Fascinating introduction to how the military is using additive manufacturing to simplify the supply chain and speed up delivery for replacement parts. Like with other military innovations, such as radar and carbon fiber, I wonder what the civilian applicability of such decentralized manufacturing of niche replacement parts could be. Would a business that uses AM to reproduce all sorts of replacements parts for industrial machinery or household appliances be viable? What are the challenges and risks that such a business would face?
Interesting overview of Hersey’s application of additive manufacturing to chocolate. While I agree that 3D printing of chocolate would increase customizability and potentially drive customer demand, I doubt it would have any material impact on the nutrition of chocolate. Chocolate is chocolate and innovations that reduce the amount of sugar, for example, could just as easily be implemented in traditional mold-generated bars. Overall, I see 3D-printing of chocolate simply as a marketing gimmick.
Thank you for your insightful article. The Grand Challenges program seems like an excellent way to spur innovation to transform pain areas like poverty and health. I wonder how more precisely the Gates Foundation encourages open innovation. Are there any explicit funding competitions or crowdsourcing drives? What process does an organization go through to secure funding? Could the Gates Foundation crowdsource it’s decision about what projects to fund? I would love to learn more about the successes to date of the Gates Foundation!
Insightful article about the potential of open innovation to revolutionize the snack-food development process. While such innovation may lead to popular products, I fear that it will only amplify trends towards addictive, unhealthy snack foods. It may help PepsiCo’s bottom line to develop addictive, popular products, but if these products contribute to the rise of obesity, heart disease, and diabetes, should they exist at all? As one of the main companies that feeds America, I believe PepsiCo must take responsibility for the rise of diet-driven disease leading to significant healthcare expenditure. Are there ways for PepsiCo to create popular and healthy products that involves open innovation? Or is the snack food industry simply at odds with health? Unprocessed fruits and vegetables will always be better for you than the healthiest bag of chips.
Well written article about the potential of machine learning to revolutionize central banking. I agree that ML should become a standard part of the Fed’s process of evaluating present and predicting future economic conditions. Moreover, greater predictability of the Fed’s decisions, which could be the result of the implementation of a standardized ML model, would only be good for economic stability. However, I believe we are very far from deferring the responsibility of raising or lower interest rates to a ML tool. A ML tool can help inform such decisions, but a decision with such widespread societal implications will most likely continue to be decided by people, albeit people better informed by ML prediction tools.
Fascinating article. I like how it brings up some of the key challenges that demand-driven for-profit transport services pose to traditional public transportation networks. While these new transport services like CityMapper in London or the former Bridj in Boston provide on-demand, high quality, and low-cost transport services to some, by compete away revenue from existing public transportation systems, they could have a significantly negative societal impact. Affordable, efficient public transportation is key to social mobility and is often cited as a major reason why social mobility is higher in Western Europe than in the US. Preserving high quality public transportation for all must be the mission of TfL even if it requires heavily regulating services like CityMapper.
Excellent introduction to this novel chronic disease management tool! Making health coaches more efficient and effective is critical to delivering patients the best possible care. Clearly using machine learning to direct patient care will require some sort of oversight to ensure quality care. I can easily imagine a world where a machine learning tool would provide care recommendations and a physician would be required to approve them. Alternatively, for things like adjusting IV infusion rates in ICU patients, a machine learning based tool could independently adjust patient care. Such a scenario would of course require much stronger regulatory oversight. Liability would probably be a central issue. This is all good food for thought.