Data-Driven Mobile Apps: The Power of AI and Machine Learning

In 2022, there were 16 billion mobile devices operating, marking an increase from approximately 15 billion in the previous year, whereas it’s projected that the number of active mobile devices will reach 18.22 billion by 2025.

With that being said, smartphones and other mobile devices have become a necessity — and so have mobile applications. From ordering food to booking flights, mobile apps have made our lives more convenient and efficient.

What makes these apps even more useful and efficient is the use of Artificial Intelligence (AI) and Machine Learning (ML). These intelligent, AI-driven features embedded within various apps offer users a personalised experience.

From Siri and Google Assistant to eCommerce and entertainment apps, we have come to use AI algorithms to interpret voice commands, anticipate what users need, and easily perform complex tasks.

This article explores all about AI and ML in mobile app development and how they are changing the way we use our smartphones.

As AI and ML take centre stage, we need to outline what these concepts involve. Below we explore what the terms “Artificial Intelligence” and “Machine Learning” really mean and how these technologies can transform the way we understand mobile applications.

Essentially, Artificial Intelligence is designed to carry out operations normally requiring human intelligence. AI can learn from available data and perform tasks in ways that are similar to how humans do. 

These tasks include reasoning, problem-solving, understanding natural language, and visual perception.

In mobile app development, AI creates systems that can mimic certain aspects of the human brain’s cognitive functions. 

For example, text generation with AI involves training machine learning models using large datasets. AI-generated text can serve various purposes, from content creation to chatbots, translation, and more.

When it comes to travel and communication mobile applications, AI features enable real-time translation — like using your phone camera to detect text and translate it instantaneously. 

These features enable users to engage in conversations effortlessly, regardless of the language.

Machine Learning is a subset of AI that involves the use of algorithms and statistical models to enable a computer system to improve its performance on a specific task through experience.

In other words, Machine Learning algorithms empower apps to adapt and learn from data. Machine Learning models can automatically learn from training data and make predictions or decisions without being explicitly programmed to perform those tasks.

For instance, Google Photos is a well-known photo and video storage app that uses Machine Learning for a variety of purposes. It employs Machine Learning algorithms to automatically organise and categorise photos based on content, such as people, objects, and places.

Additionally, deep learning is a subfield of Machine Learning that involves artificial neural networks inspired by the human brain’s neural structure — imagine the neural network as mirroring the human brain’s interconnected pathways.

From automated testing and content generation to personalised recommendations and predictive analytics, AI and ML have been transforming the way we create and use mobile apps, enhancing both the user experience and the development process. The section explores the main benefits of implementing AI and ML and real-life use cases to exemplify these advantages better:

One of the key advantages of incorporating AI and machine learning in mobile apps is the ability to provide personalised experiences to users. No more generic content, now users can explore content that is tailored to their activity and interests.

Machine Learning algorithms analyse user behaviour, preferences, and historical data to make personalised recommendations. 

This is similar to having a personal assistant in your pocket, understanding your preferences, and suggesting content, products, or services that you’re likely to be interested in.

Netflix uses AI and Machine Learning to offer personalised recommendations. By analysing a user’s watch history, ratings, and other interactions, the app can suggest movies and TV shows tailored to the user’s preferences.

The more a user engages with the platform, the more accurate and personalised the recommendations become. This level of personalisation keeps users satisfied, leading to increased watch time and customer retention.

Mobile app developers can use machine learning models to monitor and predict app performance. 

These models analyse various data points, including user interactions, app usage patterns, and system performance, to detect potential issues before they impact the user experience.

Imagine you’re working on a video-streaming mobile app. Users around the world rely on your platform for seamless entertainment, and any streaming issues can lead to frustration — this is where predictive analytics take the spotlight.

Using predictive analytics, you have the power to discover and proactively address streaming issues. For instance, you notice that during peak hours, users in a particular region often face buffering problems. 

Predictive analytics reveals this pattern, allowing you to optimise the content delivery network for that specific region.

Your behind-the-scenes efforts to predict, prevent, and address streaming issues ensure that your audience continues to enjoy a flawless streaming experience.

AI and Machine Learning also help enhance your app’s data storage and security. How? Machine Learning systems can be used to identify and mitigate security threats by analysing patterns in user behaviour.

Such technologies help optimise data storage and retrieval, making apps more efficient and responsive. AI algorithms also detect anomalies, which can help prevent unauthorised access to sensitive data.

For example, Amazon Web Services (AWS), a leading cloud services provider, utilises AI for data storage and security. AWS services like Amazon Macie use ML to automatically discover, classify, and protect sensitive data but also stay compliant with data privacy regulations.

We answer some of your most burning questions related to AI and ML in mobile application development:

AI and machine learning have brought the power of a system that can rival the capabilities of the human brain, at our fingertips.

From personalising recommendations based on user behaviour to providing predictive insights into app performance, the integration of AI has significantly improved the way we build and use mobile apps. 

With natural language processing and neural networks, they perform complex tasks seamlessly, making our mobile apps more intuitive and secure.

By leveraging a powerful and accurate Machine Learning model, developers can create systems that intelligently adapt to user behaviour and preferences. 

Such a Machine Learning algorithm enables apps to offer personalised experiences, predictive analytics, and enhanced data security, ultimately redefining the capabilities and functionality of modern mobile applications. Discover how EB Pearls can help you leverage the power of AI and Machine Learning for your mobile app. Get in touch with our team, and we’ll reach out shortly with more details for your specific project.