Building Smarter Apps Using Mobile Artificial Intelligence – IoT For All

Mobile artificial intelligence is disrupting the already breakneck-paced mobile app development game. In 2020, the mobile AI sector reached a valuation of 2.14 billion dollars, and that number is expected to grow 4.5x by the year 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. 
Mobile artificial intelligence aims at making 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 freed up countless hours of customer support grunt work for Amazon. On a UX level, it has also delivered noticeable quality of life improvements to end-users.
The most significant industry growth will most likely come from AI virtual assistant technology. The show-stopping success of last-generation AI assistants like Siri and Alexa demonstrates the holding power of the technology. AI-capable processors in next-gen mobile devices will come pre-packaged with various intelligent solutions such as language translators, context-aware AI assistants, AR and VR enhancements, and improved security features. The future of these apps and on-board solutions is high extensibility and integration with third-party mobile applications, providing developers with a full-featured AI development ecosystem.
Projections for related sectors such as smartphones, drones, cameras & imaging, robotics, automotive, and cloud computing also show explosive growth from mobile AI technology. Despite attempts by the governments of the United States and other western countries to place restrictions on consumer drone technology, the drone sector is likely to grow exponentially with the availability of AI-capable mobile processors. Next-gen drones offer mind-blowing features to home and enterprise users like AI-assisted photography, AI autopilot and navigation, surface mapping and GPS, and many more applications.
The potential for next-gen AI to remove countless man-hours from the AI app development pipeline cannot be overstated. AI helps programmers overcome obstacles that formerly cost a lot of time and money, such as porting software across platforms and eliminating much of the manual error-checking and troubleshooting once accomplished by human testers.
As the total number of mobile users continues to grow as the younger, more technically literate generations come of age, the demand for features such as customization has skyrocketed.
While UI in the past was handled in a first-party way by app developers, it is now the case that many app developers utilize on-board UI from smartphone manufacturers to provide an interface for their users. As these manufacturers include AI-capable processors, smartphones can analyze user behavior and perform real-time customizations of app interfaces for improved user experience, such as nudging buttons in the interface a few millimeters to account for variation in the size of a user’s fingers.
Artificial intelligence brings amazing new possibilities to mobile development through machine learning, recognition technologies, biometrics and voice technologies.
There’s a reason that many businesses have invested so much money into machine learning development that comes down to the machine learning paradigm’s ability to predict and optimize for user behavior which leads to upsells and cross-sells.
Much of Spotify USA, Inc.’s success with its flagship application, Spotify, has come from machine learning integration. Spotify delivers tailor-made playlists and attention-grabbing content like new releases relevant to a customer’s interest right as the app is booted up. Machine learning not only helps improve the end user’s experience with the app overall, but it keeps them coming back for more by using context to deliver appropriate content to drive up total usage hours.
In a highly-competitive app market driven by metrics such as how long the user has used your app, machine learning enables companies to keep users entertained and engaged, driving up relevant metrics to rank higher on Google Play and the App Store.
Online retailers use machine learning to generate a profile for their customers based on various metrics such as purchases the customer has already made, the customer’s relationship with other users, the customer’s behavior on the site or application, and many other factors. Using this data, the retailer gives the customer a set of recommended products based on their interests. For example, Amazon makes extensive use of machine learning to connect customers with products that they are likely to purchase. Machine learning is present in every stage of the Amazon logistical workflow from the end user’s experience using the site or app all the way to the way shipment schedules are optimized.
Major transport providers like Uber implement machine learning in their logistics apps to provide drivers with up-to-date information on the road. Machine learning solutions help predict the fastest possible route for drivers, optimizing for potential traffic jams. Utilizing historical data to make inferences about road conditions, ML-based applications can also plug real-time traffic info into historical projections to make the most accurate guesses.
Mobile AI powers groundbreaking image recognition technology like Google Lens. Google Lens and other similar apps have revolutionized the way that many people interact with the world. Strides in image recognition have made it possible to do anything from recognizing specific plant varieties and species to translating foreign language text in real-time with OCR powered by machine learning
Financial institutions use the same technology in their mobile apps to process checks without needing the customer to come into the bank branch. Pharmacists use this tech to scan medical prescriptions and import them into software to check the presence of the medicine in pharmacy databases. Retailers use OCR to extract valuable insights from purchase order analytics automatically. And the list goes on and on.
Next-gen mobile AI improves on prior facial recognition technology, making use of technologies like artificial neural networks to speed up the process of detecting human faces. Mobile AI facial recognition modules first search the image in real-time, detecting and tracking human faces. Once a face in an image is labeled, the face is properly aligned for further analysis. Features are then extracted from the face and matched to a database of facial information to provide reliable authentification.
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 such areas as healthcare, government, finance, and more.
Sophisticated text-to-speech technology benefits from mobile artificial intelligence implementations, providing crisp and clear voice functionality generated from text input. Improved text to speech helps visually impaired users navigate apps and websites, transforming static text into richly voiced content. As text to speech technology improves, users will be able to translate entire books into audiobooks at the tap of a button.
AI assistant technology utilizes voice recognition driven by mobile artificial intelligence to interact with users without any latency. Phrased 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 execute many different requests, intelligently sensing the intent of the user’s request based on context, making inferences where information is incomplete.
The mobile artificial intelligence sector is growing exponentially. Many industries face rapid transformation due to strides in artificial intelligence technology. As mobile processors integrate AI-friendly features, the AI capabilities of first and third-party applications will drastically improve. 
Key technologies making this happen include machine learning, recognition technology, biometrics, and voice technologies. Mobile AI helps optimize processes, remove obstacles for users and providers, deliver relevant content, enhance end-user engagement, and improve the development process. AI makes mobile apps more extensible, modular, dynamic, and offers superior performance for both developers and users.

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Connect with Chris Hood, a digital strategist that can help you with AI.

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