J Horgan

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On November 15, 2018, J Horgan commented on LEGO: Leveraging the Building Blocks of Open Innovation :

Ideally LEGO would be able to target both adult super-fans and children alike. The process of open-sourcing LEGO set ideas and funneling them to the product development teams after 10,000 votes is brilliant. The popularity of the game Minecraft tapped into the same creative vein in both adults and children. People create for their own enjoyment as well as to share with others online, so LEGO should continue to leverage their digital platforms and provide as much exposure to the creations of their super-users as possible. I don’t think there is much risk of tarnishing brand reputation as long as the ideas that are company-endorsed are carefully vetted. Additionally, the risk of being left behind is too great to not leverage the digital platform.

This is a very interesting topic. On the subject of bad actors, I can see machine learning algorithms directing fishermen to predicted areas of concentration, potentially conflicting with conservation efforts. The use of AIS data to identify the movements of fishing vessels is clever, but as you mentioned AIS is not typically used by small fishing vessels and can easily be turned off. The information provided by this algorithm/monitoring will need to be enforced, however that is costly and potentially impossible in international waters.
Aside from general education, our best chance to help this situation as individuals is through our purchasing and consumption habits. While certain areas of the world require fish in their daily diet, consumption in the West seems more driven by luxury markets and health fads. If we truly value this as a society, we need to start voting with dollars.

From my experience at Medtronic on the Operations side, my biggest concern with personalized medical devices is the regulatory environment. Even for “simple” products like suture and laparoscopic stapling devices, the FDA is incredibly strict about changes in production. Any changes to equipment or processes would typically trigger a re-validation that could take weeks or months. While 3D printing may be useful for producing small components of high complexity, I cannot imagine the FDA allowing significant design changes for patients.

The case of Cambridge Analytica’s rise and fall is fascinating and I will be curious to see who replaces their efforts in future elections. It is particularly interesting that the general technology for targeting specific users was created for marketing/advertising applications and has been going on for years with little user concern. The true danger occurs when political advertisements masquerade as objective news. You asked great questions, however in an age where people are influenced by a picture, tweet, or brief article headline, I wonder what effect increased transparency would have. I imagine an arms race, with companies such as CA developing new and improved tools to stay one step ahead of attempts at regulation. It seems that the only way to prevent voters from being influenced in this manner is through education on the topic and for them to take responsibility for their media consumption.

On November 15, 2018, J Horgan commented on Adidas’s Race to be #1 in 3D Printing :

What a fascinating example. I am trying to imagine a world where a customer walks into a shoe store, is fitted, and has their shoes made for them while they wait. The ease with which 3D printing allows the construction of matrix structures and internal cavities suggests that additive manufactured shoes could have amazing ergonomic properties. I am curious to see how ADIDAS will turn 3D printing into a long-term competitive advantage that cannot just be copied by their competition. Partnerships with athletes will change drastically too. Product development teams will benefit greatly from decreased production times between iterations.

On November 14, 2018, J Horgan commented on Using Machine Learning for Crime Prediction :

The potential application for “PredPol”, both for police forces and for alternate uses, is fascinating. I love the idea of using the predictive software not only to anticipate crime locations, but to deploy preventive resources such as social services. These alternate uses can strengthen communities and build trust in the software in communities.
I am curious about the long-term effects of policing areas predicted by the application. While its current inputs may accurately guide police forces to crime “hot spots”, criminals have proven to be resourceful in their evasion of the law. I would be interested to see how these “hot spots” develop over time. Are police continuously sent to similar locations to keep crime down, or does crime continue to move around cities? Can this application actually prove that it is making cities safer?