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Artificial Intelligence (AI) has been changing the way mobile app developers design and develop their products. One of the biggest advantages of AI in mobile app development is the ability to create personalized experiences for users. With AI, app developers can analyze user data and provide customized content, features, and recommendations based on individual preferences.

Personalization Recommendations through AI

AI algorithms can analyze user data to provide personalized recommendations based on individual preferences. These recommendations could be for products, services, or content that the user might be interested in.

One of the key features of Netflix’s AI system is its ability to provide personalized recommendations to users. The AI software analyzes a user’s viewing history, search queries, and other data to determine what content the user is most likely to watch. This information is then used to populate the user interface with personalized recommendations, making it easier for the user to find the content they want to watch. The personalized recommendation system has been a key factor in Netflix’s success, as it has helped the company retain users and increase engagement. Mobile app developers can do the same.

Optimization of Video Compression

Another way that Netflix uses AI in its software is to optimize video compression. Video compression is the process of reducing the size of video files so that they can be streamed over the internet. Netflix’s AI software analyzes the video content and determines the optimal compression settings for each video. The software takes into account the user’s internet speed and device type to determine the optimal video quality for the user.

AI can help optimize video compression in several ways, including:

Content-aware encoding: AI algorithms can analyze the content of a video and identify areas that are more important to the viewer. By allocating more bits to these areas, the overall quality of the video can be improved without increasing the file size. For example, in a video with a person talking against a plain background, the person’s face and mouth movements are more important than the background. Therefore, content-aware encoding would allocate more bits to the person’s face and mouth to ensure that these areas are represented with the highest possible quality.

Rate control: AI can predict the complexity of each frame in a video and adjust the compression rate accordingly. This can help maintain a consistent level of quality throughout the video while minimizing file size.

Perceptual video quality metrics: These metrics are designed to mimic the human visual system and assess the subjective quality of a video more accurately than traditional objective measures. Perceptual video quality metrics are based on models of the human visual system, which take into account various visual factors that affect the perceived quality of the video. These factors include luminance, contrast, sharpness, color accuracy, and motion smoothness. AI can use perceptual metrics to evaluate the visual quality of a video from the viewer’s perspective. This can help ensure that the video is compressed to the point where the quality is still acceptable to the viewer.

Adaptive streaming: AI can analyze the viewer’s internet connection and device capabilities to determine the optimal bitrate for streaming a video. For example, if a user has a slow internet connection, the AI will automatically adjust the video quality to ensure that the user can stream the content without buffering. This optimization ensures that the user has a seamless streaming experience, regardless of their internet connection.

Content Delivery Network (CDN) Optimization

In addition to optimizing video compression, Netflix’s AI software also optimizes its content delivery network (CDN). Netflix uses machine learning algorithms to predict which titles will be popular in certain regions and then ensures that those titles are available in local data centers. This optimization reduces the time it takes for users to access the content they want to watch, as it reduces the distance that data has to travel. The AI software also monitors the performance of the CDN and adjusts it as needed to ensure that users can stream content smoothly.

Netflix also uses computer vision and machine learning techniques to gain creative insights to improve promotional artwork for shows. Computer vision algorithms are used to look at objective image metadata, contextual image metadata, and latent representation of images. Pairing image data with user data allows Netflix to be able to determine which image components make people more likely to engage with the content.

AI for Content Creation

Netflix Japan has also experimented with AI for content creation while creating a short film titled “The Dog and the Boy”. The credits of the film showcase a background from a scene going from a hand-drawn sketch that then becomes an AI-generated background which then gets revised by hand. The company is using machine learning algorithms to provide new tools for creators amidst a labor shortage in the anime industry.

Conclusion

Netflix’s use of AI in its software has been a key factor in the company’s success. The AI system enables Netflix to provide a personalized and seamless streaming experience to its users, optimize video compression and its CDN, and to create new content that is tailored to the interests of users. As the platform continues to grow and the amount of data available increases, it is likely that Netflix’s AI system will become even more sophisticated, providing even greater value to its users and content creators. Mobile app developers can use data gathered by AI to assist in creating personalized experiences for users as well as inform internal decisions.