The subject of AI in media production is examined by Michael Armstrong in his paper ’Realising Additional Value from Linear Content Using Automation: how metadata and templates enable media personalisation and user interaction’ and by Rasa Bocyte in the paper ’Trans-Vector Platform: Optimised Distribution of Video Assets Across Digital Channels’.

For decades, broadcasters have been producing linear programmes, such as news, magazines or documentaries, which contain valuable audio-visual information about a vast variety of individual topics. The problem is that these individual topics are often neither addressable nor findable.

Could AI and machine learning, segment or chapterise this archived material so it would be re-usable in the interactive digital world? Might AI even be able to re-edit it into personalised media? We look at a fascinating project which is doing all this and more. Improvement is still required, especially in the editorial challenges for AI, of creating re-compiled media, but public-facing trials are underway and generating much interest.

AI in media production

AI in media production

Also key to the re-use of these re-purposed assets is the recognition of the diversity of today’s video delivery platforms, in particular social media. AI can be used to cleverly target particular content offerings across platforms according to: predictions of audience interests, trending stories, particular localities, anniversaries, etc - all achieved through news scanning and on-line trend monitoring. Content can also be automatically adapted to suit the style and culture of each platform. Join us to hear from an ambitious European project which is seeking to optimally craft and distribute video across a diversity of channels.

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