While I agree with your recommendation that sidewalk labs communicate more clearly on how idea are ideas are actually incorporated, I wonder if the very nature of crowdsourcing makes that difficult. The input is only taken at the very beginning of a process without consideration for feasibility and costs. As the project moves on, the ideas have to evolve and take those factors into account that the average person may not be aware of. I think it’s worth thinking about applications where crowdsourcing is easier and harder and in situations where feasibility/costs are a big factor, how to tailor the crowdsourcing model at the right points to still be helpful.
Their explanation for pulling the program seems a bit lackluster. There is no reason a studio can’t try both the tentpole traditionally developed program in additional a smaller number of smaller bets. I think potentially there might be additional reasons why crowdsourcing doesn’t work in this instance. The success of a show isn’t just determined by a script. There’s a ton of work in iterative writing, advertising, production, directing that require investment beyond the initial script. So I think while crowdsourcing can get a lot of early ideas, it still requires a huge commitment of additional resources to make it successful, and that may be where Amazon executives weren’t willing to follow-up.
Amazing insight into an incredible technology that seems like it’s already in use. I’m wondering as the process gets more democratized (to your point), how the ecosystems deals with medical device regulation and quality assurance. Right now, if Stryker sells a product, they are liable for it’s quality and performance. If now a hospital makes their own implant from their own 3D printing machine and something goes wrong down the line, who is at fault? This is probably something the FDA is going to have to grapple with as the technology becomes more widespread but as of earlier this year it feels like it is still exploratory (https://www.theexpertinstitute.com/3d-printed-medical-devices-where-does-product-liability-law-go-from-here/).
Love the exploration of both 3D printing and crowdsourcing in exploring this topic. To the point of commercialization, the speed of technology adoption continues to increase as we move forward. It will be interesting to see which industry drives the adoption of this technology. Will it be the food industry or will it be another industry with a more immediate commercial opportunity that is able to drive the costs down to bring this into the hands of other industries. In the near term, there’s a concern whether this is more of a marketing/branding move by the company than it is a real viable growth opportunity.
Thanks for the great content. I think it’s incredibly important for technology to help society fight issues of fake news and the influence it has today. On the flip side, I’m worried about who controls the “training” of the machines in identifying fake news. Facebook, for example, has come under fire for what challengers have called a liberal bias (https://www.nytimes.com/2018/08/28/technology/inside-facebook-employees-political-bias.html), where they claim employees police opinions that are not aligned with their own. If they are also in control of the machine learning algorithm themselves, it could give nay-sayers further reason to doubt the legitimacy of news organizations.
I think the challenge in the credit score space is that the end consumer is not the individual’s being rate but the business/banks/3rd parties who want to assess the credit worthiness of an individuals. To successfully serve that end business, you’d need a huge amount of coverage (e.g. if you can only use machine learning to measure credit scores for a small fraction of the population, then it’s less useful) as well as the integrations with all relevant parties (e.g. right now credit checks are instant because of the systems that have been setup to connect the different parties). Because of the uphill infrastructural challenges, I can see why the credit agencies are slow to move on new technologies like machine learning.
HotelTonight’s competitors advantage, as you point out, is their user experience. I’m less convinced that machine learning in and of itself is their competitive advantage since hotel inventory access is commoditized. Also as HotelTonight becomes less niche by expanding into wider booking windows and a desktop experience, it become less differentiated than the other larger hotel engines. Which begs the question, what sets it apart. Customer loyalty feels hard to achieve in a price sensitive market especially since the Brand isn’t connected with the end hotel chain itself that a user may feel loyal to.