Wow, the idea of open innovation really came to life for me through your description of collecting ideas for city planning from actual citizens. On the one hand, I think this is a great idea, as eliciting feedback from the bottom up can do two things:
1) generate new solutions to problems that may not have otherwise been identified
2) create a sense of city ownership and loyalty from individuals who have a say in its design
However, I think there are potential problems in the efficiency of managing ideas and opinions from many people. From a resources perspective, it may not be practical to follow through on every idea submitted. And there is the risk that when differences of opinion form, those who end up on the losing side of a decision become disinterested and upset. If Sidewalk Labs is able to manage these risks while pursuing its open innovation concept, its strategy could result in better city design and more loyal and happy citizens.
Thank you so much for sharing your personal perspective on how 3D printing can help Rwanda as a whole. The portion that resonated with me the most was the prohibitive costs to shipping in prosthetic limbs, reconstructed breasts, etc. and how additive manufacturing could help solve this by providing a personalized low-cost option in country. This would be especially impactful for a country devastated by war not so long ago. However, I wonder how prohibitive the unit cost of printing these types of body parts would be. Is this a feasible alternative for a nation with limited economic resources, and to your point, what other necessary projects would have to be foregone to invest in 3D printing in Rwanda?
This is an interesting article that brings to life machine learning through the lens of AirBnB, a company many millennials are familiar with. To me, the current algorithms findings that help hosts estimate fair prices and create more attractive listings are currently the biggest added value. As a host, you may have a great place to rent out, but you need help on the business side to maximize its full potential. On the guest side, I would love to see more machine learning innovation from AirBnB around recommending cities for travelers to visit based on past preferences. This could be a really cool way to grow the travel market even more and reduce some of the upfront research people are required to do when planning a trip.
This is a tangible example of additive manufacturing affecting others’ lives in a very personal way by creating new limbs, organs, etc. I am curious as to how long it will take to produce functioning organs via 3D printing, as it still seems a long way off. In my opinion, the DoD should definitely provide care for those who have been injured serving our country because the U.S. as we know it would not exist without them. The answer to how much the DoD, and therefore taxpayers, should invest in this technology lies in its proven effectiveness and the speed of innovation. If there isn’t meaningful progress in this field after substantial amounts have been invested, the DoD should direct taxpayer dollars to other forms of care for wounded soldiers.
This example of open innovation as part of the content creation process for Amazon Studios is very interesting! In theory, it seems that this strategy could work because creativity can come from anywhere. However, I think there are two main reasons why it wasn’t successful, at least for now:
1) The high upfront cost of content creation means that studios must invest a lot without knowing whether a project will be successful. The content business tends to generate a lot of duds and a big hit once in a while. So pursing many of these potential ideas at once and investing resources in them along the way is a very costly business model.
2) For subscription video on demand (SVOD) services, it has been observed that a single big hit does more to generate new subscriptions than a large library of titles. So when Netflix and Hulu sacrificed quantity for a few high quality shows, those big hits were instrumental in driving new subscriptions. Through its crowd sourcing model, Amazon chose to pursue quantity at the expense of quality, and this proved to be the wrong move for new subscriber acquisition, at least for now.
I really enjoyed reading about how machine learning is changing how consumers experience Burberry in store, and not just online. It is interesting to think about how this changes the level of personalization in one’s brick and mortar shopping experience. However, I wonder if there are cases where this type of behavior feels intrusive to customers who don’t want so much of their data stored or who want to be left alone to browse in peace? When you consider all types of shoppers, is the net effect still positive? Regardless, this trend seems to have come to stay, at least for now.