The nature of investment management is such that it is unlikely that any algorithmic approach to the field is unlikely to outperform the market in the long term. That said, negative returns in the first few years should not, to the truly rational investor, ring the death knell for the project as long as the market has a whole has comparable returns. If the project still outperforms the market, even with the negative returns, it could still be viewed favorably.
The idea of outsourcing innovation is very effective in some ways, but it has some potential pitfalls that companies like General Mills need to understand. It is the extension of the Epic problem to further iterations. General Mills cannot help being slightly changed by the addition to the company. This is natural, but through a repeated process of additional acquisitions, General Mills runs the risk of diluting its own culture to the point where it is unrecognizable compared to the company today.
I appreciate Nike and Addidas’s perspectives on the mass production of shoes by additive methods, but am truly skeptical, like the author, of the feasibility of this approach on a meaningful time horizon. The cost impact of R&D for these emerging technologies compared to current methods just does not support a dramatic shift anytime soon.
The concept as a whole is very interesting and is reminiscent of GAP’s attempts to predict future trends in fashion. The author does ask an important question about the viability of ML tools for smaller stage companies. I do, however, think the author needs to clarify BOH’s goals further to truly ask the right questions about the possibility of ML helping the business. The goal of “forecasting trends” is likely too broad to be of any use to a programmer building their model. Figuring out what specific types of trends, what data sets are possible to mine from social media and other sources, and the feasibility of extrapolation vs interpolation from previous data are all key to refining BOH’s outcomes.
While extremely interesting and absolutely closer to the direction humanity needs to head in to solve the problems of the present and future, I wonder about the efficacy of problem solving via machine learning in an environment that is as static as an established city. The efforts could potentially improve the city as it grows, but I think the real impact of this sort of study would be much better realized when the gathered knowledge is applied to the construction or overhaul of a new city.
I am intrigued by the concept and understand the inherent merits of this type of construction. I am very curious to understand the materials and construction supply side of these processes. I imagine that there is a large constraint on capabilities to produce when you are essentially converting materials into a different, more usable form. It also seems that there might be less cost savings in the launching sense, unless you utilize materials in already in space for production.