Karen Nyawera's Profile
Loved, loved, loved this post! I also like your catch phrase – “Is Spotify singing to us what we want to hear?”
I think you raise an interesting question – given the entrance of large tech companies in this space, such as Apple Music, how can Spotify maintain a technical edge in its personalized recommendation service? I think to push the question further, we have to ask how does Spotify create a sticky customer? Can the recommendation service enhance or deter the process of locking in customers? I think that it can – through the point you raised about copying the Netflix strategy and vertically integrating so that it can use its understanding of customer music preferences to create its own content.
For teachers, by teachers! Thank you Sam for a great article. It seems like TPT is a great resource that teachers can access to augment their existing resources and improve the quality of teaching in the classroom. Having said that, it was great discussing in class about the adoption rate of TPT. When I read your article, I was worried that there might not be enough teachers using the platform, so it was great to hear that it is widely used albeit to different degrees.
I am curious whether you think that machine learning might provide a competitive advantage for C.H. Robinson. Naturally, I wonder whether other freight transportation and logistics businesses have also implemented machine learning and to what extent. Is C.H. Robinson a first mover in this industry with respect to applying machine learning to better predict shipments. Instead of risks, I think that there are opportunities in this industry for a business that is better at the prediction game given the continued growth of online shopping and the associated returns. I think that you might also enjoy reading the article by Hermione Granger – “Returns – Climbing out of the e-commerce landfill”.
Great piece Jeannie! I love music and I think that this is an interesting perspective on the future of music. At the core of their application of machine learning is the desire to improve the customer experience, as a result create sticky customers. As a Spotify user, I do appreciate the “Discover Weekly” feature, where I have found songs and artists that the algorithm has matched very well for me, but given the extremely personal and emotional aspect of music, I do wonder about the privacy concerns. One of the things that I have noticed from using “Discover Weekly” is that the algorithm seems to rank/rate recent music listened to highly, thereby recommending songs that are in line with your current emotional state. Having said that, I can imagine situations where the Spotify algorithm is picking up on a user’s emotional distress and amplifying that distress by recommending even more angst-filled songs. Similar to the issue that occurred when Target predicted that a teen girl was pregnant before her family knew – I can imagine among many different outcomes, a situation where Spotify could predict suicide or increase the likely of suicide through its song recommendations. I guess the question that I am trying to get to is how and when will the machine know that its recommendations though performing as expected are having negative outcomes beyond the limits of the application.
It is interesting for me to see a traditional industry like mining adopting next-gen technologies in their operations. It seems from the paper that you might think that instead of going for low-hanging fruit, the company should consider taking a bigger leap with its applications of machine learning. Without knowing too much about the industry, one of the applications that I wonder about is using machine learning to first identify gold deposits, and then predict reserves and/or mining pathways that would optimize for costs and revenues.