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Very interesting article Jayant! I agree Ripple has the potential to transform the way we transact online on an international level. As you raised, I am also concerned about Ripple’s holdings of XRP. Because Ripple owns both the majority of XRP tokens and protocol development, their approach is more centralized than other cryptocurrencies like Bitcoin, which in some ways may make adoption easier given the trust and credibility imbued in a consolidated system. However, there are issues with classification (token vs. security) which could fundamentally alter the value received from distributed technology, limiting movement and ultimately forcing a reversal to the current complex remittance system.
Reference: https://www.nytimes.com/2018/07/01/technology/cryptocurrency-ripple.html
I agree that the space and time constraints make additive manufacturing a natural fit for the unique environment of submarines! Is there a reason (beyond the large upfront investment) that 3D printing is only being used for non-ship parts with limited on-board testing? It seems like the plans are in place to introduce and integrate additive manufacturing into the repair and ship building process, but the technology has yet to diffuse to the Sailors or other personnel on the front lines. In that case, it might be worthwhile investing in technological training and development for the workforce, opening access to additive manufacturing processes across the organization. While contracts with external organizations are valuable and will help to speed along delivery of service and parts, providing the technical knowledge to employees across the Navy will help ensure the sustainability of the program.
This is such a cool application of additive manufacturing, and great to be able to see what the concept looks like in practice. I agree that there is a tension between internal R&D vs. acquisition. Given the benefits that additive manufacturing offers in the construction industry, I would argue that it is imperative Bouygues invests in developing and designing their own process for 3D printing.
Beyond advancing the field and getting ahead of competition, the technology could help to reduce current costs to the business. Although the technology is still in its infancy, any investments in developing 3D printing could be used immediately to supplement current processes. I don’t think the company needs to wait for the perfect application, or for the technology to mature, but rather introduce parts of the 3D printing technology slowly into the current building cycle. An entire 3D printed home is an interesting test case, but I think the more interesting piece of this application is how companies can use the technology piecemeal to reduce labor or resource intensive parts of the construction process, making the entire project more efficient overall.
This application of machine learning and AI is very interesting, as it ideally allows police to scale their presence while reducing human biases that lead to critical failures in judgement. As you mentioned, the issue is that any historical datasets used to train any AI or machine learning models are already seeded with bias which could lead to flawed algorithms. In order to avoid repetition of errors and the multiplication of biased machines, introduction of this technology needs to be done slowly, with excessive human monitoring and intervention. Unlike other applications, machine learning in enforcement needs to be more of a predictive guide and less of driving force, with multiple touch points available for human correction.
Great article! The early metrics from the Command Center program seem incredibly promising, particularly when it comes to the quality and speed of healthcare delivery.
As you raised in the article, AI and machine learning will undoubtedly change medicine from the provider perspective. Additionally, in order for machine learning to truly be integrated into front-line healthcare delivery, the nature of relationship between the provider and the patient will also need to change. It seems to me that the patient perception will be critical here, as patients have power (particularly in the US healthcare system) to dictate how they receive treatment. The real test of increased technology in healthcare will come when misdiagnosis or critical failure arrives at the hands of AI or machine learning processes. At that point, patients could demand more care from providers, and less machine involvement, necessitating a reversal of the amount of technology involved in delivery.