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Distribution Machine Learning

AI, Machine Learning and Distribution Content Metadata

Every day there’s news of yet another application of machine learning or artificial intelligence in the media and entertainment industries.

Well, from a couple of days of reading and email newsletters, but there is quite a focus.

MESA Alliance quotes Deluxe Entertainment Services Group chief product offer Andy Shenkler as saying:

“AI is obviously playing a fairly broad role, especially with the areas that we at Deluxe are working on,” he told the Media & Entertainment Services Alliance (MESA) in a phone interview. That includes “everything from the post-production creation process, localization” around advanced language detection and auto translation – “and then even down into the distribution side of things,” he said, noting the latter was “probably the least well-known and discussed” part of the equation.

That article goes on to talk about who’s technologies they use and how they use it to assign metadata to incoming assets. Speaking of Content Metadata (in this case about finished content, not for use in production) Roz Ho, senior vice president and general manager, consumer and metadata at TIVO, writes in a guest blog at Multichannel News:

Not only does machine learning help companies keep up with the tsunami of content, it can better enrich metadata and enable distributors to get the right entertainment in front of the right viewers at the right time.

Machine learning takes metadata beyond cast, title and descriptions, and enables content to be enhanced with many new data descriptors such as keywords, dynamic popularity ratings, and moods, to name a few.

Liz finishes with a short dissertation on how these machines, and people enhanced by them, will be the direction we take in the future.

And out of CES some headlines:

CES 2018: Consumer Technology Association Expects Major Growth for AI in 2018

CES 2018: AI Touted Heavily by LG, Samsung, Byton on Eve of CES

It seems like every day there is news yet another application of Machine Learning (AI) into the Media and Entertainment space, either in production – where it is helping decide what goes in to production as well as helping in production – through to helping people find more appropriate content.