The soundtrack for Season One of HBO's Westworld has been released. The 34-song compilation, Westworld: Season 1 (Music From the HBO Series), includes original music by composer Ramin Djawadi along with his covers of Radiohead, the Rolling Stones, the Cure, Amy Winehouse and others. The set includes previously released covers of Soundgarden, the Rolling Stones and the Cure, which appear on an EP featuring selections from the series. A reworking of Radiohead's "No Surprises" hails from the EP as well and the new soundtrack also features Djawadi's interpretations of "Motion Picture Soundtrack," "Fake Plastic Trees" and "Exit Music (For a Film)." Other covers include Nine Inch Nails' "Something I Can Never Have," Amy Winehouse's "Back to Black" and the Animals' "House of the Rising Sun".

With so much music readily available, tools that help a user find new, interesting music that matches his or her taste become increasingly important. In this article we explore one such tool: music recommendation. We describe common music recommendation use cases such as finding new artists, finding others with similar listening tastes, and generating interesting music playlists. We describe the various approaches currently being explored by practitioners to satisfy these use cases. Finally, we show how results of three different music recommendation technologies compare when applied to the task of finding similar artists to a seed artist.

There aren't many saloons where you can get into a decent pistol duel nowadays. But at the Mariposa in Sweetwater, you can walk in, order a shot of bourbon, and straight-up Aaron Burr a robot--all to the strains of Radiohead and Nine Inch Nails, courtesy of a player piano that happens to be ever so slightly out of tune. "In the show, everything is so real, until you look closely. The music is a subtle layer of that." Djawadi is no stranger to scoring an epic HBO drama; he also composes for Game of Thrones.

Remember when artificial intelligence was supposed to be a good thing? When we thought we would, in our old age, each be tended to by a personalised robotic nurse? When we thought that all our jobs would be made obsolete, allowing us to live lives of unbroken leisure? That glorious future might still be on the horizon, but for now AI is rubbish. We live in a world where stupid robots and gormless algorithms are incompetently conspiring to make our lives much more difficult than they need to be.

With the recent dramatic transformations in the world of digital music, a music listener is now just a couple of clicks away from being able to listen to nearly any song that has ever been recorded. With so much music readily available, tools that help a user find new, interesting music that matches her taste become increasingly important. In this article we explore one such tool: music recommendation. We describe common music recommendation use cases such as finding new artists, finding others with similar listening taste, and generating interesting music playlists. We describe the various approaches currently being explored by practitioners to satisfy these use cases. Finally, we show how results of three different music recommendation technologies compare when applied to the task of finding similar artists to a seed artist.