AI: New Technology Could Bring New Life to Past Media Programming

Content is King. As consumers, it’s what we crave. As communicators, it’s what we create. From radio, cable and satellite to podcasts, apps and Roku, the appetite must be fed. More platforms also means more competition and the need for what is heard, watched, read, broadcast or streamed to be compelling and produced quickly and efficiently.

Logan Ketchum writes about this dynamic in his new article, “Is ‘AI’ Radio and TV’s Audience Engagement Gold Dust?” published in Radio & Television Business Report. In this piece, he acknowledges not the ongoing need for brand new creative programming but, more interestingly, how archives of existing content – from sports to history to crime – might be repurposed using Artificial Intelligence.

What if, for example, a producer wanted to quickly pull together a retrospective on a titular event in U.S. history? Digital archives would most likely exist but how onerous would they be to comb through? With AI in place, machines could go through content using automated tools such as audio transcription, facial recognition, scene detection, and other cognitive engines to find relevant content nuggets in hours or minutes, rather than weeks or months. For true crime programming, by way of another example, the ability of producers to go deeper and more comprehensively into past news coverage could make their shows even more compelling with less reenactments and more real-world footage.

This could apply to the news business as journalists, like the rest of us, are expected to work faster and cheaper. Could AI help fact checking, research and create other short cuts – in particular within time frames that could not possibly be accomplished otherwise?

With AI what’s old could be made new again – putting archives that are collecting digital dust back to work in new, creative and interesting ways. It’s an intriguing thought as content creators continue to look for ways to satiate media’s (and the public’s) insatiable demand for personalized programming.