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Q&A: Roland Sars, Media Distillery

Roland Sars, CEO of Media Distillery, talks about the application of AI to improve the user experience of TV platforms

What do you see as the main ways in which AI can be used to help broadcasters and TV operators?
Nowadays, broadcasters and TV operators are faced with the change in consumer behaviour from linear television to the more extensive use of replay environments. If they want to remain relevant, they need to act on this change and focus on optimising their replay platforms and VOD platforms. We believe that AI, being much quicker than regular technology or human effort, will enable broadcasters and TV operators to optimise and continuously adapt their platforms to satisfy new-born consumer demands.

How does Media Distillery’s Deep Content Understanding platform work?
Media Distillery’s Deep Content Understanding platform comprises a wide combination of Machine Learning methodologies to recognise every visual and audial aspect of video. These methodologies include speech detection, logo detection, face recognition, object recognition, topic detection and OCR recognition. By using a synergistic approach of combining the different technologies, we can provide highly accurate results.

What benefits can the metadata generated by this technology in real time bring to operators?
The benefits of this are that broadcasters and TV operators will learn and know what is inside their video content: who is on screen and when, what a programme is about, or when a programme started and ended. These forms of metadata are vital for TV operators to be able to optimise their replay environment. Essentially, the metadata helps broadcasters and TV operators enhance their platforms more like Netflix and YouTube, for example.

How do consumers stand to benefit from the new services that this technology can support?
Consumers will benefit as the technology enables TV operators and broadcasters to resolve some of the most pressing consumer frustrations in relation to watching television programmes in a replay environment. These frustrations include missing out on the first couple of minutes of a programme because they were not recorded, having to sit through a few minutes of television advertisements, on top of online advertisements, before the programme finally starts because the programme started later than scheduled, or wanting to binge watch a programme but not being able to smoothly transition to the next episode. Furthermore, the technology, and the real-time metadata specifically, will help TV operators and broadcasters offer a number of new services in their replay environment for a better consumer experience. For example, they can offer personal recommendations, short clips based on a consumer’s favourite topic or personalised and in-video search options.

To what extent does this type of application reflect changing viewing patterns among consumers?
Linear television is losing ground to video-on-demand: consumers increasingly watch video content when it suits them, not when the television guide tells them to. Yet the replay environments of television operators have not been optimised and are not user-friendly at all. As a result, software companies, such as Netflix and Amazon, which have a strong focus on consumer experience, are taking away consumers from the traditional television operators. By using AI applications to offer an optimal consumer experience in their replay environments, television operators will be able to compete with the software companies that are conquering the media industry.

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