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On November 15, 2018, Mutian commented on YouTube | Machines Cleaning Up Human Content :

Thanks for sharing this interesting information! I think it’s really cool that Youtube is using ML for this important issue. It can be very difficult and expensive for humans to review every video that’s posted on the site, but leveraging ML and AI to make the review process more efficient seems to make sense. Several of our classmates have voiced a concern about censorship, but I personally I think making a Type 2 error is more costly than making a Type 1 error. Type 1 error is when you incorrectly reject a video which shouldn’t have been rejected, whereas Type 2 error is allowing a video to be on the site which shouldn’t be allowed. Because of the polarized political environment and the availability of firearms in the US, I think allowing a video that condones hate speech or violence could have real world consequences (e.g., Pizzagate conspiracy)

On November 15, 2018, Mutian commented on Adding Value In Nike’s Production Line :

Hi Kyle, thanks for sharing this interesting information! In addition to the cost advantage of 3D printing for Nike, I thought it might also have the added benefit of speeding up innovation. In some other industries I’m familiar with such as lighting profile, it can take several weeks or months for a molding or die shop to create the necessary tooling for a new product. Depending on the situation, there may be a backlog, and you will have to wait in line in order to have the supplier create the tool you need. using additive manufacturing, Nike can accelerate the testing and manufacturing process for a new shoe without having any tooling delays

Thanks for sharing this interesting information! This is a really interesting use of machine learning, and I can see how it can be a competitive advantage for Progressive. While other auto insurance companies may not be able to tell a good driver from a bad driver – and therefore has to charge everyone an average rate – Progressive can actually change its pricing strategy to attract the good drivers because of its Snapshot data. Assuming consumers learn about this feature, good drivers will be incented to go with Progressive because they can afford to offer a lower price for insurance to those consumers

My concern would be the application of this technology in the health insurance space. Imagine a world where your health insurance company can access your fitbit or iphone health data to understand your eating and exercise habits, and adjusts your insurance premium based on that information. While most people can get over the fact that Progressive has tons of data about your driving habits, I think people would be more hesitant to accept health insurance companies having similar information about them

On November 15, 2018, Mutian commented on Bioprinting – The Future of the Healthcare Industry :

Hi Michael, thanks for sharing this interesting information! Bio-printing certainly seems like a very interesting technology, and it sounds like it will save millions in R&D cost, accelerate drug development, and reduce the need to test drugs on human subjects. It sounds like the technology still has a long way to go until it can produce full, functioning organs. This could really be a great benefit to the world by increasing the availability of organ transplants and prevent unnecessary deaths from a lack of available organs. However, I think it’s possible to take this technology too far in the future, once we have the ability to print entire organs and organ systems, could you print human beings or do things like brain transplants. There are a number of ethical questions, which we will have to work together as a society to create the frameworks for dealing with

Hi Joaquin, thanks for sharing this interesting information! It’s very cool what Local Motors is doing to crowd-source innovation. I think one component of what allows this model to work is the fact that they are giving royalties to people / teams that come up with winning designs, which engenders a sense of ownership in the innovation process and creates a financial incentive for people to undertake this difficult task. However, this is only possible because the company’s products are meant to be small-scale, low volume products. If a company like Toyota were to employ the same approach, those designs might be used in a very large number of vehicles. In that case, it may be more expensive to pay a healthy royalty than to just employ teams of engineers (who make fixed salaries) to generate designs in-house

In terms of your 2nd question about allowing participants to sell directly to consumers, I think there are a couple of barriers. One is the fact that many of the participants may not have the capital to invest in manufacturing facilities, which tend to be expensive. Moreover, I think you run the risk of having products being sold to consumers that don’t pass stringent quality and safety standards. These are not your typical consumer product – they are large machines that need to operate on the roads, with potential to cause accidents and severe injury if they’re not safe. As a result, I think it may actually be better for Local Motors to manufacture the products themselves

On November 15, 2018, Mutian commented on Open Innovation Driving Growth at Alibaba :

Hi Emma thanks for sharing this interesting information. A lot of times in the US, we think about open innovation as the equivalent of crowd-sourcing, but I found it interesting that Alibaba also leveraged their employees for some of these innovations and ideas. At McKinsey, there was also an annual competition called “Practice Olympics” where teams from around the world got together to pitch a new consulting offering or technology to the firm’s partners. Anybody from any level of the organization can bring forth a new idea, and many of those ideas eventually become actual client-facing product offerings.

As Alibaba turns its sights to the US and other markets around the world, it may have to rely more on crowd-sourcing directly from consumers, especially in markets where it does not have a strong operational presence. Because its employees are Chinese, they can leverage that expertise to understand Chinese consumers. But when they enter another market like the US where their Chinese employees do not understand the consumer as well, they can start leveraging crowd-sourcing.