To outsource innovation to the public in the pharma world makes perfect sense. This is a great article that demonstrates the power of open innovation. At the end of the day, this is an industry in which people are driven to save lives and improve the quality of care to patients. The real problem with pharma companies’ R&D hurdles these days seem to be the lengthy and costly processes. Companies have to constantly make trade-offs between projects to prioritize and go for projects that have the highest probability of success with that of the lowest risk. However, it makes perfect sense to spread that costs over to the public who may have creative ideas or a better way of solving problems. Any time there is a lengthy development cycle for innovation, companies run the risk of losing talent and having a committed team from start to finish. Open innovation system shifts the control back in the companies’ court and allows for the best and the brightest to solve problems along the development cycle. I enjoyed reading this article.
Fantastic article. I had not realized that UNICEF Innovation Fund raised over $14 million to fund early-stage start-ups with open-sourced technology. This is a great example of a leveraging innovative process design to create social value and make a positive impact. I will be sure to follow the fund’s performance to see how its short-term focus of strengthening innovation leveraging block-chain, fintech, wearables, 3D-printing, and other groundbreaking technologies play out. That said, it is quite controversial, as you pointed out, that a global philanthropic organization that is working with the most vulnerable children around the world is inherently using these children as a fertile testing ground for innovation. I totally agree with your assessment that this risk of exploitation is further heightened as data is central to all of the frontier technologies, and yet children have limited control over their personal data.
I, too, have little doubt that AVs will be a staple of our transportation systems in the future. From individual vehicles to public transportation, I truly believe autonomous will be the norm. However, I agree with you that the transition from existing systems will differ drastically across the world and the implications will be widespread. That said, transportation is inherently from point A to point B. What happens when one city advances much faster than the other? As you said, to be completely safe, “there needs to be a critical mass of AVs on the road”. Also, as we’ve seen in the mobile market, Androids and Apple operating systems are inherently not compatible. From charging wires to applications that we download to use, there exist different versions for each. What happens when not just Waymo but other AV makers hit the road? How will these cars drive next to each other? Will it be safe for the riders?
I thoroughly enjoyed reading this article. Using 3D printing to build houses seems like the obvious answer to how we will build communities in the future. I was especially struck by your report that “the amount of waste causes both environmental and economic challenges. From an environmental perspective, roughly 136 tons of construction waste is sent to landfills each year in the United States alone”. A question that came to my mind is whether this Chinese company should advance its technology to incorporate the varying method of constructing these houses based on different geographic locations of the structure. For example, in Arizona, houses can be stood up quickly because there is no earthquake or other major natural disaster risks. In contrast, a deep architectural foundation needs to be constructed prior to building any concrete buildings on top in places such as Taiwan, San Francisco, or Florida because of the risk of earthquakes and hurricanes.
I am curious, in a similar manner as Mike’s comment” that whether 3D printing for chocolate (food in general) will just be a fad or will it become the “new normal”. While it is entirely obvious that the food industry is headed towards more customization, I wonder if such customization is simply in the appearance and physical aspect of the final product or is it also within the taste and ingredients used. On the subject of “luxury”, similar to watches, I perceive hand-crafted goods by a skilled artist as a luxury and am willing to pay a premium for it but what happens when it is made simply by a machine? Will that actually give chocolate makers a competitive advantage?
At the end of your analysis, you posted a few questions. You asked that for a company who has long abused its seeming monopoly over seeds, does this aggregation of data give them too much power? What are the risks in Monsanto owning all data on crop outcomes? We know using machine learning can help improve crop yields, but what is the price? I am a strong believer in needing advanced technology to feed the future population. Machine learning is one way to increase crop yield. And with machine learning comes management and control of data. While it is entirely possible that one company that holds the aggregation of data will come to have more power than its peers, I do believe in the power of market competition. If this technology proves to be lucrative, other firms within the space will likely enter to drive down the price of the offering and enhance technological edge to take a slice of the pie. At the end of the day, the problem that needs to solved is feeding the world’s population by increasing yield crop and whoever can do it first will get to set the price but eventually a price equilibrium should be reached where the market is sustainable for the population to purchase (otherwise government will have to intervene).
I wonder what happens when the user’s dating preference changes or when a user decides to switch from casual dating to serious dating. You said in your analysis that “the chatbot will select a unique member profile based on your search preferences, suggest ideal locations where you and your date can get to know each other based on shared interests and give dating advice to keep your first date jitters at bay.” The problem I see with any machine learning algorithm is that what happens when your historical behavior (i.e. your search history) is not a good indicator of your future preference, or better yet, it is not a good prediction of what is actually a good match for you? How can machine learning advance its algorithm in a way that it not only predicts a likable partner based on superficial elements to the user but also finds a partner that is actually good for both parties?
You mentioned in your analysis that “the true value for the Company will come from its ability to better understand the broader hidden “patterns and insights” within all of Fibit’s user data”. While I agree with your assessment, I also wonder how human intelligence can help augment and make decisions based off of the prediction element of the Machine-Learning-powered insight that comes with analyzing the data. I also wonder whether there is an inherent bias that exists from the data set that is being collected by Fitbit. In other words, most people who wear a Fitbit device are already health conscious and likely making healthy decisions and taking health-oriented actions on a day to day basis. I wonder how useful is this data when Fitbit attempts to scale the application to the general population. Can it be scaled and applied on an apple to apple basis or do we do need to start from scratch and use a completely new set of data, in which case would render Fitbit’s data useless?