My initial reaction to reading this article on Glossier is that this brand truly know’s how to leverage the power of open innovation and connect with their consumers. When crowdsourcing ideas, I believe the most important step is to have a clear and simple prompt that gets consumer to quickly respond and rapidly ideate. Likewise this prompt elicits ideas and insights that can be directly used in not only creating new products but also marketing campaigns, brand awareness, and enhances the overall brand image. What I love most about Glossier’s approach is that the understand the importance of each consumer’s response or reaction and use that as additional data points to validate or reject product ideas.
Personally, I appreciate the initial attempt by NYC to leverage the power of open innovation to bring to light advances within the public sector. As this article concludes, I am worried by the lack of urgency, disconnected approach, and disparate resources at hand. In order for open innovation to work and to produce ideas that are brought to market a strong innovation funnel and supporting stage-gate must be put in place. However, this is will only be effective if there is a clear innovation/growth strategy put in place. This will provide a framework by which open innovation prompts should be structured and in turn guiding the crowd’s responses – ultimately providing desired solutions and outcomes.
Wow! Organovo is a very cool and innovative organization with some interesting research in flight. Learning about this organization and their vision for 3D printing organs brings to mind a couple of ‘philosophical questions’ ultimately around the right that organizations have in pursuing these endeavors. Like, Elmo posted above the impact of producing 3D printed organs will bring to light several societal issues and could potential become a larger barrier to entry than an FDA (or similar governing body). However, in regards to the short and long term aspirations at Organovo – Bernie, I want to echo your sentiment and concern that they might have focused on too narrow of a market when pursuing this innovative application. Likewise, I worry that this application may only have limited use cases and not assist too broadly with the donor market due to physiological complications and potential rejections.
Very interesting view point and post Carlos! I believe you bring up a very thought provoking question in regards to the impact of other competitors towards leveraging additive manufacturing within aerospace. My reaction is that using 3D printing in approved and validated applications will over time drive down costs, decrease product development time, reduce resource utilization, and increase innovation capacity. Given these expectations, I believe we will begin to see competitors respond and focus their efforts on identifying additional 3D printing opportunities impacting the aviation product development process more broadly.
This was a very thought provoking and interesting post (one would assume you were a college quarterback). You bring up a very plausible application for machine learning not only within the NFL for play calling but more broadly across professional sports. As an avid sports fan however, I am very weary of the implication that this potential technology may have on sports, which you have referred to as a source of entertainment. First, I believe it would be a shame if teams started winning or having an advantage due to a more efficient or innovative algorithm taking away the joy of the game. Second, if this was allowed I am unsure whether or not machine learning would be able to ‘add’ too much to the play calling approach and ultimately producing better results. The reason is that these are humans executing on the plays that were provided them which adds several variables from effort, speed, field conditions, weather, etc. not machines following a standard set of actions similar to chess.
This is a very insightful article into the use of machine learning in wearable healthcare technologies. In reading this article, the questions that are presented around the future of WHOOP is very thought provoking. I agree that their unique and innovative platform is their true strength and product differentiator. However, I do not this that this should be open sourced and a platform that is shared publicly. This type of machine learning algorithm is what set’s WHOOP apart and, as mentioned in the article, until this is validated and deemed precise this should be kept in-house. Likewise, I believe their choice to use supervised learning is very smart given the limited variation and consistency within the measured attributes although there are numerous variables. The data will tend to be structured and in turn supervised learning will be able to adapt and leverage this data in a more dynamic and streamlined fashion relative to unsupervised.