10 powerful applications for AI in broadcast
For some time, industry discussions on artificial intelligence (often called AI) have been growing. But what is the reality and what are the potential industry benefits?
Artificial intelligence is about using intelligent and independent systems to perform complex tasks. So what AI features should I keep in mind when writing applications?
There are symbolic learning here: speech recognition, image recognition, object recognition and NLP (natural language programming).
Artificial intelligence is based on machine learning: supervised, unsupervised and enhanced, applied to deep learning, neural networks and recurrent neural networks.
As AI becomes more and more widely used in modern society, it is necessary to take a step back and list the main applications of the broadcasting (in a broad sense) industry. These are 10 potential AI applications.
1. Quality Check
Quality checking is a key task before content enters the transmission or distribution stage. The video must go through a series of necessary checks to check the technical compatibility with various devices and visually check for abnormalities, which may reduce the quality of the viewing experience.
Before the content enters the transmission or distribution phase, quality inspection is a key task. Traditionally, the tasks performed by the system will find problems with the technical standards of the file, while the manual is to find defects in the viewing experience through a five-point inspection. However, as the number of devices people watch videos increases, only checking 5 points is not a foolproof method to ensure 100% quality control. Watching this content manually and checking the compatibility of various devices can also take a lot of time and can be a tiring and repetitive task.
AI’s ability to handle symbolic learning and machine learning comes in handy in performing QC tasks. The system database can be filled with information about conforming to technical video standards for various devices and image recognition can help in finding flaws in the actual video viewing experience.
2. Search
Searching for content in a large library can be a difficult task. The content has been properly marked in the archive so that it can be searched by the task of manually entering metadata to mark the content. But even so, if the search conditions change, it is easy to miss the most relevant content.
AI takes metadata marking to the next level. Based on image recognition and symbolic learning, a large inventory of metadata can be created. AI can help in the classification of content; whether a moment is happy or sad in a video, for example. Another example is being able to identify brand logos in sports events, which will help in successful promotion of those events. Processing using AI systems increases both the speed of searches and increases the accuracy of the search response.
3. Metadata
As we all know, metadata can increase the value of content: in terms of monetization and demonetization, metadata is essential.
Through speech and image recognition, Symbol AI can create metadata information associated with any content. But AI uses machine learning to take metadata to a new level, providing content classification or grouping. It can be further improved by using neural networks to create trends. For example, associate content with its popularity in various age groups.
4. Compliance
Compliance is the process of identifying events / scenes in the video. Due to regulatory requirements, these events / scenes may limit transmission or distribution in specific regions.
Through supervised learning, AI can be used to identify such scenes in a given video clip and present “timeout” and “timeout” points to the editing system to perform further editing.
Through neural networks and deep learning, AI can help rating agencies to quickly suggest ratings for a particular program or movie. The whole process can be stored in the memory of a system using a Recurrent Neural Network.
5. Editing
Editing is very much a human skill and requires complex decision making based on the creativity of the editor and what they consider to be the best viewing experience.
But there are editorial decisions that are routine, as with the compliance example above. For example, artificial intelligence can help identify rough language that needs to beep, or blurring of certain frames or positions in a video, using advanced transcoding or editing system functions.
6. Highlights
After the game, the audience is very interested in the main events, so the highlights of sports events are the most popular. Currently, manual editing creates highlights.
AI symbol learning can help to identify the key highlights of sports events more quickly, and by using the above-mentioned advanced code conversion and editing system, it can help create highlights.
I ’m not talking about the distant future: Ferrari announced a partnership with Intel and they are working on ways to create a personal feed while watching the game. A drone follows a specific car, and AI will be able to mix and cut to provide related feed. It will be interesting to see how it develops.
AI can improve it through the search function, and can quickly provide detailed information on similar events in the past, and create or embed links to make the highlights more interesting. Such statistical analysis and video files will add value to the content.
7. Break Structure or Advertising
Identifying truly relevant advert placement alongside content can be tricky. If an advert appears at an oddly timed moment in a program, it might irritate the viewer. But if it appears during a scene switch, it may well engage the consumer and encourage them to continue watching the next part of the program.
AI, through image recognition, can identify such scene changes and provide sweet spots to place advertisements. By providing relevant advertisements based on metadata associated with one or more given scenes, it can be taken to a new level. Deep learning and neural networks can help identify the emotions of the scene and provide opportunities to insert relevant advertisements.
8. Subtitling and Close captioning
The video captioning system is obviously nothing new. However, subtitles are complex, and there are often flaws in sentence construction or punctuation. Regional / local accents further add complexity.
9. Supervision
While the distribution or transmission of content to a wide audience is straightforward, maintaining the quality of experience for the viewer and quickly identifying any issues can be tricky.
Using enhanced machine learning in AI can help improve our regulatory practices. Although it is of course possible to detect faults and identify any problems, AI can raise them to the level of predicting faults through deep learning.
10. Presenting the News
Robot technology is a part of AI, which is related to the physical movement of the system and can be used in daily life. Driverless cars are one such application. In the broadcast world, we can use robots to present the news of the day. Humanoid AI can display news based on scripts and visual effects from remote locations. It can also react to breaking news.
source from: https://www.globecast.com/blogpost/10-powerful-applications-ai-broadcast/