siri or Google, the voice recording is sent to an Edge network, where it is converted to text using AI and a response is generated
Advanced personalization
Edge AI provides advanced personalization to help brands in interacting with their consumers more effectively. Personalized features and offers in mobile apps are more preferred by users as they can interact easily with what they’re into. Exclusive personalization is brought by Edge AI as it operates directly on individual data. Analyzing users’ behavior to determine their preferences can help business users to engage more customers towards the brand.
Recording the response of the users and making changes in the app accordingly is a good strategy to provide a better user experience. Edge AI Mobile apps that offer exclusive personalization based on their user’s preferences can also increase in ROI and sales of their brand. Netflix is a good example of personalization that offers suggestions to its users that they like the most based on their search results.
Quick Response Time and Effective Decision Making
The data processing is carried out in real-time without having it to send to cloud that process data in long-period of time. The cost is reduced, efficiency is increased, and the decision is automated with a high level of security and privacy. The connected mobile apps don’t require Wi-Fi and internet connectivity. For example, an aviation maintenance app might manage data locally before uploading it to the cloud once the jet is connected to Wi-Fi. Furthermore, the AI system would allow the mobile app to do additional functions. In the case of maintenance, AI might assist users in spotting problems and suggesting guidelines rather than having to scour Help Guides for answers.
Security and privacy
Edge AI ensures data security and privacy as data is stored, processed, and managed directly at the Internet of Things (IoT) endpoints. Almost every company has to keep its data confidential from hackers and third-party users. Consider the intelligent safety monitoring systems in a factory, when devices fail to function properly or people move in an area where they are not supposed to, an alarm should sound before an accident occurs.
Video data, for example, that is processed in real-time, may only exist for a fraction of a second before disappearing. Because the intruder should have direct access to the physical equipment where the data is processed, it is easier to protect data privacy and security in these cases. So, developing systems with Edge AI techniques can improve data security at a large scale.
Conclusion
Edge AI has shown tremendous benefits that can be applied to various sectors to improve the performance, efficiency, and security of their systems. Edge AI works on modern AI techniques i.e. machine learning and data sciences. It has reduced the cost of networking and has enabled companies to collect and process large amounts of data in no less time.
More organizations are recognizing the value of integrating Edge technology in their systems to provide faster, more efficient service while expanding profit margins as customers spend more time on their mobile devices. Edge AI will continue to expand, grow and enhance in the field of mobile application development.
