Just to Noah’s points:
1. On the other hand, customers have no profit margin to put over the cost they are paying for whatever input materials they are buying. Maybe the advantage companies have on that is offset by what they charge over what customers would pay by doing it themselves.
2. Brands could still exist as design brands. They don’t need to actually manufacture the product to be a thing. Brands would also become much less defensible against piracy in a world of ubiquitous 3d printing.
3. Is there really all that much innovation still to be made in sneakers? Do people really care that much about it? Maybe they care less about it than about being able to have customized sneakers anywhere and anytime they want.
I feel this has a lot of potential for more lower-end restaurants, where people really don’t care much about the quality of what they are eating, but more about getting it fast, cheap and with lots of fat or sugar in it. In the higher-end restaurants though, I am afraid customers would feel like paying for a much lesser experience than that they have with an human chef. Part of the fun of going to different restaurants is actually experiencing what different chefs have to offer and this could give out a “standardized” feel anywhere it is used.
I liked how Valve used the trust score to put cheaters to play against cheaters, protecting the experience of those who wanted to play fair. Cheating in videogames has a long and proud history and it is arguably even a part of the culture, finding cheat codes, passwords, secret shortcuts and other similar things to improve performance, and some people like to use those. On the other hand, in a very competitive game like CS, of course a lot of gamers will want a fair playing ground to really test their skills, and that is fine as well. To be able to recognize that there are different types of players and offer a different experience for them based on their differences is a good solution for the cheaters problem.
It is very frustrating to know how it can take 15 years for a life-saving therapy to get to market in a process that seems to have so many opportunities for acceleration. Is it partly because it is hard to actually find an adequate number of viable volunteers for each phase of clinical trials? If so, can machine learning really help Roche find volunteers without access to external databases with information on potential volunteers?
I agree with ABCDEF, it is probably more a fault of the designers than of the technique. There are some better examples of how 3D-printing can be used in construction in a way that does not look awful, like in this link: https://www.3dnatives.com/en/3d-printed-architecture030520174/
I am a heavy user of Duolingo and retention can really be an issue even for users like me. It is really hard to keep motivated once you’ve “completed” a language you started only for fun, a language that is not a part of your daily environment. Maybe they could use the knowledge they have about users to keep them engaged with the language through media of all kinds, beyond just the actual course.
I also agree with what Taylor Stockton commented before me, that they need to go into other educational spaces. There is a lot of potential for its method in test prep, like for the GMAT, CFA or other kinds of exams. As someone who has been starting and giving up learning to code for a long time now, I can also see how much it would help to have a Duolingo for coding just to help students remember certain rules and tags.