Jarno M. Koponen
Jarno M. Koponen is working on intelligent systems and human-centered personalization. He currently is product lead at Yle, one of the leading media houses in the Nordics.
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A new hope: AI for news media
AI on your lock screen
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.
Netflix is experimenting with different episode orders for ‘Love, Death & Robots’
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.
Netflix is pursuing more interactive content, including, maybe, a rom-com
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.
Personalized smart content is coming to news as well. Automated systems, using today’s state-of-the-art NLP technologies, can generate long pieces of concise, comprehensible and even inventive textual content at scale. At present, media houses use automated content creation systems, or “robot journalists”, to create news material varying from complete articles to audio-visual clips and visualizations. Through content atomization (breaking content into small modular chunks of information) and machine learning, content production can be increased massively to support smart content creation.
Say that a news article you read or listen to is about a specific political topic that is unfamiliar to you. When comparing the same article with your friend, your version of the story might use different concepts and offer a different angle than your friend’s who’s really deep into politics. A beginner’s smart content news experience would differ from the experience of a topic enthusiast.
Content itself will become a software-like fluid and personalized experience, where your digital footprint and preferences affect not just how the content is recommended and served to you, but what the content actually contains.
How is it possible to create smart content that contains different experiences for different people?
Content needs to be thought and treated as an iterative and configurable process rather than a ready-made static whole that is finished when it has been published in the distribution pipeline.
Importantly, the core building blocks of the content experience change: smart content consists of atomized modular elements that can be modified, updated, remixed, replaced, omitted and activated based on varying rules. In addition, content modules that have been made in the past, can be reused if applicable. Content is designed and developed more like a software.
Currently a significant amount of human effort and computing resources are used to prepare content for machine-powered content distribution and recommendation systems, varying from smart news apps to on-demand streaming services. With smart content, the content creation and its preparation for publication and distribution channels wouldn’t be separate processes. Instead, metadata and other invisible features that describe and define the content are an integral part of the content creation process from the very beginning.
With smart content, the narrative or image itself becomes an integral part of an iterative feedback loop, in which the user’s actions, emotions and other signals as well as the visible and invisible features of the content itself affect the whole content consumption cycle from the content creation and recommendation to the content experience. With smart content features, a news article or a movie activates different elements of the content for different people.
It’s very likely that smart content for entertainment purposes will have different features and functions than news media content. Moreover, people expect frictionless and effortless content experience and thus smart content experience differs from games. Smart content doesn’t necessarily require direct actions from the user. If the person wants, the content personalization happens proactively and automatically, without explicit user interaction.
Creating smart content requires both human curation and machine intelligence. Humans focus on things that require creativity and deep analysis while AI systems generate, assemble and iterate the content that becomes dynamic and adaptive just like software.
Sustainable smart content
Smart content has different configurations and representations for different users, user interfaces, devices, languages and environments. The same piece of content contains elements that can be accessed through voice user interface or presented in augmented reality applications. Or the whole content expands into a fully immersive virtual reality experience.
In the same way as with the personalized user interfaces and smart devices, smart content can be used for good and bad. It can be used to enlighten and empower, as well as to trick and mislead. Thus it’s critical, that human-centered approach and sustainable values are built in the very core of smart content creation. Personalization needs to be transparent and the user needs to be able to choose if she wants the content to be personalized or not. And of course, not all content will be smart in the same way, if at all.
If used in a sustainable manner, smart content can break filter bubbles and echo chambers as it can be used to make a wide variety of information more accessible for diverse audiences. Through personalization, challenging topics can be presented to people according to their abilities and preferences, regardless of their background or level of education. For example a beginner’s version of vaccination content or digital media literacy article uses gamification elements, and the more experienced user gets directly a thorough fact-packed account of the recent developments and research results.
Smart content is also aligned with the efforts against today’s information operations such as fake news and its different forms such as “deep fakes” (http://www.niemanlab.org/2018/11/how-the-wall-street-journal-is-preparing-its-journalists-to-detect-deepfakes). If the content is like software, a legit software runs on your devices and interfaces without a problem. On the other hand, even the machine-generated realistic-looking but suspicious content, like deep fake, can be detected and filtered out based on its signature and other machine readable qualities.
Smart content is the ultimate combination of user experience design, AI technologies and storytelling.
News media should be among the first to start experimenting with smart content. When the intelligent content starts eating the world, one should be creating ones own intelligent content.
The first players that master the smart content, will be among tomorrow’s reigning digital giants. And that’s one of the main reasons why today’s tech titans are going seriously into the content game. Smart content is coming.