Producing and distributing a movie or TV series is a big project. There are hundreds of elements affecting the success of the final product. With generation Z gradually taking over the internet, they are asking for more personalized content. Something that is never possible without data science. This is why nearly every section of the movie and TV industry is taking advantage of AI. Giant streaming networks and media companies like Netflix, Amazon, Disney, HBO, Warner, AppleTV are using machine learning techniques to understand their customers, plan their projects or design their content.
As an example, Netflix, the biggest streaming service out there with about two hundred million users, (see InsiderIntelligence) uses machine learning to estimate the total project cost for different production settings -like the filming locations or the number of extras-. The streaming network also schedules its filming times and plans its post-production process using neural networks. They use data science to find out the best release dates for a movie in each region/language according to user preferences in that region/language. Even after release, Netflix data scientist apply their techniques to optimize the quality control process.
Nearly every streaming service we know uses a recommender system to show the user the programs she might like more. The models that predicts the user interest are extracted from the user historical data. Historical data includes what the user has viewed, her search keywords, how she has rated movies, content date and duration, and the device she is using.
Personalized Viewer targeting
Content networks can use AI to display the same content in a different way to each type of viewer. For example, when two different users log into their Netflix account, It is possible that they see two different pictures for the same TV series. Each one might see their favorite actor in a movie thumbnail. This is because Netflix uses ML to show personalized thumbnails for media items, based on parameters like age group, favorite cast, and region.
There are AI algorithms that can be used to analyze entire movies. These methods process the scenes and use object detection, text detection, and voice recognition techniques to find the objects, people, title, cast, and dialogues of the show. They use the information that they have extracted to generate tags for the media item and categorize it based on its content (e.g. genre, ending, …etc.). These algorithms make search results more relevant and give the user advanced search options. Using them in recommendation systems can reduce the singular dependency of recommendations and historical data.
Improving content quality
Many media networks, including Youtube, use voice processing and NLP algorithms to offer automatic Subtitle Generation for people speaking other languages. Another deep learning method syncronizes this automatically generated subtitles with the video feed. It generally takes less than a minute for subtitle-synchronization neural networks to process a 45-minute video. This time is a lot shorter than the time this task takes from a human operator. In particular, Netflix employs machine learning methods to distribute resources throughout the nodes in its content network based on the demand patterns for that time and location.
Content automation is a relatively new, and less common kind of AI use case in the media industry. However, its use is spreading over time. Some examples of it include the artistic creations of GPT-3, the novel deep neural network made by OpenAI labs. We introduced one of them in a former blog post. Organizations employ machine learning to advise the writers about user preferences. They offer better choices for the characters and events of an animation’s script. One such model was trained by Imagineering -Walt Disney’s R&D organization- based on 28000 quora answers. IBM’s supercomputer, Watson, even managed to produce a short horror trailer.