I really enjoyed reading this and learning about how machine learning appears to be a proven approach in improving retention rates at universities. I agree that data privacy will be a contentious point for this to be adopted at other universities. But I also wonder how and at what cost UA is using machine learning. Whether it is all in-house vs. a third-party vendor. And if it’s in-house, how much they are investing in this initiative and if this might be a good software business opportunity to create solution for the market if one doesn’t already exist.
Interesting topic and insight! I agree that this could be super helpful to fine dining restaurants to help them optimize otherwise inefficient process and better estimate customer preferences. However, I wonder what solutions already exist to help restaurants improve their business and, if so, at what cost. Is it or will it be feasible for one restaurant that already has extremely low margins, if profitable, to spend more money for something that has long-term benefits. I don’t think fine dining restaurants optimize for their bottom line so I definitely think the use of machine learning in this industry is a big possibility.
One question I had is about the Speedfactories and if this applies to 3D printing of midsoles or the production of other parts of the shoe. Because if its the latter, then the 3D printing production process will still need to catch up in terms of time and cost to match the effectiveness of the new Speedfactories. Are there a lot of Carbon 3D printers at these Speedfactories. I would love to learn more about the actual economics of using Carbon 3D printers vs. traditional production processes.