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Online podcast recording platform Riverside It came out with its own version of an end-of-year review like “Spotify Review”wrappedThe feed, called “Rewind,” creates three personalized videos for podcasters.
Instead of sharing stats like how many minutes I’ve logged, or how many episodes I’ve created, Riverside created a fifteen-second laugh track, showcasing a quick succession of clips in which my podcast host and I engaged in pissing each other off. The following video is similar, except it’s a great clip of us saying “mmmm” over and over again.
Riverside then scans the AI-generated transcripts of your recordings to find the single word you said more than any other (we assume it cut out words like “and” or “the”).
It’s a bit ridiculous, but on my own A podcast about internet culturemy co-host and I said the word “book” more than any other word (perhaps this was an aberration due to the subscribers-only “book club” sign-ups…or the fact that the co-host had a book coming out, which we saw components constantly).
Another show on our podcast network, Spiritual drinkssaid “Amanda” more often than any other word (not because they’re obsessed with me, but because they also have a host named Amanda).
In the podcast network’s Slack, we exchanged our videos. There’s something inherently funny about a video of people saying “mmm” over and over again. But we also know what these videos represent: our creative tools are becoming more saturated with AI features, many of which we don’t want or need. The Riverside Rewind points out the uselessness of these tools in and of themselves — why would I need a video of my co-host saying the word “book” over and over again? It’s good for a quick laugh, but there’s no substance.
Although I enjoyed Riverside’s AI recap, its arrival comes at a time when my industry peers are missing out on opportunities to create, edit, and produce new podcasts, thanks to the same AI tools that produced our rewind videos. But while AI allows us to automate some tasks — like liberating our “nations” and dead air — podcasting itself is not mechanical.
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The AI can quickly generate a transcript of my podcast, which is important for accessibility reasons, helping to automate an activity that used to be incredibly time-consuming and tedious. However, the AI is unable to make editorial choices about how to animate audio or video to effectively tell the story. Unlike the human editors I work with, AI can’t tell when casual conversation in a podcast is funny and when it should be cut because it’s boring.
Although dedicated AI audio tools, such as Google’s NotebookLM, have emerged, their ability to act as a creation tool has also seen significant failures recently.
Last week, The Washington Post began publishing personalized AI-generated podcasts about the day’s news.
You can see why this might seem like a “good” idea to profit-hungry executives – instead of paying a team to do the intensive work of researching, recording, editing and distributing the daily show, you could automate it – except you can’t.
The podcast took off Made-up quotes And factual errors, which poses an existential threat to the news organization. According to Semaphore, the newspaper’s internal testing found this Between 68% and 84% of AI-powered podcasts failed to meet publication standards. This seems like a fundamental misinterpretation of how LLMs work. You can’t train an LLM to distinguish between fact and fiction because it is designed to provide the most statistically likely output to the mentor, which is not always the most truthful output – especially in breaking news.
Riverside has done a great job of creating a product that is fun for the end of the year, but also serves as a reminder. AI is creeping into every industry, including podcasting. But in this moment of the “AI boom,” where companies are tinkering with new technology, we need to be able to distinguish between when AI serves us and when it becomes fodder for useless ideas. slope.