Artificial Intelligence in Entertainment

Across industry verticals, enterprises are gearing to leverage their rich data, and make it work for them, using machine learning and artificial intelligence.



When it comes to Media and Entertainment, the role of data becomes crucial in understanding better the end-consumer – their tastes, preferences, dislikes among others, when it comes reading, listening or viewing new content. Such data insights around the user experience would help in better formulating business strategies, in ensuring marketing to select demographics, and in developing bespoke content creation and curation.


In a constantly changing world, where it is tougher to retain customer interest and loyalty, AI helps in

ensuring that companies adapt to, and tailor content that customers are interested in.


Better Search Experience

The use and application of Machine Learning is already contributing to a better user experience, with recommendation algorithms making relevant content accessible to readers, viewers and listeners. By tagging images and text archives, machine learning can autosuggest copyright-free images for articles.


Machine learning has had a significant impact on sequence to sequence learning where translations are now more precise and adaptable to writing styles, allowing new audiences to discover and connect with such content easily and fluently, contributing to many new use cases, not possible before, breaking new grounds and barriers.


Content Personalization using Machine Learning Models


Artificial Intelligence has positively impacted how content is discovered and engaged with by consumers, as well as how content is created and distributed. Increasingly, Artificial Intelligence and Machine Learning models are enabling that content delivery is customized to the user interests, to ensure they watch entertainment and advertisements that are only of interest to them. In essence, algorithms today are influencing not just what consumers see across platforms, but also how the creative content development is shaping up


Content Personalization is aimed at improving consumer experience as well as enhancing the consumer value, by making them read multiple articles based on their interests, through AI-based algorithms.


Netflix, for instance, deploys an intelligent AI-based workflow management and scheduling application system named Meson. Meson’s capabilities revolve around efficiently managing various machine learning pipelines that generate video recommendations tailored to the interests of consumers.


Beyond Netflix, there are many others who are using machine learning tools to go beyond mere recommendations. For instance, TVision Insights analyzes actual eyes on screen to measure audience attention. Affectiva, on the other hand, is using deep learning to learn from facial expressions and non-verbal cues. Affectiva’s software is being used by Flying Mollusk Studio, a videogame company, to develop a psychological thriller game, where the difficulty levels increase with the level of fear in the user.


Interactive voice content is the forte of Novel Effect, the interactive storytelling app, that uses voice recognition technology, and enables users to read children’s book, with theme music and sound effects to accompany user voices, all contributing to a perfect children’s book experience.


Hyper-personalized Production, Marketing and Advertising


Machine Learning is also impacting not just consumer experience but consumer marketing and sales. With the help of machine learning models, it is possible to identify a target set of consumers who are most likely to be interested in subscribing, and the best time to reach them.


Machine learning algorithms that work with textual, images and video data, can extract language, objects and concepts, and can support content creators as assistants, or become content creators themselves.


Similarly, movie Production Houses have access to large datasets, that can be mined using artificial intelligence to dovetail the type of projects to produce, the distribution mechanisms, and the marketing strategies to reach a specific target audience.


Alibaba’s Luban is an artificial intelligence-based designer that can churn out >8000 banner designs per second, far exceeding the human capabilities. During the Aliexpress Singles Day 2017, Luban created >400 million banners.


IBM’s Watson helped 20th Century Fox to create a trailer for “Morgan” the horror movie. The AI system analyzed and classified input “moments” from visual, audio, and other composition elements from 100 horror movies to create a six-minute movie trailer within 24 hours, that would otherwise have taken weeks for humans to create.


In Conclusion


Going forward, the application of artificial intelligence in media and entertainment will revolve around developing more immersive consumer experiences through closer integration across media platforms, ranging from television to digital videos. Content discovery would be aided by virtual assistants. Therefore, media and entertainment in the future would shift from a “one way” watch-only mode, to becoming fully interactive, enabling consumers to join-in with their digital avatars.


When it comes to content creation, artificial intelligence would help with intelligent suggestions based on writer’s writing style and their topics, leading to overall improvement in quality of the articles.


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