How to Build AI-Powered Mobile Apps

The fusion of AI and mobile apps is setting new trends in app development. The numbers speak for themselves – the global mobile app market is on a trajectory to amass more than $613 million by 2025, a testament to the unprecedented success and growth spurred by AI innovations.

AI has ceaselessly breathed life into the mobile apps industry, ushering in transformative capabilities that have taken product performance to new heights. Today, it is impossible to imagine mobile apps without the influence of AI, a technology that has infused them with enhanced functionality and user-friendliness. The advanced applications that we see today, capable of face, pattern, and speech recognition, are all a result of the AI revolution.

In this article, we delve into the world of AI and its indispensable role in the realm of mobile apps. We will explore the resources necessary for development and provide a comprehensive guide on how to harness them to build robust AI-powered mobile applications. Let’s embark on this journey by unraveling the basics of artificial intelligence.

• What is artificial intelligence, and how does it work?
• How does artificial intelligence enhance mobile app development? 
• How to use artificial intelligence in mobile apps
• Points to consider for implementing AI in mobile apps
• How to build AI-powered mobile apps with React Native and Tensorflow-an example
• Best practices for AI-powered mobile app development

What is artificial intelligence, and how does it work?

AI is not one technology; it is an umbrella term encompassing all software and hardware components supporting machine learning, deep learning, computer vision, natural language processing, natural language understanding, robotics, etc. Artificial intelligence enables machines to emulate human intelligence. AI develops algorithms with reasoning abilities and decision-making capabilities using machine learning (ML), natural language processing (NLP) and deep learning (DL). AI allows businesses to process large amounts of data to extract valuable information quickly. Companies use these data-backed insights to improve capabilities, increase productivity, enhance overall business and accelerate growth.

Now, let us see how AI works.

An AI system is built by carefully reverse-engineering human characteristics and capabilities into a machine and using its computational prowess and abilities to exceed human capabilities. Understanding how artificial intelligence works requires diving deep into each sub-domain and seeing how it could be applied to different industries. Here are the key technologies behind the working of an AI system.

• Machine learning: ML teaches a machine to infer from past experiences and make decisions. It can identify patterns and analyze past data points to determine the meaning of those data points. This allows it to draw a conclusion without requiring human experience. The automation capability of AI for evaluating data to arrive at conclusions saves time and allows businesses to make better decisions.
• Deep learning: Deep learning is a form of ML that teaches a machine how to process inputs in layers to infer, classify and predict the result.
• Neural networks: These are based on the same principles as those involved in working human neural cells. These algorithms capture the relationships between underlying variables and process the data the same way as a human brain does.
• Natural language processing: NLP refers to the science of understanding, reading, and interpreting language by machines. When a machine understands what the user is trying to communicate, it responds accordingly.
• Computer vision: Computer vision algorithms attempt to understand an object by breaking it down and looking at its different parts. This allows machines to classify and learn from different images in order to make better output decisions based on past observations.
• Cognitive computing: Cognitive computing algorithms try to mimic a human brain by analyzing text/ speech/ images/ objects like a human does and tries to give the desired output.

How does artificial intelligence enhance mobile app development?

AI expansion continues to catalyze mobile app enhancement, making mobile apps smarter. These apps can be used to predict user behavior and make informed decisions enabling brands to learn from user data and improve user experiences.

AI can enhance mobile app development in many ways:

Enhanced security

With growing hacking incidents, app users are concerned about security, particularly if they use solutions like mobile banking and online shopping that accept digital payments. The safer your app, the better it is. AI can help in this area, too, fulfilling user expectations. Artificial intelligence, which processes large amounts of data and detects potential cybersecurity threats that can help prevent attacks before they happen by alerting you and other users. Your app’s security will improve with time, as well-trained machine learning algorithms draw on past experiences. AI can also increase the security of your app’s biometric authentication system by increasing accuracy and efficiency. This is done by analyzing biometric data such as fingerprints and facial scans from the database.

