John Harvard

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

Great article! I actually think if the competition starts working in 3d printing it could be to GE’s advantage. If everyone is trying to figure out the best way to 3d print, you would imagine someone will find a way to make the process more cost effective. This could lead to a new industry standard where everyone is 3d printing and they can work together to make industry best practices. I do see how cost prohibitive this currently is, but hopefully the new CEO can find a way to make it work 🙂 I have faith in him!

I think “What is the appropriate level of machine learning to incorporate but still allow Glossier to remain true to its brand?” is a great question to pose. Glossier’s unique business model “crowdsources the magic formula” as you mentioned, yet all of the crowdsourcing is happening prior to the product development phase in order to influence product development. Glossier also prides itself on taking a long time on product development in order to release the best product, and usually only offers one. For example, the milky jelly cleanser is the only face wash they offer. I wonder if Glossier has ever collected reviews on products once they have launched and has ever considered changing their formula. They could gather this data and utilize machine learning to create product improvements, however, that may be against Glossier’s DNA as I’ve never seen them edit a product once it hits the market before. If this were the case, I’m sure Glossier could still utilize machine learning to aid with product development and take away some of the manual scraping of social media comments, etc. Since Glossier is gathering a ton of data prior to product development, I think they can definitely build in machine learning to their advantage.

On November 13, 2018, John Harvard commented on Stitch Fix: Using Machine Learning to Help The Grinch :

I think there is definitely transferability with Stitch Fix’s algorithm to other industries. As you mentioned, Lake’s team created different algorithms on their own volition. With the right data scientists, I believe machine learning can be applied to many industries. I actually just watched an interview of Katrina Lake and she mentioned that she could see this business model being applied to the travel industry very well, something she may consider down the line, but thinks it would be transferable across many industries. I definitely agree – everything these days is data-based and when you have a large data-pool you should be able to utilize machine learning to improve.

On November 12, 2018, John Harvard commented on Will the Sport of Football Survive? VICIS Says Yes. :

I think VICIS can make a big impact on football but in order to do so they need to go mainstream. If the NFL rated it the #1 safest helmet, why is playing with this helmet not mandatory? I would urge the NFL to form a partnership with VICIS and subsidize the cost of these helmets for players or even provide them for free. I see that the price is prohibitive but the NFL needs to give away some of their profits and protect the health of their players. In 2017, NFL teams’ revenue exceeded $8B. With this being said, I think they are morally obligated to provide these helmets for players and do everything possible to reduce the risk of CTE.

On November 12, 2018, John Harvard commented on Burberry: Digitizing Luxury Retail with Machine Learning :

I believe that Burberry should develop ML capabilities in-house. The retail landscape is drastically changing and digitization and personalization are necessary to compete in the market. Since this skill set is something that will be imperative to Burberry’s success in the future, I think they need to invest in bringing talent in-house that can help give them a competitive advantage. This reminds me a lot of Walmart’s decision to buy digitally native companies such as Bonobos and Walmart realized they needed to embrace e-commerce in order to succeed and so they acquired digitally native e-commerce companies—they then can learn from these companies about their e-commerce strategies and apply them to Walmart. Outsourcing the ML job may help in the short term, but for long term success I would advocate for in-house ML at Burberry.

On November 12, 2018, John Harvard commented on Listen Up: Spotify, Machine Learning, and the Podcast Opportunity :

Your second question is a very interesting one: What responsibility does Spotify have to design algorithms that promote diverse artists and podcasters? Spotify will want to have diverse artists and podcasters because they have a very diverse client base—they will need a wide range of podcasts to appeal to their customers. However, Spotify ultimately wants their customers to listen on their platform, so if certain customers are not interested in a diverse range of podcasts then they will not want to show them a diverse range as that may decrease their chances of listening if they don’t see a podcast they might like. This runs the risk of having listeners view Spotify’s offering as not diverse, since they are only seeing a selective portion of their offering through the algorithm. If Spotify wants to show a diverse range of podcasts to all listeners regardless of listeners affinity for diversity, they would have to change their algorithm to include this and not only show podcasts that each person may be interested in— something that may be against Spotify’s DNA.

Regarding Alibaba’s work force, I think the company will soon realize that humans add a unique element that cannot be solved by machines or open innovation, and will want to hire back their employees. They are able to use intuition and make judgement calls that is just unique to a person’s abilities. This reminded me of the Valve case we had when the company hired in people who were competing with them. I think Alibaba should hire back the employees they let go to not only harness their tech developments but also utilize their unique decision making abilities.