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On November 14, 2018, TOM_HBS2020 commented on Hinge: A Data Driven Matchmaker :

Really interesting article! Online dating applications learn from our biases in order to produce matches, and thus I do not see how machine learning could eliminate bias in dating the way that the process is currently structured. I don’t believe that we can ever eliminate bias from the dating process, as we are naturally biased towards certain characteristics (ex. intelligence, good looks) in order to pass these traits on to our future children. However, by broadening the nature of the questions asked and the answers that users are matched on, we can work towards a broader definition of these characteristics, thereby checking our own biases.

On November 14, 2018, TOM_HBS2020 commented on 3D Printing Straighter Smiles :

Great article. I do believe that Align can leverage its large set of data from dental plans. The company can look to the data to uncover trends in current usage of the technology and apply these findings to their production process. For example, these trends could aid in “decentralizing” the process in the article as you mentioned as a possible next step– Align will learn from its data where the product is utilized most often, and increase the number of machines in that specific area. Align can also use this data for marketing purposes to generate consumers, resulting in decreased fixed costs/unit produced.

On November 14, 2018, TOM_HBS2020 commented on Shooting for the Stars :

NASA’s use of open innovation is fascinating, especially given the general conception that the work it does requires deep scientific expertise. I believe that NASA can continue to use open innovation efficiently by avoiding truly open-ended questions. NASA has gained value from narrowly defined questions that directly benefit from crowdsourcing– such as the example you provided of 21,000 volunteers studying more than 100,000 images and identifying 5 million objects. This project benefited from the sheer number of volunteers, who provided more manpower than just one astronomer at NASA. By strictly defining the question, NASA will not get distracted by ideas.

I believe that training machine learning algorithms using high quality, representative datasets would minimize bias. I would be concerned about bias if the algorithm is trained on a database enriched for certain characteristics. For example, if the data set includes a high number women and high number of cardiovascular events, then the algorithm may learn to associate cardiovascular events with women. However, I believe a great benefit of machine learning algorithm is that the process reduces human bias that naturally occurs during an in-person physician-patient appointment.

Housing is such an exciting application of additive manufacturing. I believe that Contour Crafting should partner with both governments and non-profit organizations to scale their impact. The potential for this technology in developing countries is huge, as numerous non-profits work on increasing access to housing but have fallen short in their reach. The low cost of 3D printing a home is particularly remarkable, and governments would certainly be incentivized to partner with Contour Crafting for this reason. One issue that may arise is access to urban land; 68% of the world’s population is projected to live in urban areas by 2050 [1]. Thus, Contour Crafting will need to consider how to apply 3D printing for housing in light of increased urbanization.

[1] 68% of the world population projected to live in urban areas by 2050, says UN. United Nations, Department of Economic and Social Affairs. . Accessed 11/14/18.

On November 14, 2018, TOM_HBS2020 commented on Crowd-sourcing the Secret of Life: 23andMe and Open Innovation :

Great topic. I agree that it is important to question the quality of 23andMe’s research studies. I would be particularly concerned about selection bias, as individuals choosing to participate in 23andMe may be more homogenous in genetic composition than an entire population. Thus, the generalizability of the studies to other individuals remains low at this time. I would agree that the results must be interpreted responsibly, and question whether the results of these studies are actionable in today’s world. The medical conditions studied in 23andMe result from an interplay of genes and the environment not yet well understood, and so it remains unclear whether individuals are harmed or benefit by making lifestyle changes once aware of their genetic predisposition to certain conditions.