I do think customization will be a main feature footwear. As we see in other industries whether its fashion, food, or health personalization is moving forward. This applies to shoes too, especially athletic shoes. Different running gaits, or history of injury play into what shoes you wear (regardless of what the elite athletes wear). I think Nike would be smart to build out their 3D printing models. As leading innovators, they should beat out adidas in this area to maintain their edge.
Sophia, I love this. I hadn’t heard about this yet, and appreciate you discussing the issues. To you question about whether algorithms should need to meet higher standards than drugs to be accepted by the FDA, my hope would be no. In all cases of drugs, doctors make the best advice for the patient and I would hope this would be no different. Some woman are not able to take the pill, or some other form of contraception, and I would like them to have opportunities and benefits from alternatives too.
I enjoyed reading your essay on L’Oreal and the commentary on the make up industry, as I know first hand the difficulty of finding the right products to use. I like thinking about the extension this will have on the health industry as well. I agree that trust will be a very important guide here, as most people will say they don’t just wear one brand of make up but vary make up brands based on product. Thus, will the recommendations given to the consumer be truly a representation of what is best for them, or merely what they want to sell to make the most profit.
Mike, great commentary on First Data. I think it is really important for a company like First Data to keep its data set and metrics secure otherwise “fraudsters” will just copy the behaviors to get around being detected as fraud. For example, in the simple rules-based approach, all a fraudulent purchaser would have to do is follow those steps to get around being detected. Going forward, while machine learning will have a multitude of behaviors to look for, since its comes down to statistics and certainty, all the fraudster would have to do is be really “human” in a few areas to trick the machine learning algorithm.
Romaan, I echo your excitement for a faster TSA process. To respond to your question about “potential forms of Machine Learning manipulation” I would highlight that ID matching can be incredibly difficult to get data on. Every state has a different ID, and they all update their IDs every 5-10 years and at different intervals. The onus is on the state to send the IDs to the DHS– but rarely they do this. So just considering the US, the algorithm has to have data on every valid ID, from every state, and continually be updated and learn the features of new forms of identification. Now, expand that out to the rest of the world! Thus, a major concern is not just having the technology within the DHS, but getting buy-in from other ID officiating authority to provide DHS with a robust enough data set to build upon. This could mean we are farther than we would like from a 30 min arrival at the airport.
In light of Keith’s comments, I do agree that there is a concern over privacy in this space, and I would also add the importance of security. With so much customer information being stored in one place, I’d like to know more about Alibaba’s internal infrastructure to keep it safe. I think Alibaba has the opportunity to set a precedence–perhaps not in company management like we discussed today in class– but in AI ethical practices about how it approaches these problems in the future.
I echo the privacy concerns mentioned above. I would respond to your comment about the 2009 competition and the need for more crowdsourcing of algorithm development. While open source frameworks is a really good tool for innovation, in the case of Netflix, two researchers were able to uniquely identify some of the users within the dataset that Netflix released: https://money.cnn.com/galleries/2010/technology/1012/gallery.5_data_breaches/index.html . Thus, even with their attempts to make the data non-attributable, it is clear that in the space of machine learning, privacy can be harder to reacher than just merely staying within privacy laws.
I agree, what a great read! I think it is important to bring in “amateur” astronomers, and other young scientists, to support building data sets. But I would add that given the other challenge NASA is facing, lack of budget and increased competition, it can be hard to incentive scientists in the future to want to work at NASA when they could do the same work at the competition for higher compensation. This leads to another problem NASA is facing which is attrition of its work force to it the growing space and astronomy private sector.