Tom_Student's Profile

I think that 23andMe is an incredible company, using crowd sourcing and open innovation in healthcare. I personally have used the platform to learn more about my genetic history and have found the findings fascinating. However, I do have concerns about the risks of data security. It was not until after using 23andMe that I was aware of the fact that law enforcement and federal government can pressure the company to share customers’ DNA [1]. Going forward, I think this risk is critical for 23andMe to implement more transparency around how customers’ data can be used. There should be clear disclosure to consumers in advance of using the 23andMe platform as to their policies on data security and privacy.

[1] https://www.cnbc.com/2018/06/16/5-biggest-risks-of-sharing-dna-with-consumer-genetic-testing-companies.html

It is clear that Align will need to outcompete a growing number of competitors to maintain market leadership. However, one asset that Align has at this point in time is trust. With medical products – including dental – reputation and trust are critical to growing market share. As a first mover using 3D printing in the orthodontic market, I believe that Align has a clear advantage in brand trust. It should continue to leverage consumer trust in its brand, in addition to exploring how it can incorporate new technologies.

What I find most striking about the use of algorithms in music creation is the way in which new features are incorporated. As you mention, evolution as an algorithmic process involves passing on traditions while incorporating new features. New features are more difficult to derive from algorithms than previous patterns are. However, in fine arts, we have seen artists create new works using algorithms. For example, the canvas print of “Portrait of Edmond de Belamy” was the star of Christie’s autumn print sale this year. The Financial Times [1] discusses how artists are exploring AI’s potential for independent creativity, which would allow for algorithms to come up with “new features” without human intervention.

[1] “The world’s hottest new artist: an algorithm?”(2018). Financial Times. [https://www.ft.com/content/18d72352-a5f6-11e8-8ecf-a7ae1beff35b]

Thanks for writing on this important topic. The potential of 3D printing to create housing more efficiently is exciting. However, I wonder if this technology is well positioned to address homelessness. Given that homeless populations are typically unemployed and/or have minimal economic means, who would fund such housing projects for the homeless? I understand that Contour is developing 3D printing capacity, but it is unclear to me how their business model will address homelessness while still achieving commercial sustainability.

Yes, I believe that firms will continue to employ humans in customer service. However, I think it is likely that the number of humans employed will be reduced, as some tasks can be automated. On the other hand, certain tasks that require human-to-human interaction and/or on the spot judgement (without historical precedent) will never be automated among institutions that offer excellent customer service.

As firms begin to use more machine learning, it will be critical that they maintain control over the development of machine learning algorithms. Therefore, machine learning should be developed in house to ensure that firms – particularly financial institutions – can exercise sufficient influence and regulation over their business.

I like that this article calls out both the benefits and the risks of digitization of energy assets. I am curious to learn more about what is required for Enel to build the market for peer-to-peer (P2P) energy trading. How robust is the blockchain solution that Enel is piloting in a P2P market and what will it take to achieve high levels of adoption and usage? In addition, I would like to know more about policy incentives that will either support or hinder the development of such a P2P market.

Your question around the potential for scale in Clover’s open innovation model resonates with me. While I love the open innovation model to guide product development at a restaurant, it seems hyper localized and best suited to operations that are small in scale. I am curious to understand if Clover has experienced any risks to-date from open innovation, and if they have thought through control mechanisms that would be required for this model to scale.

Einstein’s ability to use artificial intelligence to connect Salesforces’ customers with their users seems incredibly powerful, if implemented properly. I am curious to understand more about the painpoints of customers who have experienced “mixed results” from the Einstein tool. What aspects of Einstein are working well, and where is there room for improvement? What additional underlying data would be helpful to improve Einstein’s outcomes?