Licensed Drug Dealer
This is super interesting. I never thought about the applications of additive manufacturing to food. I would be curious for some of these companies to explore what the supply chain implications for the food industry would be if they were able to scale this technology at a relatively cheap price. This could also save billions of dollars in terms of profits but also be a key to addressing food security issues.
One thing that Stryker can do is to look at leveraging this technology to custom implants for patients. This is one way they can differentiate from their competitors to create a close partnerships with the doctors to create customized solutions for their patients. If they are able to become closely associated with the doctor, that is an easy way for them to increase market share.
I would be interested in knowing how ML and past data trends especially around the biases of how the credit system is stacked up today will translate into this new credit system. There may be an ethical dilemma such as the one we saw with Amazon and its hiring AI which was systematically rejecting female candidates.
I think this is a super interesting article on open innovation and machine learning and how they combine together to solve a major problem. I am excited to see how the brand signaling effect plays into how much this specific company can scale up in comparison to the consumer preferences. Further I would be interested in the role of the designer would evolve as the ML algorithm learns more about consumer preferences.
I believe that once this technology is available that it will be scaled to as many industries especially places like retails scores including big box, fashion and other such areas. It would be interesting however to look at what percentage of people are coming into stores for the ability to buy something with convenience and who is coming in for the customer service experience and how this would evolve the role of retail workers.
I think this is an extremely interesting take on how Machine learning and crowdsourcing come together to create an interesting business model . However, for something I would be interested in would be how this plays out. I would imagine that ML in this case would converge onto a singular taste which is an amalgamation of a bunch of different palates. However given how tastes combine in complex ways and not as a smooth function, using an algorithm which depends on incremental improvement to get better might make a product which suits no single palate as there might not be a smooth signal and only noise.