Smart automation

AI-powered automation in mobile app development streamlines repetitive tasks like code generation, automated testing, bug detection, and performance optimization. It improves developer efficiency by reducing manual effort and allowing them to focus on higher-level tasks. This results in faster development cycles and enhanced app quality. With AI handling mundane tasks, developers can allocate more time and resources to higher-level tasks such as innovation, problem-solving, and delivering a superior user experience.

Improve efficiency

AI tools and frameworks have the power to significantly improve the efficiency of mobile app development. By leveraging AI capabilities, developers can streamline various stages of the development process and automate repetitive tasks. For instance, AI can automate code generation, saving developers valuable time and effort. Additionally, AI can optimize app performance by analyzing data and making intelligent decisions to enhance speed and efficiency. By automating these tasks, developers can work more efficiently and concentrate on higher-level tasks such as innovative feature development and improving user experience. Ultimately, AI-driven efficiency enhancements contribute to faster development cycles, increased productivity, and the delivery of high-quality mobile apps.

Automated testing

App developers who want to ensure their app runs smoothly should automate testing, which was traditionally done manually. Nowadays, many tools help automate the process, which includes integration testing, unit testing and functional testing. As the number of tests and test cases increases, it becomes more difficult to manage all the tests manually, making automated testing tools essential for keeping up with changing requirements. These tools also make it easier to test different platforms and configurations (e.g., Android vs. iOS).

How to use artificial intelligence in mobile apps?

Artificial intelligence can increase app retention, engagement, conversation rates, and other factors. Let us take a look at how AI can enhance mobile apps.

In-app personalization

Personalization is a great way to attract users and keep them engaged. AI-powered personalization allows you to adapt your app content to each user’s needs increasing the likelihood that they will stay on your app or return. Additionally, personalized recommendations are a great way to help users find the right product faster. Let us take, for example, an AI-powered shopping app that recommends a pair of shoes based on your past purchases. It can also suggest items from different brands similar to the product you check out.

Suggestions based on contextual searching

Contextually suggested terms can be a great way for users to narrow down their search results. These suggestions are often based on the context of the query and can include data such as weather conditions, times of day, and location. AI-powered search engines can access huge amounts of data and generate contextually relevant phrases below the search bar. This allows users to save time by automatically entering every query variant, which can be a time-saver when using an app. The best thing about these suggestions is that they are auto-completed and formatted for the query. To complete their search, the user only needs to choose one of these suggested phrases.

Chatbots

Chatbots are a type of AI designed to provide information against user queries with proper answers based on the technology of NLP at the backend. Chatbots can be used in-app to help customers or as a way for companies to provide customer service. It is a good idea to have a chatbot to help users plan their trips if they use a travel app to book hotels or flights. Chatbots could also be used as part of your marketing strategy.

Object detection

Mobile apps can use AI technology to detect objects. Integrating artificial intelligence with image recognition technology, apps can recognize people, things, places and other relevant factors. For example, AI could be used to aid in disease diagnosis. A staff member can upload a scan of an anomaly or a photo, and the underlying algorithm will quickly review it and determine if there is a problem. On the other hand, picture recognition software can dramatically speed up the process of claim resolution and underwriting in the insurance industry. This is done primarily by quickly and efficiently detecting dangers from social media scans and damage assessment from photos.

Virtual assistants

AI-powered voice assistants, such as Siri, Google Assistant, or Amazon Alexa, enable users to interact with mobile applications through voice commands. These assistants provide convenience, hands-free operation, and instant responses to user inquiries. Virtual assistants possess the ability to identify and comprehend a speaker’s voice, thereby understanding their spoken commands. Utilizing advanced voice/speech recognition technology, these assistants can execute requested actions. The convenience offered by virtual assistants is notable, as users can interact with the software simply by clicking on it. This feature not only saves significant time but also enables users to engage with the application hands-free, particularly beneficial while driving or performing other tasks that occupy their hands. Moreover, individuals with hearing impairments rely on voice recognition as an essential means of communication. Additionally, AI plays a pivotal role in generating dynamic call scripts promptly for sales personnel and adapting dialogue ideas to suit each call, thereby facilitating optimal customer service.

