AI Revolution: Spotify's LLM Breakthrough, Apple's Smart Device Leap, and Fintech's AI-Driven Future #83b
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AI success story - How Spotify Enhances Content with Large Language Models
When your music streaming app suggests amazing songs one after another, isn't it fantastic? Or the time the app discovers a true crime podcast for you even if you can't recall the name of the one your coworker suggested?
The secret ingredient that drives the search and recommendation features of all streaming platforms are annotations, which are unique tags that enable style, genre, topic, and any other pertinent information to be applied to each audio recording.
Adding annotations in a scalable and economical manner is a challenging issue for Spotify, whose library comprises over 100 million songs, podcasts, and audiobooks.
Additionally, the AI-based recommendation tool wouldn't function as well without them. So, how might AI be made more effective? Employ AI!Spotify created a pipeline that feeds unlabeled audio files to an LLM, which "reads" the input content and applies pertinent tags in order to scale the corpus of annotations.
The pipeline's human-in-the-loop component allowed human QA specialists to review the machine-generated annotations. Consequently, Spotify was able to increase the number of annotations by ten times and the productivity of human annotators by three times by using the LLM-based tag generation method.
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