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Really great post! Enjoyed reading about open innovation in this context. I think one of the key risks around open innovation is losing sight of the original intent of the leaders. Things can get muddled as you open up with open innovation and I think it can be challenging to stay on task and on strategy. It’s a very powerful tool, but I think there are appropriate times and scenarios where it makes sense, and others where it may be more of a challenge.

On November 13, 2018, JC commented on 3D Printing the Future of Rail at Deutsche Bahn :

Awesome post! I really enjoyed reading this. I think that they should not pursue printing in house for the time being. I think that right now the technology is advancing really quickly and likely 3d printing companies will be able to innovate faster. Once the technology adoption curve has slowed down, I think it may make more sense to consider that type of investment. If they do go in-house, they will need to think about how this impacts their core business and align incentives accordingly.

Great post! I really enjoyed reading about how Nestle uses open innovation. I think there is a broader trend with CPG companies investing in small start-up organizations through venture and innovation groups. I think that given the slow nature of the large CPGs, they will need to more pro-actively engage with the small start-ups to stay competitive.

On November 13, 2018, JC commented on Broad use of Machine Learning at Ford :

Really great post! It’s very interesting to think about how large, legacy companies like Ford can use machine learning to stay competitive in a changing landscape and world. Currently, given the uncertainty with machine learning to date, I think that Ford is best to partner with existing companies, rather than developing software solutions in-house. Over time, as their business model may shift due to the changing transportation landscape, it may make more sense for them to invest in an in-house solution.

On November 13, 2018, JC commented on Nike’s 3D Printing: Just Do It :

Awesome post! Nike is one of the great examples where 3D printing is providing current benefits to customers. One concern I have is that Nike’s ambitions seem to be kept at bay by the speed of the technology innovation. You mentioned that they have increased speed through partnerships. Do you think Nike should take it a step further and develop this capability in house. While it may same radical, if it’s paramount for the future of their business (sending customers files instead of shoes), perhaps it makes sense to invest now.

On November 13, 2018, JC commented on Promise and Peril for Machine Learning at Netflix :

Great post! I think that it’s really interesting to look at Netflix because they are on the more mature end of the machine learning spectrum. Rather than just starting to use it, they are moving into the land of how much is too much and how far is too far, questions you raised in your post. I do become concerned when Netflix uses a very limited data set when providing recommendations. I think that when and where possible (understanding privacy laws), it would be valuable for Netflix to understand more about their customer holistically to provide better and more appropriate recommendations. Of course, they do need to be careful because again as you pointed out, it can turn into profiling in a dangerous way.

On November 13, 2018, JC commented on Duolingo: From Hello to Hallo through Machine Learning :

Awesome article and really interesting read! It seems like duolingo’s core competency is really well suited for machine learning because of the size of the data set and the type of data. One limitation as you mentioned is that learning in an app is very different than stepping up to a counter and placing an order. I wonder if as machine learning advances, duolingo would be able to use their same machine learning skills for live video streamed interactions, thereby utilizing their core competency while improving their offering to be more realistic.

Really interesting article! I think that beauty is an industry that is ripe for machine learning innovation, but as you called out, it is going to be very challenging to win in this space. One thing that Proven needs to be wary of is creating a biased data-set. If they are only inputting data from satisfied customers that repurchase, they are not creating the neural networks of the failures (those that did not repurchase because the product did not work). I wonder how they can incorporate both the positive and the negative matches to further improve the recommendation engine.

Really great post! It’s truly incredible that this company is able to have such a large impact on those without homes. Given the benefits of a 3D printed home (less waste, more thermally efficient, etc.), do you think that this will ultimately change the way homes are built in developed markets? I wonder if the benefits are so vast that this will actually revolutionize the way homes are built around the world, including for-profit entities rather than just via non-profit organizations.