Predictive analytics

Analytics is an integral part of any business. No matter whether you are in healthcare or retail, insurance or finance, analytics is essential to any business. Artificial intelligence will change the way you look at app analytics. Intelligent algorithms can seamlessly scan through customer usage data to provide insights that allow you to analyze past performance and predict what will happen in the future. AI can help you improve your business performance by predicting demand and analyzing user behavior.

Automated replies

AI enables automated reply functions, such as smart replies, which analyze the sentiment of messages and suggest appropriate responses. This feature enhances communication efficiency by providing users with suggested replies that are tailored to the specific message or conversation. Users can simply select a suggested response instead of composing a reply from scratch, saving time and effort. It is especially useful in situations where quick responses are required or when dealing with a high volume of messages. Companies like Google have successfully integrated smart reply functionality into their messaging platforms and email services. This AI capability not only improves the user experience by facilitating faster and more convenient communication but also helps businesses manage their communication channels more efficiently.

Real-time language translation

AI-enabled translators that operate in real-time and offline modes can be included into mobile applications. The use of such apps enables unhindered global communication. Real-time language translation is a significant benefit of AI in mobile app development. By integrating AI-enabled translators into mobile applications, users can communicate seamlessly across different languages and overcome language barriers. These translation apps utilize AI algorithms to process and translate speech or text in real-time, providing instant translations.

Emotion recognition

AI is capable of deciphering human emotions through sophisticated audio and visual processing. AI can recognize human emotions by observing human senses, voice articulation, and facial expressions, allowing for more personalized and humane interactions. By incorporating emotion recognition technology into mobile apps, developers can create more personalized and empathetic interactions. For example, an app can analyze the user’s voice tone and facial expressions during a video call or voice interaction to detect emotions such as happiness, sadness, anger, or surprise. This information can then be used to tailor the app’s response or provide appropriate recommendations, making the user experience more engaging and relevant.

Enhanced search capabilities

AI-powered search algorithms can improve search functionalities in mobile applications. These algorithms can understand user intent, correct spelling errors, and provide more accurate and relevant search results, enhancing the overall user experience. This ensures that users receive relevant search results, even if they mistype or misspell a word. The result is an enhanced user experience where users can find what they are looking for more quickly and effortlessly. Whether it’s searching for specific content, products, or information within the app, AI-powered search capabilities improve accuracy, relevance, and user satisfaction. By integrating AI into search functionalities, mobile apps can deliver more intelligent, intuitive, and user-centric search experiences, ultimately enhancing the overall usability and value of the application.

Best machine learning-enabled platforms to develop an AI-powered mobile app

Azure

Azure is a Microsoft cloud service that offers a large support community, high-quality multilingual documents and many tutorials. This platform supports Python and R programming languages. AI app developers can create mobile apps with precise forecasting capabilities thanks to an advanced analytical mechanism.

IBM Watson

IBM Watson is a tool that allows developers to handle user requests comprehensively, regardless of format and data. Multiple approaches can be used to quickly analyze voice notes, images and printed formats. It is the only platform that supports this search method. Other platforms use complex logical chains of ANN to search for properties. In most cases, IBM Watson’s multi-tasking proves advantageous because it determines the minimum risk factor.

TensorFlow

Tensorflow is Google’s open-source library for AI applications that allows companies to develop multiple solutions to solve nonlinear issues. Tensorflow apps leverage the user’s communication experience and find the correct answers to users’ questions. However, this open library is not the best option for beginners.

API.ai

This platform was created by Google’s development team, which is well-known for using contextual dependencies. It can be used to build AI-based virtual assistants for Android and iOS. API.ai is based on two basic concepts: entities and roles. Entities are the central objects, and roles are the accompanying objects that control the activity of the central object. API.ai’s creators have also developed an extremely powerful database that has strengthened their algorithms.

Wit.ai

API.ai converts speech files into printed text. It also has a history feature that can analyze context-sensitive information and generate highly precise answers to user questions. This is particularly useful for chatbots used on commercial websites. This platform is ideal for creating mobile apps with machine learning on Windows, iOS and Android.

Amazon AI

This AI-based platform can be used to recognize human speech and visual objects using deep machine-learning processes, This solution can be used to deploy cloud-based apps, allowing for low-complexity AI-powered mobile applications.

