Thanks for the article. I continue to lean on this point but AM in prototyping, supply chain, etc. is one thing but when you come to installing it in industry and safety-critical components, I feel we could be moving too fast. The points you raise around failure rates and mechanical properties are so important for original equipment manufacturers. I would imagine even in the traditional product development process, time must be taken to flesh out and test those concerns. I would hope Siemens is providing the same rigor around 3D-printed equipment to maintain their credibility and reputation for quality. This may be a space where additional regulation or oversight might be needed since it is so fast-paced.
Great article on some of the unique challenges in the public transportation industry. I agree that as a public service provider, there are benefits to for-profit partnership, but these submissions should ultimately benefit the key stakeholders of the government, community, and especially riders. I am sure portions of each idea have been considered and even implemented as the MBTA makes other changes.
I worry about a lower bar given the challenges the MBTA already faces evaluating these suggestions but think it could be done specifically for riders or community members while maintaining current expectations for for-profit companies. It ultimately comes down to who MBTA plans to serve and what its goals are. Does it believe it on-the-ground riders and drivers know what is best or economists and for-profit companies? I don’t know what ideas have been implemented but would assume riders and front-line employees could provide a number of small ideas that could add up to be quite impactful if there is a more streamlined process.
Holly, thanks for the great article. I agree with you and other commenters that it is very hard to trust machine learning using inputs that have been proven to be biased in the past.
I also wanted to touch on the point of transparency which I think hits on this tension the world (especially in the USA and since 9/11) faces between privacy and (national) security. If, and this is a big if, this could remove bias and actually be predictive, would there still be a concern using machine learning? Transparency could allow criminals or others to game the system and thus defeat the purpose but I agree the trust needs to be there first anyways. This is more of a philosophical or thought exercise as I believe we are a long way from removing bias from the system and actually being able to predict crimes using statistics or algorithms but believe it needs to be considered. I unfortunately feel we are moving this way even before we have proven results or understand the ethics and tradeoffs but do think the considerations should continue to be raised!
Great summary of the situation. As I finished reading, I had the same questions you did. This may be due to my own ignorance but is 3D printing able to provide the same safety and reliability that traditional steel and concrete do? How much of a structure or project can be done by 3D versus repairs or a few components here and there? Also do you have a sense of the public (and engineering) trust to date? For example, do people know or care when something is 3D printed, are engineers using this in safety-critical environments, has there been enough stress testing and quality control to ensure this is worth it? I am worried that the first incident will really damage the use of 3D printing. Lots of questions which are worth considering!
Thanks for the article. I love this idea but have 3 main concerns. 1) How easy is it for others to replicate? Can Betabrand keep a unique set of users or designers to stand out in what is likely a competitive space? 2) What is the product cycle-time? Depending on there responsiveness, is it possible for things to be out-of-season or demand by the time people actually receive them? and 3) How does this become a fashion brand which is supposed to be forward-looking? Can users see and drive that or does it stay more personal and functional? Can and should Betabrand do both and if so, how would open innovation support this?
Enjoyed reading about machine learning in Netflix. In addition to customer retention and engagement, which are obviously critical, is Netflix also using machine learning to look into customer acquisition? For example, could it analyze existing movie/TV watching habits, advertisement responses, or other trends to determine how and where to best advertise particular shows? Finding external data to drive these changes may be challenging or expensive but could be instrumental to Netflix’s growth, especially as it nears saturation in certain countries.