How to Use AI in Mobile App Development
Not many mobile users realize that they have daily access to one of the most advanced technologies of recent years — artificial intelligence (AI). Meanwhile, it’s changing how people shop, listen to their favorite music, and complete daily chores.
Between 2021 and 2026, the AI mobile niche is projected to grow at a 25.1% CAGR and reach 22,08 billion USD. The main reason for this progress is that AI algorithms may be applied to any industry and significantly improve user experience.
But what exactly do we mean by AI in mobile app development? This post will give you a general idea of the great transformations provoked by intelligent algorithms.
1. Image Recognition
Image recognition is a subcategory of machine vision that helps distinguish objects inside an image.
To train image recognition models, AI app developers use deep learning techniques. They prepare a data set to introduce objects to an image recognition system. Of course, machines see things differently: every image is a set of pixels for them. Developers apply feature extraction to present object patterns in the form of vectors so that machines could “see” objects properly.
Interestingly, this technology mimics the mechanism of how animals detect objects but in a more computerized way.
2. Facial Recognition
Facial recognition helps identify and verify a person’s face on the image or video, determine if a face image belongs to one or two different persons, and search for human faces in a collection of images.
iOS and Android apps powered by facial recognition usually have security purposes. Face ID, for example, is one of the most commonly used applications of this technology now available on many smartphones by default.
3. Voice Recognition
Voice AI for mobile phones is presented in the form of digital assistance. Using voice and speech recognition technologies, apps can understand what was said and how to react to a given command.
Virtual assistance is not exactly new on the market, and the niche continues to grow. Customers realize how much time they save using only their voice, while people with disabilities can accomplish a wider range of actions with their smartphones.
As competition on the mobile app market becomes more severe, companies are working hard to perfect existing customer experiences. An AI-based text assistant (chatbot) available 24/7 is one of the vital workflow additions.
These smart instruments can process user queries and respond to typical questions with the help of natural language processing (NLP).
NLP is a combination of natural language understanding (NLU) and natural language generation (NLG), allowing robots to have conversations with humans.
5. Automated Reasoning
Using automated reasoning, mobile applications solve current issues based on historical data. Something that happened some time ago automatically brings insights and solutions to this very moment.
Any automated reasoning framework requires four parameters:
- Problem Domain: what issue exactly a system must solve;
- Language: what logic and programming language a system must use;
- Deduction calculus: what tools and process a system must implement to analyze information;
- Resolution: how to control the overall flow.
Navigation systems are the most obvious examples of applying automated reasoning techniques.
6. Neural Machine Translation
Using AI in mobile apps for translation seems an obvious solution. Every language is a large data set of words, so algorithms learn several of such data sets and how they correspond with each other. This type of translation is called neural machine translation.
Traditional machine translation interprets every word in a sentence ignoring the overall context. AI and ML in mobile app development enable understanding phrases, sentences, and even tone of voice, which significantly improves translation quality.
7. Advanced Personalization
By advanced personalization, we mean multifactor examining user behavior (based on a history of orders, location, account settings, etc.), providing a unique and engaging in-app experience.
According to McKinsey, 76% of consumers would opt for brands that prioritize personalized messaging and offerings. It’s highly likely that AI personalization will soon become the first choice for big and mid-sized businesses.
In our original blog post, we review real-life examples of AI-powered mobile apps and AI tools already used by mobile development teams. The transformation has started — let’s see the results.