When it comes to using machine learning for the purpose of music discovery, few people know as much about it as Brian Whitman. A decade ago he co-founded a music data company known as The Echo Nest, which was the acquired by Spotify last year for $100 million, thus landing Whitman his current position as a member of Spotify’s product team. With the music streaming market becoming so competitive, Spotify has turned to machine learning to help give it an edge against the like of Apple Music and maintain its position as the market leader. This is according to Whitman himself who recently sat down with Fast Company to discuss Spotify’s plans for the future. 

Brian Whitman landed at Spotify just in time. The MIT Media Lab alum and machine listening expert joined the product team at Spotify early last year when the streaming giant dropped a reported $100 million to acquire The Echo Nest, the music data company he cofounded a decade ago. Since his time at MIT, Whitman, along with cofounder and fellow PhD Tristan Jehan, has focused obsessively on the intersection of big data, artificial intelligence, and music, using that sweet spot to try and redefine music discovery in the age when songs flow freely like water and new artists pop up by the hour. Today, he’s sitting across from me in a conference room in Spotify’s New York headquarters showing me what his team has been building for the last few months. It’s called Fresh Finds. At first glance, it’s just another playlist—it could easily be one of the collections hand-curated by Spotify’s 32 music editors, or one of its 75 million users for that matter. But Fresh Finds is different: The weekly playlist is generated using a chunk of the predictive big data technology that Whitman and his team at The Echo Nest brought with them to Spotify last March. The mission of Fresh Finds is to identify under-the-radar artists that are generating buzz online and surface the ones most likely to break out.