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On November 15, 2018, petersonnenberg commented on Crafting a Tailor-Made Bone – Leveraging 3D Printing in Healthcare :

Great read, PK! It is fascinating to see how 3D printing can play an important role in healthcare by making tailored bones and other kinds of tissues. Besides the important social impact that this technology brings, building a sound and scalable business is still challenging. Making partnerships with governments or non-profits focused in healthcare innovation can be useful for companies to strive in their early stage while they prepare for growth. To your point, for this technology to reach scale, it is important to keep on researching new material and processes that could make 3D printing cheaper.

On November 15, 2018, petersonnenberg commented on How can ML help Fox predict box office performance? :

Great post, Miguel! It is very interesting to see how Fox is taking advantage of machine learning by partnering with Google. In an competitive environment where Netflix and Amazon have huge access to data due to its entirely online distribution, the traditional film studios are increasingly at risk. Therefore, machine learning can be an important tool for traditional studios to increase their efficiency and remain competitive. To your question, I believe that algorithms can deal with the subjectivity of movies, a form of art, by assessing human reactions to it, something that online players, such as Netflix and Amazon, have done so far.

Great read! It is fascinating to see football clubs taking advantage of data analytics and machine learning to improve their performance. It is a great evolution from the time when clubs hired people to count how many minutes each player kept the ball, how many passes they completed, and so on. I believe that discipline and tactics informed by machine learning analysis can be a competitive advantage in soccer. Germany, the 2014 world champion, is a good example of a team that relied on a very tactical and disciplined game, informed by data, to win the biggest soccer tournament.

On November 13, 2018, petersonnenberg commented on Duolingo: From Hello to Hallo through Machine Learning :

Great post, Jackson! Very interesting to understand how machine learning can make learning foreign languages much more efficient!
Technologies such as Google translator can be a “magical” tool for travelers and people who don’t use foreign language frequently, acting as a counter-incentive for the learning of a new language. This might cause the market of learning foreign languages to decrease in the future and put Duolingo in a challenging situation. One possible strategy for Duolingo would be, as mentioned in the post, to leverage their capabilities and play in the learning of traditional subjects, such as physics and reading.

Great post! Being myself very health-conscious in terms of nutrition, it is great to see that advances in this area will lead to more tailored and efficient diets! I completely agree that it is of huge importance for Nuritas to state its mission, as it should be a north for their growth. If Nuritas’ goal is to provide better nutrition in large scale, it can make complete sense to partner with well established organizations in order to leverage their structure (R&D, marketing & sales, distribution, …). To your question, I believe that a pharmaceutical company would indeed require Nuritas to be their exclusive partner due to the importance and confidentiality of R&D and commercial strategies for a given product containing peptides.

On November 13, 2018, petersonnenberg commented on I, Robot Meets Wall Street: The Age of the Robo-Advisor :

Great post! Very interesting to understand how a financial service firm like Charles Schwab has been using machine learning to become more efficient! As it was well explained in the article, robo-advisory has been very efficient and precise so far, but it still relies on humans to cross-check its recommendations. I believe that this combination of machine plus human will hold for very long in the financial advisory business: as humans need machine to be more efficient, machines will need humans to deal with human interactions, which aren’t always driven by logical arguments that can be understood by algorithms.