Susan Cho's Profile
Such an interesting post, Isabella! To your question, “After all, if the AI algorithm created the underlying melody, is it copyrightable?” I feel that it definitely cannot be copyrightable. If the algorithm is parsing out popular chord sequences, melodic patterns, and/or beats from existing songs to create music, it seems to me to be a form of plagiarism… Though it’s true that artists are always inspired by each other in creating music, if the algorithm simply cannot generate music without being fed existing songs, I feel it’s hard to argue that it’s an “innocent” form of plagiarism as opposed to artists, who strive to come up with original music rather than piecing together parts that “work” from existing songs.
This was such a fascinating post — thank you for writing, Saad! Getting a glimpse into how Dr. Ming viewed AI, its potential for use as a tool for empowerment and good, is really inspiring. Applying AI to humanitarian crises struck me as such a novel idea as most people tend to try to find commercial applications for AI to improve existing workflows and processes. And, though ingenious, it was personally heartbreaking to read about the refugee application… I can’t imagine the hope and desperation people felt as they scrolled through countless faces of people, trying to identify those that looked like their lost loved ones. Gave me so much to think about!
Thanks for the post, Elizabeth! As a millennial, Venmo is basically synonymous with peer-to-peer money transfers so I had never doubted its sustainability until now! The two apps are also owned by giants in the fintech industry (Square owning CashApp and PayPal owning Venmo) so it’d be fascinating to see how each player tries to leverage its existing customer base to bolster their respective app’s ecosystems and attempt to build competitive moats (via network effects) for an easily replicable service offering that cannot be differentiated based on functionality.
Laura, first of all, 10/10 title.
Despite my love for wine, I’ve never actually used Vivino! This sounds like such a fun solution for people who are intimidated by conversations around wine. Firstly, it allows consumers to efficiently discover their own preferences about wine (given that the app tracks similarities in descriptions and tasting notes for well-received bottles), which then empowers the consumer to be able to explicitly talk about their wine preferences. With constant usage of the Vivino app, users will also become more sophisticated wine consumers as they begin to correlate the flavor descriptions per the app to the wine that they are actually tasting. You’ve inspired me to try it out!
Hi, Kate — thanks for such a fun post! It was particularly interesting to think about the tension between Hinge’s key value proposition as a high user turnover app (due to high success rates) versus its need to maintain its network effect, which, traditionally, involves high user retention in some form. I’m excited to see how this plays out as Hinge continues to enter new geographical markets!
Yannik, this was an awesome read! I used Uber/Lyft on a daily basis when I worked in consulting and am still a frequent user of it now so I love asking the drivers about how the app works for them. One of the fascinating things I heard was that if a top-rated driver is on their way to pick up a non-top-rated user and a top-rated user subsequently requests a ride, the app will cancel the original ride to the non-top-rated user and redirect the driver to the top-rated user instead. I understood this as the app ensuring that their top-rated users have the best service from their best drivers (not necessarily to incentivize users to be better riders, since most users are unaware of this mechanism) but reading from your post, it strikes me that it may also be a cost saving mechanism to link its best drivers and users to “minimize the number of variables” for both parties and curate highly efficient rides to increase capacity.
Lina, this was fascinating to learn about — would love to visit a location sometime! For myself and a lot of other friends, conveyor belt sushi has not held up the best impression as a “good” sushi dining experience due to the fact that we’d have to sit and wait without any guarantee of our favorite dishes passing by on the belt and that the sushi may lose its freshness from sitting on the belt for too long. This solves both of those problems perfectly — what a great example to illustrate how big data/analytics can address everyday problems in a brick-and-mortar restaurant, not just in labs and high-tech companies.
Thanks for the post, Julien — this was super interesting to learn about! The figures you’ve mentioned in here with regards to cost saving were really impressive themselves but what really surprised me was how early Visa was investing in this technology (1993)! I’d also love to do some personal research around what other data attributes are being taken into consideration by the fraud scoring system; back when I was an auditor, there were some seemingly random tests we had to do at the individual transaction level, which I found intriguing (e.g. a transaction that involves a 5+ figure amount that starts with “1” because a significant number of fraudulent transactions begin with the number “1” based on some psychological theory) — would be funny/interesting to see if Visa employs some of this too.