Very interesting topic, as we see time and time again that the responses to disasters fall short, despite large sums of money donated or allocated to alleviating the problems. Even if AM produces only incremental changes and improvements to speed the delivery of essential parts where they are needed, that will free up resources and space for essentials like food and water. You are wise to point out that capabilities like power are often limiting factors for this type of innovation. Still, I think there remains a role for AM resources to be positioned in close proximity to areas in need for the initial surge of aid, reducing transit and response times overall. I think that as AM capabilities advance, those interested in AM’s applicability to humanitarian uses should push for advances related to reduced or alternative sources of power consumption.
We did a Nike case, now I want an Adidas case too! This is a fascinating article, and like those who commented above, I had no idea of the extent 3D printing had extended into footwear. I can definitely see some uses for such customization outside of athletic apparel as well- consider that many blue-collar workers and military service members wear work boots almost every day. For them, the search for the right pair of boots is never-ending, and a good pair makes all the difference. I am surprised that this capability is not widely known, and wonder how they plan on marketing this to consumers in the near future. Great article!
Your point on the first-mover advantage and risk is well-taken, particular given the high R&D costs associated with leading the way in innovation. Such innovation could definitely prove to be a radical, rather than incremental, shift in the industry, but might be a short-lived competitive advantage, particularly if no patents could be acquired. I think this lends credence to the idea that tech firms (such as IBM with Watson) may fill in the gaps with AI and machine learning capabilities which can be generalized to multiple industries, including mining. In the meantime, Barrick may have to continue harvesting the lowest hanging fruit, rather than investing heavily in uncertain technology (particularly given Watson’s mixed track record thus far).
This is definitely an interesting case where machine learning might have both financial utility for CapitalOne in addition to potentially some social impact by reaching an underserved population. I share your concern that just as Amazon tried to use machine learning to combat gender bias in hiring (and their machine kept recommending candidates who matched the profile of past hires), CapitalOne may face similar challenges with biases implicit in the data they use as inputs. I would be curious to see how data ethicists would work. Great article!
It is definitely interesting to see the parallels you drew between DeepMind’s work with Starcraft II and projects like Deep Blue and IBM’s Watson, as although Starcraft II is still just a game, it seems to represent a natural progression and the next evolution of AI. With constrained resources, you certainly have a point that maybe this technology could be applied somewhere where social impact could be better felt, but I think that as each iteration of AI progresses from one challenge to the next, we get closer to principles and capabilities that are more generalizable for real world applications. As an added bonus, Starcraft II may be able to draw more data scientists who are attracted to this project and its unique and fun learning environment. Great article!