5/5 (7)

Spotify is the largest music-streaming service and offers their customers a catalog consisting of almost all existing music. Next to numerous playlists and various radios, Spotify also offers a customised playlist: Discover Weekly. Every week, Spotify provides their users with a playlist full of new or undiscovered music. How does Spotify know the music that you will like?

Spotify uses three main technologies to recommend music to their users that they will enjoy. The first technology is Collaborative filtering. This technology analyses users’ habits such as which music they listen to, which artists they like and which playlists they follow. The technology matches users to other users with identical habits and recommends music that similar users are listening to.

Spotify also uses Natural Language Processing. This technology scans all written content concerning music on the internet. It will for instance analyse all content written about one artist, scan all the words around this artist’s name and store it in Spotify’s database. When a user listens to this artist, it will recommend a different artist that is described by similar words.

The third technology is Audio modelling. This analyses the music itself and identifies its key characteristics. It then compares this data from different music and matches music that has similar key characteristics. It will thus recommend music that has similar characteristics to their users.

By combining these three technologies, Spotify ensures that it is not only dependent on other user’s habits and written content on the internet. When music is new or unknown, no user data or written content is available. However, Spotify is still able to recommend it due to audio modelling.

Spotify is getting good at making accurate predictions about what users may like. It knows your music preferences even better than you do yourself. What do you think, does Spotify accurately predict music that you like?

 

Sources:

 

https:// www.digitaltrends.com/music/best-music-streaming-services/

http:// blog.galvanize.com/spotify-discover-weekly-data-science/

https:// www.slideshare.net/erikbern/collaborative-filtering-at-spotify-16182818/10-Supervised_collaborative_filtering_is_pretty

https:// notes.variogr.am/2012/12/11/how-music-recommendation-works-and-doesnt-work/

5/5 (4)

A large problem in high crime neighbourhoods is that the police relies on witnesses to report gun violence, but less than 25 percent of the actual gun violence is reported (Faggella 2018). How can the authorities even fight gun violence when it is not reported? ShotSpotter found an interesting solution for this problem.

ShotSpotter is a system that detects gunfire by using acoustic sensors and cameras. The system is capable to identify gunfire and alert the authorities, even when no one calls the emergency services. ShotSpotter claims that they alert the authorities faster and give more detailed descriptions about incidents so that the authorities can respond more efficiently (Shotspotter 2011).

The sound sensors and surveillance cameras are strategically installed within neighbourhoods that are dealing with high crime rates. When a gunshot is fired, the sensors will detect the sound and the cameras will turn towards the direction of that sound. Analysts will confirm if it is the sound of a gunshot and a machine learning algorithm will simultaneously compare the sounds picked up by different sensors. This algorithm then determines the exact location of the gunshot and alerts the authorities. The authorities can log in onto the system, access the surveillance footage and examine the crime scene (Weller 2017; Faggella 2018).

SpotShotter is helping the authorities to reduce their response time since they are immediately alerted after the incident. It is also increasing the number of arrests because the authorities can immediately check the surveillance footage and identify offenders. Furthermore, it helps the authorities to find more gunshot victims. If a victim is alone, the authorities are still alerted even if there are no witnesses (ShotSpotter 2018; Faggella 2018).

In my opinion, this could improve the fight against gun violence. The authorities will no longer only depend on subjective human reports, but can rely on the objective data that this system provides for them.

Sources:

Faggella, D., 2018, AI for Crime Prevention and Detection – Current Applications.  Available at: https://www.techemergence.com/ai-crime-prevention-5-current-applications/ [Accessed: 1 October 2018]

Shotspotter, 2011, SST – ShotSpotter Overview. [online video] Available at: https://www.youtube.com/watch?v=VBxqUBA_br8 [Accessed 1 October 2018]

Weller, C., 27 June 2017, There’s a secret technology in 90 US cities that listens for gunfire 24/7. Available at: https://www.businessinsider.nl/how-shotspotter-works-microphones-detecting-gunshots-2017-6/?international=true&r=US [Accessed 2 October 2018]

ShotSpotter, 2018, Results. Available at https://www.shotspotter.com/results/ [Accessed 2 October 2018]