Vanessa Trinh's Profile
I think it is extremely admirable that Tesla has committed to their Open Innovation Policy, IP is definitely their most valuable asset and they are sharing it with the world in the hopes that all can benefit. While this opens the door for abuses to the policy by their competitions, you would hope that this sets a standard or example for other companies to follow suit in the same way to allow for sharing of best practices and best ideas to really accelerate innnovation in places that can advance technology that could be beneficial for all. I think the fact that the pharmaceutical industry, tech industry and academic research is so protective of their research really honestly inhibits the innovative potential of society. There are probably much larger gains if competitors were not competitive at all, as society can gain for the research all of these respective firms have done and it would reduce waste or double work. However, this is pretty impractical and unrealistic, or else patents wouldn’t exist and IP wouldn’t be so profitable.
The World Bank also has a few of these innovation for development challenges, however, I think it is incredibly difficult to take a social enterprise project that might be a great solution and make it economically viable. We face this all the time with social enterprise projects in general. Due to high upfront costs and often cultural challenges of implementation, many of the winners of these contests are just that— winners. It is not very often they have scalable, bankable products that can be mass produced and adopted by large populations that don’t understand the technology or its benefits. I think a large gap is in education and a entrepreneurial environment or market in these countries that support these ventures. Without large scale resources from the private sector it is difficult to do these projects justice. In the same grain, private sector rarely has incentives to fund these projects and are hardly convinced to do so. How can we bridge the gap?
Definitely something out of the (vending machine) box! This is a very interesting take on making additive manufacturing more flexible and iterative. It also brings 3D printing to a much more consumer-friendly and scalable approach. Everything I’ve read so far is something that only large corporations that have the resources to invest in for large scale R&D, but also this could be way more accessible for SME’s and smaller D2C businesses. If you could make this so nimble and flexible to customer demand as vending machines, you could make a singlur vending machine be able to sell anything! It could so much more effectively utilize every single location!
Do you believe this will dilute Chanel’s brand as 3D printing products on a mass scale hardly seems luxury or exclusive. I see that Chanel needs to adopt this to compete with other mass brands in this subsegment but I feel like a lot of the allure of Chanel is due to its non-mass appeal. As a fashion house, I think it does need to keep up with technological changes, especially any that might be cost-saving or more efficient, but I can’t help but think it cheapens the brand a little?
Thanks for a compelling read. I think more generally ML has been applied to a few financial contexts but the Fed is a new one I haven’t really explored, but obviously should, I only question a few of the fundamental tenant of the Fed’s role and whether it is really in a place to inherit outputs from a system that is mostly using inputs of lagging versus leading indicator. Most people have already recognized that economic models are rarely if often ever accurate and especially in light of the financial crisis, not as relevant as many would like. Do you really think ML can do much better? Even with neural networks? As a “black swan” event, a crisis doesn’t exactly seem like a predictive result. Also, can this model be applied globally to other central banks? I’m not sure it can be calibrated to understand cultural economic differences. Economies fundamentally rely on consumer behavior which can be so different across cultures and regions. Regulatory differences, reactional differences to things like increased rates or decreased supply and taxation can lead to differences in reactions to monetary and fiscal policy, I’m not convinced this could be used practically across the board.
The concept of automating fashion forward choices is quite counterintuitive. I think it is interesting to think that you could use machine learning to predict consumer preferences but I think that might be different than creating fashion. As machine learning tries to start making creative choices, its inherent code that relies on getting inputs that are similar to the things that it knows, will not allow for novel or creative ideas outside of variations of design or products outside of what it has already done. While that may help innovative for the mix or type of products in the short term, it will be less impactful for creating new or untested ideas or designs.