It’s one of the most common low-stakes annoyances of modern life: At the end of the day, you collapse on the couch and finally have a few minutes to watch one of the dozens of incredible shows or movies you can watch. Thanks to the peak of the television age and the advent of streaming, you’re just rolling. Instead of actually looking at anything, you spend a long night opening the app and aimlessly scrolling through countless rows of identical-looking tiles. you eventually give up and watch office again.
As far as this episode is concerned edge broadcast, we examine why TV and movie recommendations are so complex and whether artificial intelligence can make them better. If Spotify can build unlimited playlists of your favorite music, and YouTube and TikTok always seem to offer something perfect, why can’t Netflix, Hulu, or Max seem to do the same?
It turns out that artificial intelligence can help at least a little. Because models from OpenAI, Google, and others already absorb a lot of information about movies and shows—not just their titles and genres, but all synopses, reviews, recaps, and more from across the web—they can synthesize that information and find connections between titles that were previously hard to find. As the context window gets larger, these models can actually ingest and understand entire movies at once, which opens up entirely new ways of understanding them.
But ultimately, recommendations are a person’s problem. Because we are all human. What you want to see and why you like what you like are far more complex and far more varied than the best models can understand. So the idea of sitting down and turning on Netflix and having the right title displayed instantly isn’t something that’s going to happen anytime soon. So instead of hoping for the best, we’re immediately looking into ways to use artificial intelligence tools to at least get your content faster. Because watching movies is great; scrolling through too much content is grossly overrated.
If you’d like to learn more about everything we discuss in this episode, here are some links to get started:
