I wonder if the idea of percentage “influence” could be applied to an academic or research setting? Open innovation for product development seems like a perfect fit for a university technology licensing office setting, where younger researchers could get their hands on a promising product and take some “influence” (read: financial stake) in the licensing process. Focusing on an academic setting could also help weed out unrealistic ideas, reducing overhead for Quirky.
I wonder if Navient could also use the open innovation platform to beta test new products on volunteer customers? This could allow them to learn more about product advantages and disadvantages without releasing them to the full market. The only potential drawback to the open innovation platform is that it could concentrate feedback from the most passionate users, potentially diluting more “normal” users.
In general, glasses frames can be sold at a massive markup, so I’m not at all concerned with the business model. The limited selection of 3D printable materials is more worrisome to me. Customers need glasses frames to be durable, flexible, comfortable, and indistinguishable from regular glasses. We saw from the failed launch of Google Glass that consumers don’t want to wear anything they perceive as “weird” on their face. Unless Safilo can produce 3D printed glasses that function just like regular glasses, I think they’ll have a hard time escaping niche status.
I wonder if this could be integrated into Stitch Fix or Rent the Runway, two tech-forward fashion companies with highly personalized approaches to the customer? Being able to print custom modifications to pieces before shipping them to customers could allow companies like these to be more flexible and carry less inventory. Furthermore, the ability to customize pieces would be a value-add for variety-sensitive customers who don’t want to have the same items as someone else.
I have a lot of questions about the risks to individual stores. How good are these stores at detecting and preventing theft? Does Amazon suffer any losses from shoplifting? During a power outage, is there any way to prevent looting? What about if the wifi in the store goes out? For Amazon, risks like these are probably immaterial in the grand scheme. But from the perspective of expanding this technology, these concerns would be top-of-mind for grocery chains and small businesses.
I’m not sure I agree that the convergence of product preferences is detrimental to Alibaba. Doesn’t it let them funnel customers to the products that make it the most money? And even if it is detrimental, rather than abandon the personalization, can’t they simply tweak it to incorporate more variability? I agree that Alibaba has a huge amount of influence over what consumers purchase, but perhaps that’s not such a bad thing.
I wonder if PayPal could use Simility to not only identify fraudulent payments, but also criminal networks? I would guess that most fraudulent transactions aren’t merely one-offs, but part of a larger network of unsavory activity. By identifying the payers and recipients of fraudulent payments, could we learn how criminal enterprises launder money through the financial system?
One of the most expensive parts of machine learning is compute power. The bigger the dataset and the more complex the algorithm, the more compute power it requires to run. Companies like Optum usually outsource this processing to cloud computing companies like Amazon (AWS) or Google (Google Cloud). I wonder if Optum should try to bring some of that processing into the company, especially if Amazon is making a play into the same space? Internal compute capabilities could also assuage data privacy concerns.
While I can definitely see the utility of this data, I also have some data privacy concerns. Could this data be subpoenaed for use in court against a driver? Would a driver be able to opt out of data collection, or would it become a requirement? From a regulatory and privacy perspective, I would want to know more about how the data would be shared between insurers, how it would be stored over time, and who would have access to it.
One concern I have with the use of ML to identify candidate drugs is that it risks emphasizing incremental progress over revolutionary new treatments. AI algorithms are excellent at predicting future outcomes based on past results. Because of this, I would guess that most of the drugs recommended by an algorithm would be inspired by drugs that have worked before. Would that reduce the likelihood of unexpected and serendipitous breakthroughs in medicine, where we discover a drug unlike any other we’ve had before?