AI in the Media Spotlight

Abstract

The use of AI technology offers many new opportunities for the media sector; in particular, it leads to an increase in productivity and efficiency to convey relevant information to appropriate viewers quickly and accurately. In this keynote, I will show how AI is gradually transforming the content production and distribution chain for broadcasters and media in general. We will start with an overview of AI applications in the media field and the underlying technologies. Then we will go through some projects led or developed by the EBU for the Public Media Services, PSM. To conclude, I will sketch the trend, the limitations and potential evolutions of the uptake of AI in media. The range of AI applications in the mediums of written press, cinema, radio, television and advertising is widespread. To start with the content production and post-production AI is used in video creation and editing, in the written press, for automatic or assisted writing, information analysis and verification. Without being exhaustive, in the broad field of audience analytics, AI can identify the optimal audience for a given content, personalise and recommend the content for a targeted audience or specific user depending on the granularity. From the perspective of accessibility and inclusion, AI plays a predominant role in improving access to content through transcription, translation, vocal synthesis and recommendation. In this context of the raising of AI in the media sphere, the PSM are facing the need to be innovative and transform their value chain to reach the audience better. This can't be performed without keeping the PSM remit which combines a full range of distinctive quality content to fulfil its central mission: inform, educate, entertain. As such, the EBU is leading projects and developing technologies to leverage AI capabilities for media while meeting PSM remit [1]. Firstly, the EBU is leading a project to benchmark AI tools on the market in the context of PSM. As a first step, we are focusing on Automatic Speech Recognition. Therefore I will describe the objective, the metrics and the evolution of the tool. Among other activities related to machine learning and metadata, the EBU is developing a tool to generate high-level tags on written content. Since NLP recently achieved a breakthrough for many applications, we are working on leveraging this technology to produce high-level explainable tags on written contents. As they are called high level, these tags identify properties correlated with several groups of linguistic features like vocabulary, grammar, semantic or formality. Originally designed to detect fake news they can as well feed recommender systems or classifiers. I will detail the machine learning algorithms behind the tool and pave the way of future works.