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Get ready for a new era of personalized entertainment

New machine learning technologies, user interfaces and automated content creation techniques are going to expand the personalization of storytelling beyond algorithmically generated news feeds and content recommendation.

The next wave will be software-generated narratives that are tailored to the tastes and sentiments of a consumer.

Concretely, it means that your digital footprint, personal preferences and context unlock alternative features in the content itself, be it a news article, live video or a hit series on your streaming service.

The title contains different experiences for different people.

From smart recommendations to smarter content

When you use Youtube, Facebook, Google, Amazon, Twitter, Netflix or Spotify, algorithms select what gets recommended to you. The current mainstream services and their user interfaces and recommendation engines have been optimized to serve you content you might be interested in.

Your data, other people’s data, content-related data and machine learning methods are used to match people and content, thus improving the relevance of content recommendations and efficiency of content distribution.

However, so far the content experience itself has mostly been similar to everyone. If the same news article, live video or TV series episode gets recommended to you and me, we both read and watch the same thing, experiencing the same content.

That’s about to change. Soon we’ll be seeing new forms of smart content, in which user interface, machine learning technologies and content itself are combined in a seamless manner to create a personalized content experience.

What is smart content?

Smart content means that content experience itself is affected by who is seeing, watching, reading or listening to content. The content itself changes based on who you are.

We are already seeing the first forerunners in this space. TikTok’s whole content experience is driven by very short videos, audiovisual content sequences if you will, ordered and woven together by algorithms. Every user sees a different, personalized, “whole” based on her viewing history and user profile.

At the same time, Netflix has recently started testing new forms of interactive content (TV series episodes, e.g. Black Mirror: Bandersnatch) in which user’s own choices affect directly the content experience, including dialogue and storyline. And more is on its way. With Love, Death & Robots series, Netflix is experimenting with episode order within a series, serving the episodes in different order for different users.

Some earlier predecessors of interactive audio-visual content include sports event streaming, in which the user can decide which particular stream she follows and how she interacts with the live content, for example rewinding the stream and spotting the key moments based on her own interest.

Simultaneously, we’re seeing how machine learning technologies can be used to create photo-like images of imaginary people, creatures and places. Current systems can recreate and alter entire videos, for example by changing the style, scenery, lighting, environment or central character’s face. Additionally, AI solutions are able to generate music in different genres.

Now, imagine, that TikTok’s individual short videos would be automatically personalized by the effects chosen by an AI system, and thus the whole video would be customized for you. Or that the choices in the Netflix’s interactive content affecting the plot twists, dialogue and even soundtrack, were made automatically by algorithms based on your profile.