Interesting combination of artificial intelligence and 3D printing approaches to boost innovation! However, I am skeptical to what extent GE will need to change their current manufacturing processes to adapt to the growing trend of 3D printing. I view 3D printing as an incremental improvement to the prototyping phase of a project, where we now have a more efficient and faster means to build prototypes for testing, however its impact on the final assembly line manufacturing should be minimal.
When considering if companies like Organovo invest their R&D budget in producing transplantable organs or wait till academic research has advanced further, I’d look at the opportunity costs. 3D printing is a relatively new concept in the market and a technique that still has not been applied to a lot of industries where there is a direct immediate need for it such as industrial manufacturing and prototyping. On the contrary, the market for organ transplants is huge and is expected to provide a stable demand for these products. Organovo could choose to invest their efforts in identifying the most popular areas of organ transplanting and we the first to market when the demand arises.
Great example! This article very well focuses on not just why companies use open innovation, but how they actually implement it with their existing organization structures and some challenges they face in implementing these changes. A good point highlighted here is how to adequately provide funding to the multiple ideas being worked upon in a company where open innovation is implemented at the employee grass root level. In one of the previous firms I worked at (Sumitomo Electrics), they setup a corporate venture fund to fund innovations being worked on by the bottomline employees. This helps boost more innovation within the company and provides an organized format for employees to gather funding for their projects.
Great example of how the upcoming technological revolution across industries is using open innovation to boost scale and innovation. However, this particular example where a substantial amount of technical knowledge is required to innovate new machine ideas, do companies like Vention need to provide a way for intellectual property protection of their developers?
I like the link between understanding customer preferences better and reasons for return through machine learning. However, as Stitch Fix expands into new geographies and new product categories, I fail to see how machine learning can help them scale successfully without having any prior data on those market trends. In fact, this would be an area where there machine learning algorithm can be complemented by inputs from their stylists to make the first move in the market and then rely more heavily on machine learning in the future as they gather more data on consumer preferences in these markets.
This is a very interesting take on how machine learning is being used for crime detection through tools like ShotSpotter.
There is also another arena of crime prevention where machine learning has made some advances. For instance, a Chinese company developed Hikvision, a camera that can scan for suspicious anomalies like unattended bags at crowded venues, claiming to achieve 99% accuracy with advanced visual analytics applications.