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On November 15, 2018, nikolasra commented on Additive Manufacturing at GE Aviation :

Thanks so much for your article! I think that relative to competitors, GE’s position as a conglomerate is priviledged by being able to have a higher R&D budget for additive manufacturing, and being able to lower the learning curve of 3D printing as they move towards the different divisions they have!

On November 15, 2018, nikolasra commented on Empire State of Minds: Open Innovation in NYC :

Thanks so much for this article! I find your second question to be very interesting. I think that we live today in a world where ideas are not that valuable anymore to the vast amount of information. Hence, the trade-off between transparency and idea theft seems to be more inclined towards increasing benefits of transparency at a decreasing expense of idea theft.

On November 15, 2018, nikolasra commented on SenseTime and Public Safety :

Very interesting article, thanks! While the ethical topics might be controversial and the technology poses a substantial threat, I think it is not the primarily role of the company to determine what is “good” and what is “bad”. While we as a society hope that companies will operate with corporate responsibility, it would be foolish for the government not to regulate the potential ramifications of high-tech innovation. Since the government should represent, and work, for society, I think the government is the organism that is most likely to do a reasonable job of mitigating the potential threats.

On November 15, 2018, nikolasra commented on The future of energy: forecasting the weather? :

Very interesting Lori! Thanks! My first thought while reading your article was about all the different stakeholders that would benefit from the mission of optimizing energy consumption using renewable energy and machine learning algorithms focused on maintenance and weather predictions. It seems like there could be so many great partnerships within the private and the public sector! Hence, an additional key question I would try to think about in the short and medium term is: which organizations should we aim to partner with?

On November 15, 2018, nikolasra commented on Who Defines Beauty: Humans or Meitu? :

Irene, thank you very much for this article! I wonder whether the machine learning algorithm could have an input of a set of preferences from every user, so that the algorithm adapts. Some people argue that there are some universal beauty standards based on specific ratios in different paths of the body related to the symmetry of a person. Would we be able to discover a “true” universal beauty if we all input our different preferences?…..

Thanks Justin!! You bring up very interesting points. To your question of “how realistic it is for AM to disrupt the construction industry”, I think that the answer is heavily dependent on which market and which geography we are talking about. Construction tends to boom in countries with younger demographics, and typically, these countries tend to be rather developing nations. There are two main challenges in trying to disrupt the construction market in developing countries. First, housing is usually a very politicized sector due to the number of jobs that it generates for the “bottom of the pyramid”. Consequently, low-income housing tends to be subsidized by the government, making it harder for AM construction to compete. Additionally, the labor cost of construction workers in such nations is really low. Hence, the advantage of reducing labor would not be so high on a cost basis. Nonetheless, the housing market in developed countries is still very large, and probably much more feasible to disrupt due to the lack of the two factors mentioned above.

On November 15, 2018, nikolasra commented on Predictive Maintenance at NRG :

Thanks for the article! The trade-off between the need to have more data to improve machine learning as quick as possible, and the loss of the competitive edge is super interesting. However, I think there could be a scenario where the competitive advantage is in the development of the machine learning algorithm to predict maintenance, and not necessarily in the data itself. Alternatively, one could think about setting up a subsidiary company that is in change of developing these algorithms and collecting the data through partnerships. By doing so, this “subsidiary” could offer the power plant operators an outsourced service so that so that the operators can keep doing what they do best and not worry about developing new internal capabilities!

Very interesting Masato! It is great to see traditional players recognizing the need for change. I do agree with your idea of partnering with VC funds in the short term instead of building their own right at the start in order to learn from the business and have visibility to many of the potentially disruptive technological innovations that exist today and the ones that might develop in the future. I see how not only the skill sets, but also the culture of Mitsubishi Corporation versus the one of a VC fund is too different in order for them to have a good shot at being successful with an internally developed VC fund. It would be interesting though to see whether medium term an in-house VC fund could make sense in order to have more agency as to which projects the VC fund invests in.