Thanks for the article! I find it really interesting that NASA is partnering with entrepreneurs and making their patent library open to help drive innovation. Given NASA mandate as a government organization, as well as the typically groundbreaking nature of progress in the space industry, I believe this public/private partnership can really spur innovation that would otherwise be too risky for private enterprise. I think NASA now need to invest in significant internal controls to keep track of the ideas it has coming in as well as prioritize high-potential projects.
Thanks for the interesting read on Nike’s push for additive manufacturing in its shoe design. On your last question about the scalability of the technology, I believe that one potential solution could reveal itself over time. I am of the opinion that the evolution of additive manufacturing will follow a similar path to that of the computer. Currently the technology is limited to large corporations and hobbyists. Over time, the proliferation will increase and many homes will have their own machines. In this world, Nike will provide the technology, materials, and design, while manufacturing will be decentralized. It will be interesting to see!
I really liked this article. It shows that there are many applications of machine learning technology beyond those one would typically think of. I have one major concern with the article though, and that is whether it is effective or not. Before I would recommend rolling this out to other regions, I would want to understand exactly how much better this PAWS algorithm is than current methods for poaching, such as following large herds of elephants and protecting against poachers that way. My concern is that over-reliance on the new fancy technology, such as machine learning in this case, will distract from the more important mission of reducing poaching.
Thanks for the great read!
Very interesting article! I particularly like how Liberty mutual is utilizing machine learning to improve the customer experience through their crash-damage estimating capability. When I think of insurance and machine learning, I typically think of complex algorithms that will try to determine exactly how likely a person is to be a liability above and beyond their premium. Their use of machine learning is something so customer facing reveals how much a firm can miss out on if they only focus about machine learning in the context of their core capability. Here they are using ML to generate a new, customer friendly capability that would have been impossible before!
Thanks for the great read!
I found this article very interesting and informative. I am definitely bought-in to the idea of additive manufacturing, but I have one lingering question about it’s application in this case: is there anything specific about additive manufacturing that makes it unique in this use case? Said another way, can we use traditional manufacturing techniques to produce soft robotics as well, or is additive manufacturing the only way to achieve these breakthroughs?
Beyond that, I think this was a very interesting glimpse into the potential applications of additive manufacturing. Thanks for the insight!