Enhancing Mobile Application Performance with Local AI Models
Enhancing Mobile Application Performance with Local AI Models: The landscape of mobile application development is currently undergoing a revolutionary change with the advent of local artificial intelligence (AI) models. The recent Google I/O event brought this trend into focus with the unveiling of their PaLM 2 models, notably the smaller Gecko model. Its defining feature is its ability to operate directly on mobile devices, heralding a new era of more robust, agile, and resourceful mobile apps.
The Client-Server Conundrum.
The task of optimizing mobile app performance has always been a careful balancing act between local and backend operations. While local operations offer the advantage of faster execution times, they are limited by factors such as the device model, storage capacity, and operating system.
This has led to a greater dependency on backend operations. However, the possibility of running a generative AI model like Gecko directly on a device could disrupt this longstanding dynamic.
Offline Mode: A New Frontier.
At Inmov, we’ve found one of the most technically demanding features of mobile app development to be the offline mode, especially in field operations where data coverage can often be intermittent or entirely absent. One of the most extreme examples we have encountered was when one of our clients expanded operations to San Francisco.
The client’s technicians found themselves working in houses fitted with Faraday cages, which block all electromagnetic fields, including radio waves, cell phone signals, Wi-Fi signals, and other types of radio waves, leaving them without any data coverage.
The traditional approach to offline mode involves storing data on the device’s internal memory until it can reconnect to the backend. While this method ensures no data is lost, the functionality of the app is considerably restricted due to the unavailability of backend services or databases.
The introduction of the Gecko model, capable of operating locally on devices, heralds a range of new possibilities. It allows apps to execute logic or services locally using the data collected during an offline event, thereby broadening the app’s capabilities even when it’s disconnected from the backend. This advancement in technology could potentially lead to greater autonomy for mobile apps, making them more effective tools for users.
Furthermore, this innovation could extend the applications of mobile apps. Currently, offline mode is offered for operations that experience short periods without coverage. But what about operations where agents have to spend extended periods in no-coverage areas? With an AI model like Gecko, we can design a mobile app capable of working independently in such scenarios.
Enhancing Data Security.
An additional benefit of running a local AI model is the improved data security. Every time the frontend communicates with the backend, it potentially exposes the information to security vulnerabilities. Although developers implement various measures to minimize this risk, no system can guarantee absolute protection. However, local operations using an AI model drastically reduce the exposure to potential hacks, as data remains on the device.
Embrace the Future with Inmov.
Inmov North America brings 15 years of experience to the table in designing and developing advanced mobile apps for a myriad of applications. Our proficiency in leveraging cutting-edge technology, such as local AI models, enables us to deliver efficient and secure solutions for our clients. If you’re looking to enhance the offline capabilities or safety features of your mobile application, get in touch with us. We’re ready to help you navigate this exciting new frontier in mobile app development.