This was a great article and I had never thought Wikipedia was such a stark example of open innovation – essentially crowd-sourcing an entire encyclopedia and have it monitor itself. What a fantastic idea! I particularly loved the positive feedback loop to create and manage new content. I think an innovative way to make this sustainable would be to reach out to the wikimedia community to source potential revenue generation ideas. This way, open innovation and crowd-sourcing will be used to benefit not only the actual content, but also the operations of the organization. Employees would thus have input from every user (seeking ‘karmic’ balance) and would be able to pick the most promising ones from the lot – an example could be building a teaching platform that can be used by schools in more remote parts of the world without access to schools/teachers.
Open innovation has more potential to be unlocked if used across the length and breadth of an organization/industry. The real challenge is to have a suitable incentive for people to engage.
Very interesting article to read – crowd-sourcing ideas to fuel innovation has often been a trait of the food and beverage industry. Lays ran (or still runs?) a similar program to source flavors for its product line. Ultimately however, I feel more than fueling true innovation, it is more a marketing gimmick that introduces more complexity in the supply chain than actual sustainable products. I do agree with the point made about using crowd-sourcing to enter a new market though – this will allow for greater adaptability to a portfolio that hasn’t entrenched itself in a new market yet.
Open innovation unlocks it’s true potential when used to solve problems faced by organizations (identifying flavors/drinks to stay relevant in the market doesn’t qualify for me). I absolutely love the idea to source process improvement ideas from the baristas though. It is quite similar in vein to the Toyota practice and embodies kaizen in a truly modern day format.
This was a very fascinating article about the benefit of additive manufacturing in healthcare. I absolutely agree with the fact that as time moves on, we will need to incorporate use of these tools in the medical training curriculum to better prepare our surgeons for upcoming advances. Generative design is also more possible with the increasing use of EMRs and EHRs augmenting the learning process. I do wonder though whether the costs of such procedures would increase or decrease with time – on one hand, the product is better and can cause longer-term cost efficiencies, while on the other hand, allowing too much customization always comes at a cost.
One way to increase uptake could be to work with government regulators and the payer network to ensure that these procedures are covered, fully or partially, under the current insurance coverage programs. Another way would be to work with government again to subsidize the cost of procuring raw materials or equipment basis the application (defense vs civilian).
This was an absolutely fun read and evoked ‘Willy Wonka’ memories to even imagine a world where chocolate can be forced to bend to our wills. I completely agree with the argument that customer-designed 3D-printed chocolate will be an instant crowd-pleaser. As I think more on this however, the following major concerns cross my mind:
1. What is the real differentiator here for Hershey’s? Is it the customized chocolate or the feeling of exclusivity that a customer gets on obtaining it?
2. To build off of Q1 above, if the differentiator is the customized chocolate, then is it really that much of a barrier to entry to deter other, ‘premium’ players from jumping on to the 3D printing bandwagon?
3. Ultimately, consumers will return to your brand for certain functional and associated benefits (think premium ingredients, brand value etc.) – is the 3D printing gimic really sustainable, not just in this case, but in the food industry in general, given that it isn’t solving for any inherent issues except boosting market perception temporarily at the cost of a more complex logistics network?
This was a very engaging article and a completely unconventional application to deploy ML. My only reservation with this is the fact that traditional ML systems need a ‘training data-set’ that is robust and exhaustive (to whatever extent possible) to learn benchmarks against which to then base difficulty levels and other stratification. In the context of this essay, this seems possible by using RM repeatedly across several student populations. But that could be because the core subject matter itself is fairly quantifiable (math!). Will this application be as effective for students studying history or art? And if it IS effective, then will more families opt for this mode of education, at the cost of the social experience that schools offer? For me these are the two possible negatives for an ML application in education.
On the flip side, I can see ML being deployed to study a variety of languages, where a student’s understanding of Spanish or Hindi can be measured against a benchmark, allowing her to then adapt the pace of her learning in sync with her imagination and creativity.
This is an excellent essay and throws light on some of the key benefits of incorporating ML in modern-day applications (AirBnB did not even exist decades ago when ML was established as a theory). I firmly believe that AirBnB can unlock more potential through the use of ML in ‘lodging’. However, to be able to make headway into the associated areas (to become the one-stop-shop), AirBnB will first need to establish itself in those areas to be able to gather data effectively. A direct acquisition would prove helpful in that regard.
For the vanilla lodging solution, it is important to note that user choices are influenced by a negativity bias in the reviews – reviews that are negative are trusted more than the positive ones. If the website actively tries to manipulate the reviews, then the trust between the user and the platform is compromised, affecting the overall credibility of the platform. AirBnB should be cognizant of these risks before opting to use ML to influence user choices.