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.
Every day there’s news of yet another application of machine learning or artificial intelligence in the media and entertainment industries.
A recent survey showed that kids want to be a YouTube star, and it’s no surprise why.
A few days ago I wrote about metadata’s application to distribution. A recent panel discussion at the Rights and Metadata Madness conference outlined some of the challenges and case studies from Rovi, MLB and Viacom outlining their metadata needs and the practices they’ve developed to deal with them.
The article is worth a read, but I’ll highlight the challenge outlined by Michael Jeffrey, VP of market solutions at Rovi:
A feature-length movie with a sports theme and containing content that includes music from other properties can have assets from 20-50 separate entities.
And each of those entities can have restrictions on what the maker of that movie can show, he said, adding that it’s possible you can’t show any beer cans or can’t use an actor in any promotions.
Now let’s add the formatting, duration, and other issues from my earlier post!
Is it as simple as technology vs creative, or is there something more complex in the relationship?
We all hate our cable company but to go as far as say the cable model is unsustainable is probably an exaggeration.
Broadcast production may be declining but the good news is that there are many new original programming sources.
Is the direct to app production approach being taken by Hooked Digital Media truly “personal storytelling”?
Streaming can only replace cable if the Internet infrastructure is ready for it. Apparently it’s not and yet, Netflix’s future depends on it.