Thanks Akash, super interesting! I think ADS-B is a great advancement that has a lot of promise for improving the safety of flights. Similar to our Watson conversation, I think it’s a great supplement to pilots -to help inform them of what to do and how to react to certain alerts and scenarios. However, I think that as we develop this tool and knowing machine learning faults, we are bound to discover limitations of the software, exceptions, and instances where the software might have shortcomings and we might need a human to step in. Given the highly binary outcomes of airplane accidents, this is a high stakes situation and I would be nervous to have a remote pilot (what if the system goes down or gets hacked, etc.) Therefore, I think this is a tool for pilots rather than a complete substitute!
Crowd sourcing ideas for fintech innovations is an interesting proposition. I definitely understand where the value could come from but I wonder how Capital One and other banks using similar approaches is tackling issues of disjointed apps. With many independent developers creating apps individually, how does Capital one create a long-term app strategy that has a variety of tools that are able to integrate with each other and support a balanced set of customer needs. Perhaps Capital One is able to control for this by providing specific guidelines in its coding challenges and strategically incentivizing specific types of app developments. In an open marketplace where developers create apps for Capital One, how does Capital One also prevent these developers from creating similar technologies for competing banks?
This is a great article! I would imagine that open innovation is most beneficial for types of city planning that are applicable to broad populations (e.g., transportation, parks, energy) and that other topics like businesses and housing mixes might lead to more divergent views given they provide more individual-specific benefits. I would imagine that these types of issues that are more community-wide are also more likely to get participation from the community. On topics that require more technical knowledge (like energy) there might be more biased decision making as well.
This is a very impactful application of machine learning that is becoming increasingly important in the pharma world. You mention the role of regulators in policing the judgement made in AI but I wonder really how much will change moving forward. To me AI seems to be the method to identify potential drug pathways and formulate a hypothesis for the most effective treatment for a specific cancer type or patient population. I would imagine that the drug candidates would still need to go through traditional clinical trials, unless the FDA creates a new fast track status for AI- discovered drugs where past data could serve as reducing what needs to be proven in a clinical trial or where the barrier is lowered for high risk cancers, etc.
This application of 3D printing is creative and inspiring, but I have some concerns regarding how feasibly this can be expanded to developing communities at scale. As you mention, any developing communities lack access to a computer- on top of that or as a result, they also have limited experience working with such complex technology. Even if this were affordable for developing communities, I wonder how the infrastructure would work- who would be in charge of maintaining the system, learning the system and training others? 3D printers are complex to operate and can break easily, so I would be worried about long-term maintenance as well as adoption.
I really enjoyed this article and think this an impactful application of 3D printing. I’m curious to see how the shift to in-hospital JIT manufacturing is regulated by the FDA moving forward. To your point around maintaining a competitive advantage, I would think that Stryker has developed some IP around both its materials and manufacturing processes that is not extremely replicable; additionally any type of FDA support they have is a barrier to entry. Furthermore, Stryker should start to leverage its own patient-based data from other business arms as well as from its hospital partners to further customize/ develop its line of implants. These partnerships and scaled data will help it develop superior products that are difficult to replicate.