Albert Einstein's Profile
Thank you for the insightful post. The application of additive manufacturing employed by Align Technology serves as a great example of the impact that this megatrend can have. By replacing plastic thermoforming for 48 individual molds with an all-in-one production process focused on stereolithography, Align has significantly reduced labor costs and increased overall productivity within the clear aligner industry. Regarding the Company’s recent decision to scale operations by establishing production facilities near locations with high demand, I think this could prove detrimental to overall profitability. As you mentioned, Align’s success has brought forth a number of competitors, such as SmileDirectClub, which are undercutting Invisalign prices largely due to their direct-to-consumer model. Establishing additional production facilities would require significant capital expenditures which, given the current level of competition in the space, I do not believe that Align should incur. I expect that the delivery timing for molds is not a material factor in a customer’s decision to begin using clear aligners. As such, I suggest that Align partner with a well-regarded parcel company to deliver locally manufactured products to emerging regions.
Thank you for this piece. The potential impact of machine learning within the healthcare space is inspiring. The price tag that Roche paid for Flatiron certainly corroborates that they too are believers in this technology. I found Flatiron’s hybrid human-machine learning model particularly interesting as it illustrates the role that humans still need to play in the product development and calibration of machine learning algorithms. In the debate of human vs. machine, I am a firm believer that both is the best solution. One question that comes to mind upon reading about the effectiveness of OncoEMR revolves around timing – why isn’t this technology being rolled out more broadly? In regards to the question about the consequences of having a single firm control this type of technology, I would point out that we are still in the very early stages of product innovation so I would expect competing firms to rise up and challenge Flatiron as their offering matures.
Thank you for this post. Your research very clearly outlines how machine learning helps the efficacy of Hinge’s matching protocol. I find the details around the volume of data that the Company is capturing highly compelling. If there is anything that we have learned from case discussions around artificial intelligence (i.e. IBM Watson and Aspiring Minds) is that the quality of data absorbed by machine learning algorithms is a determining factor in their ability to make accurate predictions. By allowing users to like specific components of profiles rather than just “swiping right,” Hinge is giving themselves an edge in the category of data quality over competitors such as Tinder or Bumble. However, with quantity being another major component of this equation, the volume of users that prefer to simply “swipe right” may be greater than those in search of a more curated experience. In regards to the question about perpetuating biases, I think that the benefits afforded to us by machine learning algorithms in this setting may actually outweigh the potential risks. For example, a recent article by The Economist, noted that by giving users the opportunity to filter for compatible matches with similar interests, such as religion, platforms like JDate may actually produce longer-lasting relationships .
1. The Economist, “Online Dating Modern Love,” August 18, 2018, [https://www.economist.com/leaders/2018/08/18/modern-love], accessed November 2018.
As a recent 23 and Me participant, it is worrisome to learn about all that the Company is likely doing with my personal data. However, the potential for 23 and Me’s partnerships with healthcare / pharmaceutical firms like Pfizer is quite compelling. If we can leverage the digitization of genetic information to better match individuals to clinical treatments, then the argument can be made that the benefits of reducing our privacy outweigh the risks outlined in the post. Another area where this dilemma may manifest itself is in online advertising, where the more that a user’s profile is known, the more relevant the advertising that they are exposed to gets. With the digitization of most industries well underway, the abundance of available data will only grow amplifying the volume of cybersecurity risk in most markets. While this presents a concern, I do not believe that we should allow it to derail our efforts to innovate.
Thank you for the thorough piece. It is very interesting to see how NASA is generating new ideas through Open Innovation. I find the process very similar to what some large corporations have sought to accomplish via in-house incubators or corporate venture capital arms and could not help but to think that a similar model may help to institutionalize NASA’s efforts. For example, some consumer-focused companies such as Chobani, have set up incubators through which they gain “early looks” at brands that they otherwise would end up having to acquire for substantially higher prices in the future . As a government agency, NASA should consider establishing an incubator through which to award grants to teams pursuing new ideas or developing startups tackling the organization’s biggest challenges.
1. Angelica LaVito, ” Chobani Incubator to Help 7 Companies Take on Big Food”, CNBC, September 26, 2018, [https://www.cnbc.com/2017/09/26/chobani-incubator-to-help-7-companies-take-on-big-food.html], accessed November 2018.