I agree that this is a great way to drive innovation in Capital One. We are already seeing the huge tech companies/platforms like Microsoft, SalesForce, Google, etc. providing APIs for free to encourage developers to build on their platforms. I know that Salesforce and Microsoft both have developer conferences regularly. To take this further, I wonder if Capital One can provide additional incentives to developers to encourage them to build more apps.
I think that an important point of 3D printing for adidas that you made is that they can potentially reduce shipping costs and times. They can integrate their 3D printing movement into their in-store experience as well, using it as both a cost reducing and advertising tool. I wonder how this will change the shoe supply chain.
Your point about honesty being detected in the application by a machine learning algorithm is very interesting. I wonder if they could use data from previous applications of users that paid back their loans as input to a deep learning algorithm that can help detect application patterns that make them more likely to not default. On the flipside, I wonder that if this market becomes big enough, someone can create an algorithm that tries to “game” the system.
Very interesting to apply additive manufacturing for construction. I agree that it can help solve a lot of the key issues that you pointed out, but my question would be: at what cost. Construction companies are extremely cost conscious, and I wonder if getting additive manufacturing into the mix would make their project unprofitable. Perhaps they can start small with certain pieces that they know they can save money on.
To your point on the education gap, I wonder if Tencent can help solve this by partnering with local Chinese universities and research facilities that are focused on Machine Learning. With regards to innovation, I am also struggling to understand how Tencent can use machine learning to drive large leaps in innovation vs. just marginal efficiency and customer experience improvements for its existing and future tools.
I think it is very interesting that they recommend furniture based on visual matches. I wonder if they can extend their deep learning algorithms to take into account designed feedback. For example, designers could look at the images of the rooms and make recommendations on what furniture they think would fit the setting. The algorithm can then take this data and try to “learn” how to design a room just like a designer would. The issue here might be the same one that you pointed out: how to get designers onboard?
I wonder if there is an ethical reason why machine learning is not used to generate a credit score. The company would have to explicitly forbid the algorithm from taking things such as gender, race, height, and other personal information into account when doing the calculation. Further, it would seem that Credit Scores have to be easily explained. I wonder if the “black box” nature of some machine learning algorithms would make this difficult.
Interesting to see crowd-sourcing for something creative like movie scripts. I have heard that other companies like Netflix are using data from users to determine what sort of TV shows to create (genre, cast, themes, etc.). I wonder if this data-driven approach has proven to be more effective than the open innovation approach, and could be why Amazon decided to shut the open innovation project down.
I also wonder if there is a legal risk to Amazon if they accepted a script that potentially borrowed ideas from a book or other IP that they were not aware of.