Our brains create Musical Memory Templates that are associated with the transition years from teen to adulthood. Somewhere around 16-22 years old. This music informs a style, a sound, and genre that we carry with us for our entire lives. It’s not that you won’t find new music that you love, it’s just a reliable mechanism for releasing the steady hit of dopamine that has you singing out loud in your car, embarrassing your children — a win-win in my book.
Now think about your late high school and early college years. While you may not have known what you wanted to do with your life at this point, there is a good chance that you were identifying things you liked and were good at. Think of these are your learning playlists.
In my case, I hated forced learning and loved inspired thought. I loved turning others on to new ways of looking at things and creative ways of looking at problems. I also was fascinated with multimedia production and graphic design. So, a career that grew into an Instructional Designer job title isn’t too far from that seed.
Nobody ever grows-up wanting to be an Instructinoal Designer, it just happens.If we used that “Musical Memory Template” or in this case, the Learning Memory Template and matched the core elements with emerging technologies, that are rooted in those elements.
Notes and takeaways:
- In the beginning, Spotify had a team of humans who tagged music, much like you’d add meta-tags a video when posting it.
- Spotify trained algorithms to look at Implicit feedback (how many times you listen to a song + what similar people listen to), this is collaborative filtering.
- Natural Language Processing, in its simplest form, is like create a word tag
- None of this works without a large amount of data and a place to put the data –> Data lakes
This, like all ideas, is a collision of ideas that sparked a new idea. These are the primary References and Resources:
https://www.spotify.com/