Through digitalisation mass individualisation has become possible. In the future this will lead to a whole new kind of media – Individual Media, where new content is created for each user.
The Future of How We Understand Information
Today we are facing challenges on an unprecedented scale. To solve these complex problems we need to improve the way we understand information.
In history of media there is a trend happening that will have a lasting impact on how we consume media. In the beginning, humans passed information along through speaking, in a one-to-one relationship. This meant that the information was always customised: A speaker reacts to his audience and adapts accordingly. Mass media, with its a one-to-many relationship, started with the invention of printing and continued through radio and TV. The medium evolved, but the character stayed the same: A large amount of people all getting the same information, making customisation impossible.
This is changing now. When digitalisation started, media content was carried over without much change. But since a few years, we are starting to see customisation happening in a lot of different areas. Netflix recommends series to you based on your previous binge-watching sessions and Spotify creates a “Mix of the Week” for you, adapting to your taste in music.
Currently, customisation only means filtering content based on a user’s preferences. In the future it is very likely that Artificial Intelligence will start creating content specifically for each user – we call this “Individual Media”. Examples of it are already happening: AIs are able to create new songs, imitate bands and transform the style of one painting onto an other. There are games and stories adapting to your reaction. And the acclaimed series Black Mirror has shown what an individualised horror game might look like.
What if Spotify will soon not select songs for you based on your taste, but compose entirely new ones from scratch?
Developers and designers try to build tools to help people solve complex problems. They are trying to build bicycles for the mind – machines that take our strength and help us apply it in more effective ways, thereby amplifying it hundredfold.
We, as a global community, are currently facing complex problems of unprecedented scales. To find solutions for them, we need to use all mental strength that we can get. Solving problems starts with gaining comprehension of the available information – a task that has become increasingly difficult.
In recent years there has been a drastic expansion of the amount of information within reach.
Unfortunately, this has also had some negative consequences: Human attention has become our scarcest resource, because of an overload of information. And usually the available information is not connected, so finding related information to improve your understanding is hard.
Information Overflow and Unstructured Information are the two problems, that our new kind of information browser is trying to solve. We do this by applying the concept of Individual Media: We let the user decide what to spend time on and offer extra information inline. This helps the user gain a deeper understanding without loosing context.
When reading about a topic we often find way too much information, but we are not sure what to spend our precious time on. The Abstraction Slider enables the user to gain more rapid comprehension: It allows you to choose a level of abstraction – you can choose the amount and detail of information presented.
The Abstraction Slider does not simply throw out parts of the text, it restructures and simplifies the language and uses abstractive summarisation to condense the information in the best way possible. Additionally it adjusts the summarisation based on your personal profile, behaviour and context.
An author doesn’t know who exactly will read their text. She has to make many assumptions about the readers: She can only guess what they already know, what they find interesting or hard to understand and what they will disagree with.
Content Augmentations fixes this, by allowing the reader to get into an interactive dialog with the text.
The AI is a knowledgeable assistant that can clarify sections, elaborate on parts that seem interesting and more.
This allows the reader to get a deeper understanding about the information. And because it all happens inline he doesn’t have to leave the page.
Personalised feeds, highly targeted ads, recommendations and predictions for the user’s behaviour are technologies already in use. They have led to what is known as the „Filter Bubble“ – users finding themselves in echo chambers of their own interests and opinions.
We think that Individual Media is a development that is inevitably going to come. It is important that we are aware of the consequences that could follow.
When applied right the concept of Individual Media can help to improve personal comprehension. But we also need to increase the shared comprehension of information, making sure collaboration continuous to be possible.
Individual Media could amplify the effect of the Filter Bubble massively. If the AI is trained to abstract or augment the content skewed towards certain political views or controlled by economic drivers, the implications might be severe.
It is the responsibility of engineers, designers, creators and all of us to not underestimate this change in the nature of media and to put the needs of humans in center of the development of new applications.
These duties begin now.
How can we make sure Individual Media won’t be abused?
Would you listen to a personalized song made by an AI?
Will Individual Media grow or pop the Filter Bubbles of today’s world?
What will happen if no two people read the same text anymore?
When will we notice the impacts of Individual Media?
Who is responsible if the augmentation doesn’t reflect the author’s intent?
A project by Nikolas Klein, Christoph Labacher and Florian Ludwig
We submitted a paper about this topic to the altCHI conference ’17.
You can read the (unpublished) submission paper here.
Supervised by David Oswald and Florian Geiselhart
Article in Abstraction Slider: Wikipedia, 7 Aug. 2017
Article in Content Augmentation: NY Times, Nov. 29, 2016