Mobile Applications And Artificial Intelligence: How Their Capabilities Are Combined

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We are at the pinnacle of a technological revolution with mobile apps. They are found in almost every industry or service, including banking and retail. This is brought about by the rising popularity of artificial intelligence and the need for quick, portable access to material. That’s why Mobile App Development Company was created, which helps companies create their mobile applications.

Technologies and Trends

Let’s start with industry trends and tell you how AI is helping businesses and people.

• Google. The company’s artificial intelligence has learned to detect breast cancer 9.4% more accurately than doctors. The system found cancer where people failed and made a more accurate diagnosis.
• John Deere. An agricultural equipment manufacturer has begun fighting weeds using computer vision and machine learning. The system finds pests through a camera and sprays them with chemicals. This way the company saves resources and gets more harvest.

Here is a list of technologies that are most often used to develop machine learning:

• Programming languages Python and R;
• PostgreSQL, SQLite3, MongoDB databases, Apache Spark, TensorFlow, PyTorch frameworks;
• sci-kit-learn, NumPy, Keras, Pandas libraries;
• image recognition networks AlexNet, ZF Net, VGG Net, ResNet, YOLO, and GoogLeNet.

Artificial Intelligence And Security In Mobile Applications

AI helps not only with personalized recommendations and data sorting but also with strengthening the security of the application. As you may have already noticed, AI is being introduced into every area, so special Gen AI Development Services have been created to help customers with this. Let’s show this with a few examples.

• Detection of suspicious account activity. Machine learning algorithms analyze user behavior, thereby identifying actions that differ from the usual pattern and may indicate a possible security violation. For example, a new input from a device that has not been used before. The system may deny access or ask you to enter a password.
• Analyze application traffic to find vulnerable areas. Algorithms detect suspicious traffic that indicates hackers are trying to gain access to personal data. Similar technologies are widely used in the banking industry.
• Applications can use data encryption and signing methods. This protects them from hacking attempts. You can also set up notifications about attempts to log into your account or changes to settings without the owner’s permission.
• Many services use AI to keep user data secure, using facial and fingerprint recognition through Face ID and Touch ID, for example.

Artificial Intelligence’s Significance For The Advancement Of Mobile Technology

Mobile apps use machine learning algorithms to offer personalized content to the user based on their preferences and behavior. For example, online movie theaters recommend the best comedies or horror films based on a user’s past viewing experience and the views and ratings of other users in that relevant group.

When a user watches a movie or TV series, the system collects information about what he watched and the user’s behavior in the application. Based on this data, the system analyzes user behavior and interests and issues personalized recommendations. Additionally, data about other users with similar interests is used to recommend films and TV series that may be of interest to this user. Also, the data analysis process can take into account various factors such as genre, rating, seasonality, similarities and differences with other films, as well as viewing history and ratings.

Recommendations work similarly in music services and online stores.

Prospects And Future Of Using Artificial Intelligence In Mobile Applications

Mobile applications using AI are increasingly being used in our daily activities, helping to solve work processes and everyday problems.

The main problem of applications based on neural networks is a large trained model that requires large storage and serious computing resources to quickly process the input data stream. Despite this, smartphones are actively developing in this direction, increasing the performance of working with neural network models, and have already reached the level of performing calculations locally at the required speed to ensure work in real time.

Most mobile applications that use AI computing require a stable, high-speed Internet connection, which is a significant limitation. However, with all this said, the future direction of AI will most likely be focused on our smartphones without sending data to a server.

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