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Brilliant example and one completely new to me – thank you! You definitely have me convinced that this is a critical step forward in helping solve for the systemic inefficiencies in US disaster relief efforts. I do think that solar could work and that if 3D printers are deployed at scale, would have an appropriate price tag especially in the shadow of the large number of USDs that are typically wasted. My only concern would be around the machines needed to be extremely simple and efficient to use in disaster relief zones where, to put it simply, there is complete chaos. I still believe this application is worth investment and attention, especially as 3D printing becomes more sophisticated and will be able to create more complex and life-saving instruments.
Well-written and thought provoking piece! I too share your concerns about the dangers that exist with this particular use case. From the perspective of businesses that might adopt these robots, I would question whether the lack of control over their behaviours is truly worth offsetting the efficiency that they arguably help achieve. I also question how much the human element can and should be removed as an additional system of control should things go out of hand. From a Facebook perspective, the company is also opening itself up to further scrutiny around its lack of control around its products being used to further nefarious activity.
Really enjoyed this application of machine learning! I had never thought about it in the context of fine-dining restaurants. In answer to your second open question, I would echo some of the points made below. While machine learning would give El Bulli an efficient way of collating food trends from all over the world, I do believe that the restaurant’s goal was to give customers an unparalleled culinary experience that goes beyond anything that they might have tried in the past. Therefore I don’t think machine learning is the answer here because it doesn’t provide anything ‘new’ and therefore the chef, as an artist’s role remains central to the process. However, I do think you raise a valid point at questioning whether there is a role for machine learning in responding to the large volume of customer inquiries in order to maintain a relationship and encourage them to continue trying to get a much coveted reservation.
This is a great piece and very interesting to see how SIA is applying open innovation to help solve its most pressing challenges. I wanted to address your question around ‘how open is too open’. It seems to me that it would be advantageous to put greater structure around what SIA is trying to solve for (the 5 most pressing challenges outlined must have varying degrees of urgency and ramifications for the business). I would then do a more targeted ‘recruitment’ of people who can then help solve these challenges, versus just students at Singaporean universities, even though that is definitely a great place to start! Perhaps partnering with a select group of start-ups or entities in the aviation industry that are also grappling with these challenges? I too wrote on open innovation and continue to question how the ideas generated can be translated to business-altering solutions. In your last paragraph you raise the example of a participating team that abandoned an idea because it was too complex. I would argue that this is where a select group of SIA employees have a key role to play in guiding these teams.
Great piece, thank you for raising these important issues! I think the idea behind Teachers Pay Teachers is very compelling especially from a community perspective where teachers can engage with others to understand what materials are working versus not. I do wonder, however, whether scale is feasible here without collaborating with the large publishing behemoths in some way. It might also benefit TPT’s mission given I imagine many schools are heavily biased towards using materials from well-known publishing houses as it establishes credence in their curriculum.