I love this post because it demonstrates a case where 3D printing drastically improves the user experience as opposed to just solving for an efficiency problem, which tends to be the focus of most takes on 3D printing. As someone that experienced the traditional molding process first hand, it as very uncomfortable and inconvenient experience to the extent that it was more so than wearing the liners!
this case of clear utility, efficient production and novel printing techniques represents a great marriage of megatrend and aplication.
I very much enjoyed reading this post. Lego as a product company with a living, changing and diverse product range is a perfect candidate for open innovation because its users can be source for originality and novelty. It is hard to think of a company better suited for this match between users willing to participate in the life of the product and a company in need of and able to utilize this creative resource. Furthermore, Lego is not in need of providing large monetary rewards because reward for participating for many participants is primarily a form of personal satisfaction. At the same time, Legos use of royalty to the inventor is a creative and interesting way of signaling that the participants are part of the Lego community.
The post also discusses an important aspect which is how Lego manages this process in a way that enhances the commercial value of their product mix while also garnering sufficient interest in their open innovation initiatives.
The post talked about open innovation in a novel and original way. While most of us would perhaps associate innovation exclusively with technology and science, the fact that it was used to solicit a commercialization plan as well is refreshingly unexpected. The choice of hyperloop is a perfect example of the utility of open innovation across these different field and problem areas.
It was very enlightening to understand how hyper loop engaged with sourcing and managing commercialization plans through a staged approach, taking competitors through the various phases of the business validation process from high-level to executable.
The choice of organization is a source a fascination and interest. NASA typically operates in the domain of high applied science, dealing with the most challenging problems. If open innovation works in this context, it is reasonable to be hopeful that it would work anywhere. It was also intriguing to read about the various mechanism through which NASA solicits open innovation. The idea of innovation prizes has been used effectively in other areas like health and humanotarian challenges with great success. I would be very interested to further understand how well this mechanism works in contrast to other programs.
On the other hand, Im am not sure how the example used, of thousands of volunteers participating in image recognition, represents an instance of cloud sourced ‘innovation’. It sounds more like an open participation program more so than an open innovation initiative.
often when the topic of machine learning is brought up, the discourse tends to exaggerate the replacement effect while muting the potential for machine/human complementarity. This post definitely avoids making that error and instead, we are presented with a very well clear picture of a world where “machine learning supercharges human capabilities.” the fact that image analysts are potentially 95% more efficient at their roles speaks volumes. At the same time, I found it sobering to ponder the possibility that this improvement is likely to result in a zero-sum AI arms race rather than a value create.
the Article does a great job at listing and explaining the various ways through which machine learning is being utilized in the oil and gas industry. through uptime optimization, sales forecasting, safety standards observance and emission control machine learning is clearly a trend worth noting in the context of the industry’s future.
It would have been interesting if the post also discussed how another mega trend in this industry, shale, will either amplify or mute the impact of machine learning.
The article does a great job at placing the organization in question in a very rich context both in terms of its external challenges and motivations and in terms of its internal drivers. For instance, the point about the Navy being slow to field a rapidly evolving technology at the risk of it becoming obsolete was very insightful. what remains unclear to me however is the extent to which submarines are developing to evade imaging all together. This I imagine is a major factor in assessing the validity of an machine learning solution to the problem.
This was a very compelling case for the value created in the construction and development business by a radical improvement in process facilitated by additive manufacturing. The author also did a great job of translating the value into meaningful outcomes like the reduction of homelessness.
A few issued I think remain unaddressed: 1- how do printers adapt to different site conditions and environments without creating redundant setup and and manual configuration requirements that defeat the purpose of automating the process, and 2- how will the design ecosystem respond to this new development given that it comes with a set of radical different construction and hence design restraints.
For more information on these challenges, I found the below to be a relatively informative and thoughtful source: