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Could "programmatic" technology used in online advertising also be used to automate and personalize the distribution of cultural and editorial content?

Interactive drama done with BBC Radio

Called EmotionScan, the online feature linking facial expression to personal finance management skills was developed by the Bank of New Zealand in partnership with a psychologist, Dr Stuart Carr, and Swiss emotion software firm nViso.

"Interactive narrative systems attempt to tell stories to players capable of changing the direction and/or outcome of the story. Despite the growing importance of multiplayer social experiences in games, little research has focused on multiplayer interactive narrative experiences. We performed a preliminary study to determine how human directors design and execute multiplayer interactive story experiences in online and real world environments. Based on our observations, we developed the Multiplayer Storytelling Engine that manages a story world at the individual and group levels. Our flexible story representation enables human authors to naturally model multiplayer narrative experiences. An intelligent execution algorithm detects when the author's story representation fails to account for player behaviors and automatically generates a branch to restore the story to the authors' original intent, thus balancing authorability against robust multiplayer execution."

"Dynamically Inserted Ads In recent years, ad technology has allowed for ads to be targeted and dynamically inserted at the time of file request. The ad server determines the best ad to serve to the listener at the time of request. In a podcast consumed online, ads may be inserted into a file that is being progressively downloaded at designated ad breaks . Some publishers may count this dynamic ad serve as an "impression" without confirming ad delivery. T he metrics in this document focus on confirming that the ad was delivered. S erver log s can confirm that the enti r e ad file was downloaded , but the process for counting a served ad can only determine that ad file was sent ."

We show that easily accessible digital records of behavior, Facebook Likes, can be used to automatically and accurately predict a range of highly sensitive personal attributes including: sexual orientation, ethnicity, religious and political views, personality traits, intelligence, happiness, use of addictive substances, parental separation, age, and gender. The analysis presented is based on a dataset of over 58,000 volunteers who provided their Facebook Likes, detailed demographic profiles, and the results of several psychometric tests. The proposed model uses dimensionality reduction for preprocessing the Likes data, which are then entered into logistic/linear regression to predict individual psychodemographic profiles from Likes. The model correctly discriminates between homosexual and heterosexual men in 88% of cases, African Americans and Caucasian Americans in 95% of cases, and between Democrat and Republican in 85% of cases. For the personality trait "Openness," prediction accuracy is close to the test-retest accuracy of a standard personality test. We give examples of associations between attributes and Likes and discuss implications for online personalization and privacy.

All life could be here, in a snapshot of this very moment. But as quickly as the film fades, the moment is lost. Play it again, and new recordings are collected from the world's online archive. What follows has the potential to fascinate, or could well be routine - but each time is different."

This television uses facial recognition technology to locate its viewer, then rotates its screen to face them.

Netflix is to consider new interactive storytelling technology for their TV dramas

"Separate voice from music out of a song free with powerful AI algorithms"