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On November 14, 2018, LW commented on Boeing: “Adding” to the Manufacturing Process :

It was definitely interesting to read about how much Boeing has invested in additive manufacturing, particularly given our discussions about Boeing in class and their emphasis on a very protracted and highly costly product development cycle. Innovations in this field can be a huge advantage for the company, but the question is how they will be able to proof out the concept to appease regulators and the general public in terms of safety standards and perceptions. Boeing might have to devote additional resources in marketing, safety R&D, and testing because they are at the forefront of AM in their field.

The applications of Additive Manufacturing to aid and disaster relief is incredibly compelling, particularly given the areas you described around the inefficiencies surrounding supply chain, disbursement of charitable contributions, and sensitivities around timing. I wonder how feasible it is to use additive manufacturing during disaster relief given the high cost barriers, and how people are solving for that challenge through innovation and scale. The idea of finding more cost effective power sources through solar also is a great option, and hopefully more resources and work is devoted to this field moving forward.

This was super compelling, and I was particularly astounded by the statistic on how many teachers in America currently use the platform. I think given the great disparities in spending and funding across public schools in America (as evidenced by your Massachusetts example), open-sourced platforms such as these will act as a great equalizer, and I would be curious to know how the company plans to expand potentially even internationally or to other underserved communities (e.g. remedial education, vocational learning, etc). I wonder also how effective public school systems and charter school systems can be in heralding open innovation platforms amongst their communities and networks, and how they can better propagate usage of platforms such as Teachers Pay Teachers.

On November 14, 2018, LW commented on LEGO: Leveraging the Building Blocks of Open Innovation :

How Lego is choosing to embrace their digital edge is really interesting, particularly to learn about how children today seamlessly integrate hardware and software in play, and are much more comfortable crowdsourcing their opinions and sharing innovations through social media and public platforms. Lego Ideas also seemed like an ingenious way to crowdsource innovation for Lego, particularly as they fight to stay relevant in the toy and play space.

On November 14, 2018, LW commented on Alibaba: Application of Machine Learning in Logistics :

The question of how Alibaba should proceed in terms of applying machine learning and technological innovations towards logistics planning was super interesting, particularly in light of their acquisitions and expansive collection of data both around consumers and delivery logistics. I also wonder how they can apply this technology and data as the Chinese population shifts and adapts so rapidly (e.g. rural to urban, road and infrastructure changes), and what additional channels (e.g. partnerships, acquisitions, innovations like drones) they can use to drive for efficiencies and scale in the logistics space.

On November 14, 2018, LW commented on El Bulli: It Could Be More Than Foams and Olives :

This was an interesting application of machine learning that I hadn’t considered before, I was curious on the choice to apply machine learning to food and culinary innovation, and why you chose to consider it for the most cutting-edge and high-end dining sector compared to something more mass market? There are definitely many innovations in machine learning and science/chemistry that can be applied to the field of gastronomy, as you pointed out. And then in the field of profitability it would be interesting to hear how applications in fast food or mass market food consumption machine learning data can be applied to a place like El Bulli.

This definitely resonated with me as a long-time (and hopefully high-value!) ASOS shopper. It was interesting to learn how they combine churn modeling with LTV to come to customer lifetime value, and I’m curious to hear more about how their marketing and retention strategies deviate for different customer value segments, and what retention strategies are effective in reducing churn for each of those segments (e.g. coupons, search advertising, email campaigns etc.) and how they test the efficacy of these strategies. The idea of having a company fire their lowest value customers is interesting as well, particularly as customers can also move up and down the CLV chain, and because this type of decision can lead to negative press or brand risk.

On November 13, 2018, Lily Wang commented on Using Machine Learning for Crime Prediction :

This was super interesting to read, particularly surrounding the debate around the ethical implications and algorithmic accountability of a tool like PredPol, and how to prevent biases and racial discrimination. I wonder also whether police officers might use Predpol to fall victim to confirmation biases, or even use it to justify any “false negatives” in the data that lead to police brutality or false arrests. As described, it could also help correct for predetermined biases and errors in human judgement based on the proliferation of data that Predpol uses, but ultimately we know that the results of even the most advanced machine learning models are contingent on human decisions made at the end.

On November 13, 2018, Lily Wang commented on Can machine learning provide a solution to CNP frauds? :

This was a really interesting read because we dealt with this problem in numerous ways at my old job. One way that payments companies approach CNP transactions is to charge higher and often fixed fees surrounding CNP to buffer themselves from fraud loss or chargeback losses. It was interesting to learn about what FICO was doing, given the breadth of their customer credit data, and likely sophisticated understanding of the buyer and their purchasing and credit patterns. At Square our Data Science team devoted significant resources to develop risk models around fraud for CP/CNP transactions, particularly around clustering of risky buyer/merchants, and tree mapping of related parties to detect sketchy actors who are mapped to the transaction within certain degrees of separation.

I thought this was a really compelling topic, particularly in how Facebook is using machine learning and AI technology to curb the proliferation of fake news. One question I had was how Facebook balances objectivity when they combine AI and human fact-checkers to review and decide if news sources are legitimate. How will they show impartiality, or judge what news is “fake” when the lines are more blurred? An interesting perspective on this was also raised during Facebook’s Senate Hearings recently, when there were numerous allegations of liberal bias from Republican senators.