Cover Story criteria . With user data , that will get even better , you are able to personalise cover artwork based on what is more likely to appeal to a specific user . The same is applicable to content itself .”
Cover Story criteria . With user data , that will get even better , you are able to personalise cover artwork based on what is more likely to appeal to a specific user . The same is applicable to content itself .”
“ It absolutely can ,” agrees Child , “ and it could potentially bring about more timely and more relevant content to viewers , either by matching up more precisely what content a viewer is interested in , or making it easy to find . In the medium term I also hope to see even more of a feedback loop from viewers into choices of what content is being made , which will further improve those choices .
“ Yes ,” says Sherwood . “ This is a major focus area for commercial AI companies who want to help broadcasters monetise the mountains of content that exist in broadcaster ’ s archives , both old and new . Examples include up-sampling old content for higher resolution devices , auto-tagging content for driving higher relevancy to viewers
and advertisers , and even auto-generating schedules for programming .”
“ Many broadcasters have very large archives , and manually searching millions of hours of programmes would be simply impossible ,” admits Karp . “ With the move to IP technology and the cloud , these archives become increasingly digitised , migrating the content in the right formats for the application of ML and AI . “ The recent example is BBC Four ’ s experimental AI and archive programming . AI helps programme makers and schedulers unlock valuable resources and put together a manageable selection in a fraction of the time . According to BBC , ‘ this enabled researchers and schedulers to uncover programmes they may never have been able to find , and will broadcast a selection that haven ’ t been seen in years ’.” ARCHIVE . “ AI will help reduce the number of mundane tasks in the broadcast space so that staff members can dedicate more time to creative , impactful ones ,” suggests Martin . “ More than that , this emerging technology will deliver enriched information that will make it easier for them to do their jobs . Through tools like facial and voice recognition , content creators and producers have access to a more
“ However , the benefits of AI go far beyond just automation .” - Stuart Almond , Sony Professional Solutions Europe meaningful set of data that they can use to create richer , more compelling storytelling .”
“ Absolutely , yes ,” says Mukherjee . “ AI will be contentaware , so it can go a long way in content curation and helping schedulers quickly identify the right content . This may require enhancing existing workflows to AI-assisted ones . Interra Systems is developing technologies for automatic classification and tagging of content using machine learning .”
“ Once broadcasters can acquire all of the sources they need in real-time , they will face a fundamental challenge that only AI will be able to solve : it would be a treasure trove of content but an overwhelming amount of video to manage ,” suggest Jacobi . “ Using AI , broadcasters will automatically filter and develop refined levels of metadata to accelerate the curation and programming of all of these new live feeds . Now they ’ re able to provide dynamically created , sportspecific , team-specific and athlete-specific live channels , interweaving authentic , hyper-local live coverage , creating a whole new level of tailored viewing experiences .”
“ In theory of course , after you collect the data you must harmonise it ,” says Guillemot . “ However , it is very difficult to do , so , in theory it could work but it would take an enormous amount of work . Docherty is another in the ‘ Yes , definitely ’ camp . “ AI can be used to understand the content better , and provide information about what types of users will be interested in what specific content . It can also be used to determine when this content is most likely to be watched , which can be used to inform scheduling .” LEGACY . “ It certainly could do this ,” says Morgan . “ One trend we are finding at the moment is broadcasters looking to monetise archives by making legacy content available over-the-top . There are trends where the young generation are suddenly discovering old programming and AI could certainly help surface other items in a series or similar content .”
“ There is a clear role for AI in mitigating the huge complexity of programming ,” states Almond . “ It can take up to 18 months from the inception of a creative idea for a programme to be broadcast , during which time the climate in which the show is being broadcast in may have changed drastically . One way to ensure the content remains relevant is to utilise
AI to help support and make use of complex data analysis to drive better future-looking decisions .”
We should question if we want AI to determine 100 % what we want to watch .” – Petr Peterka ,
Verimatrix
“ Equally , AI can help broadcasters make sense of the vast amount of legacy data they already have at their disposal . This data can be analysed and used to predict popular programmes , including when best to broadcast to increase ratings and many other factors that should be considered when making a programme and delivering it to the right audience at the right time .”
It is possible , but broadcasters need to ask themselves the question as to why they want to do this in the first place ,” advises Wood . ‘ What is the business value ? What is the commercial value ? Are they adopting AI just to jump on the bandwagon ?’”
DEPTH . According to Dawes , one challenge for broadcasters is actually understanding the breadth and depth of their archive . “ In the past , fairly limited metadata was stored with content , and it would be an enormous task to go back and bring that metadata up to today ’ s standards by hand . Technologies like TiVo ’ s entertainment technology graph — that can automate the production of content metadata — are key to providing an enhanced set of information that can be used to open up the archive and fuel new business models .”
“ AI engines with object and voice recognition already exist to automate the process of tailoring clips to the appropriate distribution methods such as streaming to cell phones , tablets , television and social media channels ,” advises Shen . “ The audience and the presentations differ , depending on the content and where it ’ s being viewed . Additionally , AI can make this process simpler through creating metadata to archived material that does not contain the granularity of metadata as well as creating data for live video in real-time . Right now , producers hand-craft the multiple versions required for a TV show . In the ‘ smart studio ’ AI engines will be able to automate the assembly of the material and deliver it in the most effective way to the target audience .” HUMANS . In terms of scheduling , it has been said that humans have nuance , taste and judgement – the qualities of which no algorithm or machine can replace - yet . To what
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