Clarifai

AI is a method that analyzes data using complex and capacitive algorithms. This platform can integrate in-app via REST API, making it the best choice for developers who want to invest in artificial intelligence for app development.

Points to consider for implementing AI in mobile apps

Implementing artificial intelligence or machine learning into a regular application requires a significant shift in the operation and management of the application as it is not intelligent. Hence, AI requires that we look at pointers different from what is required when investing in the mobile app design process, as it involves data to a great extent.

Identify the problem to be solved by AI

While applying AI to a mobile app, it is easier to apply one process instead of multiple. When the technology is applied feature-based of the application, it is easier to manage and integrate to the best extent. So, decide which part or feature of your application would benefit most from AI intelligence. Will it provide a better ETA? Depending on this analysis, collect data from this field.

Get to know your data

Understanding where the data comes from is crucial before looking forward to AI app design. Identifying the data sources at the initial stage of data fetching proves helpful. Next, you need to perform data refinement, ensuring that the data you plan to feed into your AI module is clean, accurate, and not duplicated.

Understanding that APIs do not suffice

When it comes to implementing AI within a mobile app, the next major thing is understanding that using more API would not be useful. Although the APIs used for AI development is sufficient to convert your simple mobile app to an AI app, they do not support full-fledged AI solutions. You will need to do more data modeling to make your model intelligent, which is something APIs cannot do.

Assigning metrics to measure the effectiveness

It is not worth having an AI or machine learning feature in your mobile application unless you have a way to measure its effectiveness. This can only be done after you understand what you are trying to achieve. Before implementing AI or ML in your mobile application, ensure you understand the goals.

Involve data scientists

Consider hiring data scientists for your payroll or investing in a mobile application development company with data scientists. Data scientists can help with data management and refining, which is essential if you want to excel in artificial intelligence.

Feasibility analysis and practical changes to make

In the previous sections, we discussed the crucial steps involved in implementing AI and machine learning in apps. However, before you move to final implementations, the following checks must be done.

• Do a quick test to determine if your future implementation will benefit your business, increase user experience and improve engagement. The most successful upgrade will make your customers and existing users happy and draw more people to your product. An update that does not improve your efficiency is not worth the effort.
• Examine your existing team to determine if they can provide the required results. You should hire more employees or outsource the work to an expert and reliable artificial intelligence development company if there is insufficient internal team capacity.

Data integration and security

Any mobile app will need an advanced information configuration model in order to implement machine learning projects. ML deployment may be affected by outdated data that is not in the best format. Paying attention to data sets when deciding what capabilities and features will be included in an application is crucial. Over time, your app will perform well with well-organized data and careful integration.

Security is an important aspect that should not be ignored. You need to plan the right arrangement to incorporate security requirements while still adhering to standards and meeting the needs of your product.

Strong technological support

Choosing the right technology and digital solutions to support your application is important. To keep your app running smoothly, you must ensure that all data storage, security tools, backup software and optimizing services are secure. Without this, there is a high chance of a drastic drop in performance.

How to build an AI-powered mobile app with React Native and Tensorflow? An example

Here are the steps required to create a React Native app for image recognition that is both suitable and inclusive.

Prerequisites

A few deep-learning tools must be available to bolster your development efforts and create the best solution.

TensorFlow

It is one of Google’s most popular deep-learning tools. This tool can train neural networks to help machines perform tasks allowing you to save a binary file containing a trained model. To create networks, you don’t need to start from scratch. Instead, you can use Inception to build your model, which is a classifier.

Inception

Google has created this tool for image classification as a pre-requisite, which is not only a powerful tool but also has been used to classify close to 2000 images.

Let us explore how we can build an AI-powered mobile app that can perform image recognition with React Native and TensorFlow. This application will match the images correctly.

