Today, Machine Learning is one of the most popular areas of Computer Science. Due to the widespread use of digital devices, machine
Today, Machine Learning is one of the most popular areas of Computer Science. Due to the widespread use of digital devices, machine learning provides a revolutionary way to solve problems such as image recognition, data classification and analysis, forecasting, and many others.
Let’s dwell in more detail on the application of machine learning technologies in mobile app development.
Machine learning is a step in the direction of creating artificial intelligence. The main goal of machine learning is to create automated data processing algorithms. Instead of writing code, it is enough for mobile app developersto simply enter data into an algorithm that, processes it, learns itself, builds the logic necessary to solve the problem, and produces the desired result.
In the framework of machine learning, two main tasks are distinguished: recognition of multimedia images and forecasting trends based on Big Data analysis. Each of these tasks involves the construction of various models, which subsequently form the basis of software solutions. In turn, mobile applications with built-in machine learning algorithms are able to perform highly intelligent tasks that were previously available only to the human brain.
Many do not even think that a number of everyday routine issues are successfully solved with the help of machine learning. Intelligent apps are transforming the mobile app development industry and our lives. For example, banks use machine learning algorithms to issue loans, which allow you to analyze the history of the client and determine the solvency of the borrower. And this is only a beginning. An application built on the basis of neural networks, for example, Apple Watch, constantly analyzes your movements and heartbeats in various situations and loads. Such programs can predict the exact time a person wakes up, based on the data collected. In the future, according to experts, such applications can even predict a stroke or heart attack, which, of course, will save many lives.
Using AI and machine learning for app development allows the following possibilities:
Some systems work according to a simplified learning scheme — they simply remember the latest queries. However, for many modern applications, the development of sophisticated neural learning algorithmsis not required. For example, the typing system is quite simple, so the model is trained in real-time. The photo recognition application requires energy consumption, so the improvement process starts when the phone is in battery mode.
All modern companies strive for personalized communication with customers who, in turn, also prefer an individual approach. The more targeted the advertising message, the higher the likelihood of a deal. Imagine that the application is configured exclusively according to the wishes of the user, that is, it offers everyone its own content, but within the framework of the general concept. It is possible to implement this through a customized mobile application and this is where machine learning will come into play.
AI provides amazing possibilities for enterprises. The machine learning algorithm analyzes user data: personal information, history of search queries, interaction with content, and others. By collecting and processing this information, the application displays individual content to each user. In this case, the algorithm can be executed on the webserver, and the application will produce a result after connecting to the server. The methods of “selecting suitable friends” on social networks and targeted advertising on the Internet work according to this principle.
In mobile applications, photo recognition is used quite often. For example, face recognition is used to identify users in chat applications, organize meetings and dates, edit photos, and many others. In addition, models are built to determine the age and gender of the face. In practice, they often resort to the recognition of additional biometric data, for example, fingerprints.
A special position in the recognition of content is occupied by the task of text recognition — Optical Character Recognition. It’s no secret that the ability to automatically distinguish between text characters can save people a lot of time. Say, if such a function was available in mobile applications, users could easily recognize documents, credit cards, or translate foreign words into various languages on different images.
Navigation applications can be significantly improved if we integrate photo and video recognition algorithms into them. For example, if the application connects to the camera in the car, it can analyze the situation on the road and warn the driver in case of a possible danger. So you can recognize traffic jams, traffic signs by speed limits, aggressive behavior of surrounding drivers, and other traffic characteristics.
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