Great post. It’s very interesting that 3-D printing can have such a meaningful impact on their supply chain. It seems in line with the market trends of personalization and supporting local. If they can successfully implement consistently, this could really inspire other companies to jump on the bandwagon of 3-D printing, which will be when things really start to change.
Great article. Cyber Security considerations are very important, especially in tech and IOT. As Nokia continues to think about these areas I think it will be important for them to keep in mind that although it can be helpful to collect as much data as possible through new technologies, this should not be done at the expense of the customer experience, else risk losing these customers and hence the data. I hope that Nokia is able to retain principles like these around customer-centric products as they continue to use open innovation.
The second question is very interesting – It seems unrealistic that Lego would understand the external knowledge of it’s users in perpetuity. It seems much more sustainable to rely on the actual end-users (who you know will always be there) rather than a product visionary (who may easily guess things incorrectly). Although releasing control to user feedback may appear as weakness, it seems as though it is the necessary and sustainable path.
Very interesting and important. I’ll be very curious to understand how the government and policies adjust to the new normal where original equipment manufacturers (OEMs) have such a monopoly of the understanding of the manufacturing of goods critical to government operations, particularly in the military. This seems like a change of power that will have cascading effects.
Very interesting read. The moral question at hand of duties to the environment and consumer are complicated but also simple in some ways. On the environment specifically, I like to believe organizations should strive to leave the planet in a better place than when they left it. In the energy sector, this is particularly important as not prioritizing the environment can lead to especially serious consequences.
Great article! I love the idea of exploring machine learning to supporting inclusion initiatives. I wonder what type of activities and further benefits Capital One would be able to tap into by exploring these markets further. It seems as though machine learning would be an excellent avenue to accelerate financial inclusion by developing a better understanding of trends.
Great article, Petra! My main thought when reading this is how Lemonade can create differentiators and barriers within its machine learning that other companies will not be able to copy-cat effectively. Is there certain data that they are able to gather more accurately? Does their current market give them a unique view? Or is their first-to-market advantage enough to maintain their position?
Thanks for writing on the issue, disease detection, management, and treatment are very important. I feel similarly to Petra that I’m not entirely clear on how machine learning versus advanced data analytics plays into the future of diabetes. This is not to say that there is not a place for ML, but there are almost so many ways that this could go. I think many of the examples listed have opportunities to be turned into questions that would benefit from ML, however, I feel as though I may be missing the link between these two states.
This is a very relevant and interesting issue. Machine learning works well in improving when there are measures by which the algorithm can learn and improve by. This case presents a challenge as articles written as speculation or opinion will be hard to differentiate from factual news articles, and may even be subjective. It will be interesting to see how this nuance is handled as the algorithms further mature.
This is very interesting and clearly depicts a situation where Spotify must stay on their toes to stay ahead of the competition. They have created a competitive advantage but I believe that it will be difficult to sustain for many of the reasons you described. As machine learning is limited to the data provided, I think there are opportunities to try and protect this advantage by international partnerships (to Petra’s point) as well as partnering with alternative music venues such as Soundcloud, that host user-uploaded content and would bring even more varied data to their algorithms.