The React Native Components Include

• React native TensorFlow – npm i @tensorflow/tfjs-react-native
• React native Caffe 2 – npm i react-native-caffe2
• React native coreml – npm i react-native-coreml
• React native image ml – npm i react-native-core-ml-image

The step-by-step process to develop an image recognition app with TensorFlow and ReactNative

Step 1: Initial setup

First, you would need to set up a new React Native project and install the TensorFlow.js library for React Native. The Tensorflow library aids the training process of deep learning models by providing tools to optimize and fine-tune them.You can do this by running the following commands:

npx react-native init myApp
cd myApp
npm install @tensorflow/tfjs-react-native

Step 2: Train the model using the Image Recognition API

Next, you would need to train a model using TensorFlow.js to recognize the objects or images you are interested in. Several pre-trained models are available in the TensorFlow.js library that you can use or train your own model using a dataset of images.

Create a JavaScript file named rn-cli.config.js in the root of your project directory. Next, add the following code to label the TensorFlow model:

module.exports = {
getAssetExts() {
return ['pb', 'txt']
}
}

pb = extension of the output model
txt = extension of label file

Step 3: TensorFlow to Assets

To this asset file, you will need to add the TensorFlow model to improve image recognition

tensorflow_inception_graph.pb

Step 4: Label Assets

Next, you need to add the label output to the assets directory

tensorflow_labels.txt

Step 5: Initialize the tfImageRecognition API Class

You now have a model and a label. You can initialize the tfImageRecognition API class using the following card

const tfImageRecognition = new tfImageRecognition({
model: require('./assets/tensorflow_inception_graph.pb'),
labels: require('./assets/tensorflow_labels.txt'),
});

Step 6: Call for Recognize Function

The last step is to help the model recognize the image that you have in front of you using the training set. Here is the code for the same

const results = await tfImageRecognition.recognize({
image: require('./assets/panda.jpg'),
});

Additional step
Once you have a trained model, you can integrate it into your React Native app by importing it and using it to make predictions on new images. You would also need to build a user interface for capturing or selecting images to be passed to the model for prediction. Here is a sample code to perform the same:

import React, { useState } from 'react';
import { Image, Text, View } from 'react-native';
import * as tf from '@tensorflow/tfjs-react-native';

export default function App() {
const [prediction, setPrediction] = useState(null);

async function predict(image) {
const model = await tf.loadLayersModel('path/to/model.json');
const prediction = await model.predict(image);
setPrediction(prediction);
}

return (
<View>
<Text>Prediction: {prediction}</Text>
<Image source={{ uri: 'path/to/image.jpg' }} onLoad={predict} />
</View>
);
}

Best practices for AI-powered mobile app development

To deliver a seamless and optimized AI-powered mobile app solution, you need to follow some best practices as described below:

Use of a suitable code editor

For seamless coding and commenting, code editors are crucial. Sublime Text 3 is one of the best recommendations, which offers advanced features like shortcuts, search and split, and command palette and makes it easy to code.

Analyze raw data

You need to be familiar with your data in order to code well. Your input data can help you build training sets and improve your results. So, having the data available for the algorithm and enhancing your model is essential. You can only offer good results if you know what your raw data contains in it.

User-centric approach

Your user should be in complete control. Your design should be clear and have the right features, eliminating any redundant or unnecessary parts. Also, ensure you comply with user requirements.

Make use of the Python dictionary

The Python dictionary stores all data, and it contains fundamental values, each of which is unique. It allows you to store the information in a useful way. The dictionary stores data related to user IDs and profiles, making programming easier and eliminating the need to use if and else statements.

Use machine learning tools

You might consider using the most up-to-date machine learning tools like:

• Apple CoreML, which is a domain-specific ML framework.
• NLP
• Gamelaykit
• Caffe2
• C++
• Python API
• TensorFlow

Use of low-code platforms

Low-code platforms are among the best for delivering AI-driven apps that increase productivity and improve quality. These platforms facilitate collaboration and enable intuitive app development.

Endnote

It is evident that AI significantly impacts mobile app development, making mobile apps more user-friendly, efficient, and accurate by integrating AI. Businesses can now process huge amounts of data in real-time and make informed decisions. AI can also help businesses stand out in a highly competitive app market by offering unique and innovative features. Therefore, investing in AI-powered mobile app development can help businesses stay current with evolving customer expectations and stay competitive. Both users and businesses can benefit from the power of AI in mobile applications, which is a win-win situation.

Ready to take your mobile app to the next level? Improve user engagement and drive business growth with LeewayHertz AI development services.