Rupert Thaddeus Mortimer III

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On November 15, 2018, Rupert Thaddeus Mortimer III commented on A 3D-printed liver: not ready for prime time? :

Sir Bernard, I am intrigued by your excellent choice of topic for this article. Let me attempt to address your second question as to whether Organovo should focus on transplantable liver tissue while “bigger” issues like vascular supply still exist. It probably makes most sense for Organovo to do what it is best at — in this case, using additive manufacturing to develop synthetic livers. While academic research may not have caught up in terms of vascular supply, why wait? As a private company, Organovo can conduct its own research, study its own additive manufacturing processes, and refine its final product such that by the time academia has caught up, it will be well equipped to deploy a comprehensive solution that truly serves the entire industry.

On November 15, 2018, Rupert Thaddeus Mortimer III commented on Additive Manufacturing at GE Aviation :

Mr. Carlos G., thank you for sharing this impressive and well-written article. In my opinion, the recent cut of the dividend at GE will free up cash flow constraints that can hopefully one day be invested into more R&D developments. Now, to answer your question as to how General Electric’s competitors will respond? I believe, as with any competitive environment, they’ll accordingly invest capital and human resources into being able to add this capability to their manufacturing plants. Just as now established forms of manufacturing were once considered “new,” GE’s competitors may react swiftly to make sure they don’t lose competitive positioning in additive manufacturing.

On November 15, 2018, Rupert Thaddeus Mortimer III commented on Empire State of Minds: Open Innovation in NYC :

Ms. V, thank you for sharing this eloquently written article. New York seems like the perfect city to deploy an open innovation initiative, given its population and the high frequency of subway usage. It adds a democratic element to what is normally a closed, bureaucratic process of transportation development. To answer your first question, I believe it is valuable for government agencies to introduce some monetary element to increase participation in such initiatives (such as a modest cash prize), but overall citizens (especially frequent users of subways who have to deal with its problems on a daily basis) will also have enough self-driven incentives such that they will be motivated to contribute.

On November 15, 2018, Rupert Thaddeus Mortimer III commented on How can a traditional conglomerate (aka a global business producers) can be innovative? :

Mr. Nakamura, I am delighted to see your article on Mitsubishi Corporation. Like the focus of my article (a historied enterprise called Sherwin-Williams), Mitsubishi is well-known as a mature and established business, so it is always fascinating to see these companies adopt open innovation. One risk that may materialize is that because the end-result of open innovation is often rather uncertain (sometimes capital and human resources can be poured into open innovation efforts with low probability of commercial success), and because the practice is relatively new, it may be difficult to gain institutional buy-in from senior management, making it difficult to push forward on new projects. Hopefully Mitsubishi adopts some of these practices you mentioned however — I believe they show lots of promise!

On November 15, 2018, Rupert Thaddeus Mortimer III commented on Beauty Brand with Droves of Data: How Glossier Employs Machine Learning :

Ms. Enns, I thoroughly enjoyed your article! And I am not-so-ashamed to admit that I occaisionally am known to enjoy a pleasantly refreshing Glossier face mask from time to time…but I digress. To your question regarding being able to utilize data on an integrated basis across channels, I believe this is thoroughly possible thanks to the rise of e-commerce shopping habits among consumers, and the use of credit cards. Glossier is in a unique position where it can track consumers’ shopping across various channels by potentially linking credit card numbers, and this would allow them to understand why and when consumers make certain purchases. Do certain promotions work better on certain channels? Do certain platforms (smartphone, website) work better for different products? These sorts of questions can be addressed and processed with machine learning and widescale data collection.

On November 15, 2018, Rupert Thaddeus Mortimer III commented on Easier Than Shoplifting: How Amazon Go is Revolutionizing Brick & Mortar Retail :

Thank you for sharing, Mr. Ford. I believe Amazon can continue to build a sustainable competitive advantage against other data-driven retailers like Walmart partially due to the fact that it can leverage huge amounts of existing data. Unlike Walmart, Amazon has Prime members, access to each of its customers addresses and payment methods, and now, rich data feeds from its brick-and-mortar stores. Not to mention the huge quantities of seller-generated data as Amazon has virtually no limit on the “inventory” it shows on its website. All in all, I believe Amazon was built with a focus on data collection from its origin, and that will allow it to continue building a sustainable competitive advantage in which machine learning will play a greater role over time.

On November 15, 2018, Rupert Thaddeus Mortimer III commented on Sales=MC2: Salesforce’s Attempt to Democratize Artificial Intelligence :

Mr. Einstein, I am most impressed by Salesforce’s marvelous innovation which bears your namesake. I can see many useful applications for the product from a salesperson’s perspective — it appears to help them understand their weaknesses and ultimately increase their effectiveness as salespeople. However, I wonder if there are any legitimate concerns around the sheer amount of data that Salesforce has access to via this product. Are companies comfortable with allowing a cloud-based service like Salesforce to have access to detailed internal “voice notes” that their salespeople generate? Could there be valuable competitive data that is being given up, in exchange for the relative benefits that AI provide? The functionality of the tool is useful, but I wonder if companies would prefer having that data secured internally.