Mobile artificial intelligence is disrupting the already breakneck mobile app development game. In 2020, the mobile AI sector reached a valuation of $2.14 billion, and this figure is expected to grow 4.5 times by 2026. It’s safe to say that mobile artificial intelligence is here to stay, so let’s find out how this innovative technology is used in mobile app development.
Understand the benefits of mobile artificial intelligence
Mobile artificial intelligence aims to make mobile technology smarter and more functional for users. A well-known example of the power of mobile AI is Amazon’s Alexa Shopping product, which has saved Amazon countless hours of customer support work. At the UX level, it has also brought tangible quality of life improvements to end users.
The most significant industry growth will most likely come from AI technology for virtual assistants. The stunning success of last-generation AI assistants like Siri and Alexa shows the staying power of technology. AI-enabled processors in next-gen mobile devices come preloaded with various intelligent solutions such as language translators, context-aware AI assistants, AR and VR enhancements, and enhanced security features. The future of these apps and on-board solutions lies in high extensibility and integration with third-party mobile applications, providing developers with a full-featured AI development ecosystem.
Forecasts for related sectors such as smartphones, drones, cameras and imaging, robotics, automotive and cloud computing also show explosive growth in mobile AI technology. Despite attempts by the governments of the United States and other Western countries to restrict drone technology for consumers, the drone sector is likely to grow exponentially with the availability of AI-enabled mobile processors. Next-generation drones offer home and business users overwhelming features such as AI-assisted photography, AI autopilot and navigation, surface mapping and GPS, among many more applications.
The potential for next-gen AI to eliminate countless man-hours from the AI app development pipeline cannot be overstated. AI helps programmers overcome obstacles that used to cost a lot of time and money, such as B. porting software across platforms and eliminating much of the manual error checking and troubleshooting once done by human testers.
How AI makes your app smarter
As the total number of mobile users continues to grow as younger, more tech-savvy generations come of age, the demand for features like customization has skyrocketed.
While the UI has historically been handled by app developers as first-party, many app developers are now using the built-in UI from smartphone manufacturers to provide an interface to their users. Because these manufacturers contain AI-enabled processors, smartphones can analyze user behavior and make real-time adjustments to app interfaces for an improved user experience, e.g.
Artificial intelligence brings amazing new possibilities for mobile development through machine learning, recognition technologies, biometrics and language technologies.
There’s a reason many companies have invested so much money into developing machine learning and it stems from the machine learning paradigm’s ability to predict and optimize user behavior, leading to upsells and cross-sells.
Much of Spotify USA, Inc.’s success with its flagship Spotify application is due to machine learning integration. Spotify delivers tailored playlists and attention-grabbing content, such as new releases, relevant to a customer’s interest right at app launch. Machine learning not only helps improve the end user’s overall experience with the app, but also keeps them coming back for more by using context to provide appropriate content to increase overall usage time.
In a highly competitive app market driven by metrics such as For example, how long the user has been using your app, machine learning enables companies to entertain and engage users by increasing relevant metrics to rank higher on Google Play and the App Store.
Online retailers use machine learning to create a profile for their customers based on various metrics such as the customer’s past purchases, the customer’s relationship with other users, the customer’s behavior on the website or in the application, and many other factors based. Using this data, the retailer gives the customer a range of recommended products based on their interests. For example, Amazon makes extensive use of machine learning to match customers with products they are likely to buy. Machine learning is present at every stage of Amazon’s logistics workflow, from the end user’s experience of using the website or app to optimizing shipping schedules.
Large transport companies like Uber are implementing machine learning in their logistics apps to provide drivers with up-to-date information on the go. Machine learning solutions help predict the fastest possible route for drivers and optimize for potential traffic jams. ML-based applications use historical data to make inferences about road conditions and can also incorporate real-time traffic information into historical projections to make the most accurate guesses.
Mobile AI powers breakthrough image recognition technologies like Google Lens. Google Lens and other similar apps have revolutionized the way many people interact with the world. Advances in image recognition have made it possible to do everything from identifying specific plant varieties and species to translating foreign language text in real time using OCR powered by machine learning.
Financial institutions use the same technology in their mobile apps to process checks without the customer having to come to the bank branch. Pharmacists use this technology to scan medical prescriptions and import them into software to verify the drug’s presence in pharmacy databases. Retailers use OCR to automatically gain valuable insights from order analysis. And the list goes on and on.
Next-generation mobile AI improves on previous face recognition technology and uses technologies such as artificial neural networks to speed up the human face recognition process. Mobile AI face recognition modules first scan the image in real time, recognizing and tracking human faces. Once a face is labeled in an image, the face is properly aligned for further analysis. Then features are extracted from the face and matched against a database of facial information to enable reliable authentication.
AI biometrics significantly increases the level of protection of mobile applications, making them suitable for storing more sensitive data. This expands the use cases of mobile applications for areas like healthcare, government, finance and more.
Sophisticated text-to-speech technology benefits from mobile artificial intelligence implementations, providing crisp and clear speech capabilities generated from text input. Enhanced text-to-speech helps visually impaired users navigate apps and websites and converts static text into audio content. With the improvement of text-to-speech technology, users can translate entire books into audiobooks with the click of a button.
AI assistant technology uses voice recognition powered by mobile artificial intelligence to interact with users without latency. Worded commands from the user are processed into actions by the virtual assistant, providing a seamless experience. For example, Amazon’s Alexa and Apple’s Siri are now able to perform many different requests, intelligently recognizing the intent of the user’s request based on context and making inferences when information is incomplete.
The coming changes
The field of mobile artificial intelligence is growing exponentially. Many industries are facing rapid change due to advances in artificial intelligence technology. As mobile processors integrate AI-friendly features, the AI capabilities of first- and third-party applications will improve dramatically.
Key technologies making this possible include machine learning, recognition technology, biometrics and language technologies. Mobile AI helps streamline processes, remove roadblocks for users and vendors, deliver relevant content, improve end-user engagement, and streamline the development process. AI makes mobile apps more extensible, modular, dynamic and offers superior performance for both developers and users.