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This is a fantastic opportunity for AM. Apparel brands can create a technology-based competitive advantage by offering personally customizable products that are designed for someone’s face shape. One of the most annoying aspects of wearing glasses is the way designs fit differently depending on your face shape. Before, mass manufacturing made it impossible for brands to create multiple SKUs that would work for each person, but now this technology can make that possible. Adding to that the ability to use new materials and scaffolding designs and reduce the weight of glasses opens new and exciting opportunities!

On November 15, 2018, PPTjuice commented on Straight to the Source: Open Innovation at Buzzfeed :

Really interesting article! I think the questions you pose on the article are very timely to the conversation around fake news and the intervention of external forces trying to move public opinion in a particular direction for political reasons. There needs to be a very stringent, and in my opinion human-based, filtering and editing resources to ensure the quality and the validity of the news sources. Additionally, there’s always the risk of not capturing the largest diversity of perspectives if the crowds are not leveraged in a way that are directly inputs to the news reported

On November 15, 2018, PPTjuice commented on Economies of One – The 3D Printing Company Rewriting Economic Law :

Fascinating area! I’m interested in exploring what are the main barriers for increasing the scale of AM. There seems to be a technical dimension to the barriers, where the flexibility of material deposition is a constraint to the speed by which a certain component can be build. Additionally, size of the actual component may be a factor. What do you think are the managerial or economical constraints?

On November 15, 2018, PPTjuice commented on An Apple a Day Keeps Premiums at Bay: AI at Discovery Ltd. :

Interesting application! I think the advantage of using ML for insurance claims and fraud detection are can be very impactful and effective in both reducing the cost structure of insurance companies and reduce the risk exposure of their funds. If the data set and ML processes are designed effectively to increase the customer satisfaction with the service, this could be a significant competitive advantage for Discovery.

On November 15, 2018, PPTjuice commented on DHL: Cracking Logistics with the help of Machine Learning :

Fascinating application of ML! Always when I see this applications related to risk monitoring, management and mitigation I ask myself how to meaningfully analyze the data in a way that managers can make decisions. At the same time, there are system design implication to take into account: is data analyzed and then some heuristics are determined to make automated decisions (e.g. delivery truck routing) or are humans analyzing the data and making judgement calls? I think the latter is most effective at this point when ML potential is not fully realized yet

I love seeing public organizations find ways to incentivize innovation coming from its main clients and stakeholders – the users of the transportation system. This kind of open innovation is essential to find creative ways to solve the complex challenges organizations like the MBTA face. That being said, I understand the recourse constraint in the evaluation process. My question is: what is the long term cost of not innovating? I believe this should be a priority for organizations that are facing the risk of being disrupted by alternative mobility platforms like Uber. Nevertheless, it is in society’s interest that public alternatives like the MBTA are not substituted by private organizations that may alienate disadvantaged communities in cities