How Machine Learning is Transforming Mobile Apps
Machine learning has become a game-changer in the world of mobile app development. With the increasing use of digital devices, machine learning has emerged as a powerful tool to solve complex tasks such as image recognition, data analysis, and forecasting.
As companies like Google began adopting machine learning algorithms, businesses quickly responded by creating intelligent mobile apps that catered to various needs, from fitness tracking to image recognition solutions. These apps not only have a dedicated audience but also attract new users with their unique features.
Machine Learning: The Key to Unlocking AI in Mobile Apps
Machine learning is a step towards creating artificial intelligence. Its primary purpose is to develop computer algorithms for automatic data processing. Instead of writing code, developers can simply feed the algorithm with data, which automatically improves itself by finding patterns and establishing logic necessary for solving tasks.
There are two main directions within machine learning: multimedia content analysis and data mining, which involves trend prediction based on Big Data. Each objective implies constructing different models that serve as a foundation for software solutions.
Customization: The Power of Personalized Experience
Many people don't realize that routine issues can be successfully solved by machine learning. For instance, predicting bad loans in banks uses machine learning algorithms to analyze client history and creditworthiness. But that's just the beginning.
Customization is key in today's digital landscape. Companies strive to establish a personalized touch with customers, who prefer tailored experiences. Imagine an app that can be set up according to user needs, offering personal content within a general concept. This can be achieved by analyzing user data, including private information, search history, and content perception.
The algorithm processes this data on a web server, producing results when the app connects to the server. This principle is used in targeted advertising on eCommerce sites and "people you might know" on social networks.
Image and Video Recognition: Unlocking AI's Potential
One of the most obvious examples of machine learning in action is issuing fines for exceeding speed limits. A machine learning algorithm recognizes license plates, defining who will receive an administrative penalty. Behind this solution lies a model for object detection in images.
Image and video recognition are frequently used in mobile apps. For instance, face recognition works perfectly for user identification within chat apps, dating and meeting apps, photo editing apps, and many others. Machine learning developers can create models for age and sex determination, as well as biometric recognition including fingerprints and eye retina recognition.
Optical Character Recognition (OCR): Unlocking Text-Based Data
OCR stands out among other tasks of multimedia content recognition. Automatic character recognition can save time by recognizing documents or credit cards and translating foreign words on different images. In practice, this task doesn't seem easy to solve because text has many characteristics – from fonts to word length – and each machine learning model must be constructed with respect to these features.
We learned this while developing a solution for receipt recognition and extracting meaning from receipts.
Audio Recognition: Unlocking Voice-Based Interactions
If you've used mobile digital assistants like Apple's Siri or Google's Google Now, then you probably know that these programs use voice recognition algorithms. While they don't always interpret users perfectly, machine learning methods can improve their accuracy. The problem of audio recognition is now more important and urgent than ever.
Ideally, a solution would allow users to create texts by simply speaking. Some specific technologies can be applied jointly, such as using audio and video recognition simultaneously to create a security system for a house that raises an alarm when someone breaks in.
Analysis of Sensory Data: Unlocking New Possibilities
The mobile app market has a variety of interesting apps that encourage healthy lifestyles and track user physical activities. Current apps can track heart rates or count steps, but they make approximations and require user input. With advanced machine learning models, the fitness tracker can be upgraded to continuously monitor user physical activity without additional instructions.
This can be achieved by integrating sensors within the app. For instance, an app for patients suffering from strokes, epilepsy, and migraines solves a similar problem.