I would be interested in more information on the cost and speed of additive manufacturing. At first glance, construction seems like an excellent application of additive manufacturing and this technology should be scaled around the world. In addition to the social benefits that @Nathaniel described above, this technology could radically transform cities by potentially reducing the amount of time and money spent on construction projects. If we could apply this to housing and transportation projects, we could more rapidly address the homelessness and mobility challenges facing our cities.
In response to the questions you raised, I agree that this technology poses the risk of ultimately reducing the number of construction jobs available. However, in the short term, I believe that this technology can help grow the overall construction industry, due to the quantity of construction work currently needed around the world.
I think applying Open Innovation is a fantastic idea and would love to see more governments adopt this approach. In my perspective, there are a few clear benefits. First, it increases community buy-in and engagement with ongoing city projects. I believe the more engaged people are with city politics, the more likely they are to support government funding initiatives — which will thereby increase the number of services the government is able to offer and potentially create a stronger sense of community. Second, OI challenges effectively force governments to engage directly with the opinions of the community, which ensures a broader diversity of opinions are considered.
In contrast to some of my section mates’ above, I do worry, however, about incorporating community feedback too extensively in the project planning process. While I agree that governments should be as transparent as possible about city plans, an influx of community opinions could lead to slow progress on a given project. It can be very challenging — nigh impossible — to get a community to agree on specific plans or initiatives, and I fear that too much community involvement could be counterproductive and lead to disagreements.
I had never thought about the challenges of manufacturing in space due to the lack of gravity, but the applications of this technology are clearly far-reaching. To draw on an example from class, as we saw with the beer game simulation, long lead times in a supply chain process can have amplified effects throughout the entire supply chain. If one considers how long it would take to deliver materials to space, the benefits of this technology seem self-evident. The ability to 3D print exploration or even surgical tools on-demand, in space, seems like it would enable teams to move more quickly, and could potentially reduce the possibility that teams are starved for specific resources.
I am, however, skeptical of one of the benefits of the technology you described. Considering how costly space exploration is, I’m doubtful of the need for spontaneous space exploration. While I could envision scenarios in which a mission chooses to remain in a given location somewhat spontaneously, it seems unlikely that a space team would spontaneously decide to launch an expedition given the safety risks.
Open innovation contests are a great way to ensure that creative and unique viewpoints are given appropriate consideration during the product development process. With regards to the Hyperloop One, specifically, the OI contest provided cities and universities with an opportunity to pitch routes in their area, which likely had the effect of increasing visibility of cities that otherwise may not have been considered. Additionally, by outsourcing much of the legwork to external parties, the Hyperloop One team is able to deploy members of the community as lobbyists on the project’s behalf, giving them the flexibility to hit the ground running if and when they decide to expand in any given market. This approach also has the potential of reducing the impact of the Hyperloop team’s biases (geographic or otherwise) by forcing the team to consider perspectives they have otherwise overlooked.
This is a fascinating application of machine learning in the food industry that has potentially applications for the delivery industry more broadly. If companies are able to better predict customer demand before they place an order, they could start the order fulfillment and shipping process when demand is low. This would allow the company to prepare orders evenly throughout the duration of the day, potentially reducing staffing needs. There’s also a possibility that anticipating consumer demand could allow companies to dispatch delivery vehicles during non-peak hours, thereby reducing the number of delivery vehicles on the road during peak rush hour. This would not only reduce delivery costs for the company, but would also reduce overall congestion in a city (which is often in large part caused by delivery vehicles). If companies are able to lower the costs associated with delivery, they can then lower the retail prices of the goods, thereby making it easier for people who live outside cities to obtain the items they need even if they do not have access to a car.
I imagine it would be very challenging to use artificial intelligence and machine learning to detect when content qualifies as fake news. To effectively identify fake news, Facebook’s algorithm would need to determine the veracity of claims posted on Facebook — and then make a judgment call whether the inaccuracy warranted deleting the post. This would be especially challenging during discussions in which the facts are not mutually agreed upon, and I fear a policy that calls for the elimination of “false information” would lead to stifled conversation.
I, do, however think it would be possible for Facebook to regulate the creation of fake accounts using machine learning. Robust machine learning algorithms could verify the identity of each individual creating a Facebook account which would eliminate the likelihood that one individual is creating multiple Facebook accounts for the explicit purpose of widely spreading false information.
What strikes me as interesting about Glossier is that they are predominantly an online retailer — they opened their first retail outlet just last weekend. This seems relatively uncommon in the beauty business, and I imagine poses certain challenges as its customers attempt to determine if Glossier’s products are right for them. This suggests to me that Glossier could really leverage machine learning to better understand the preferences of their customers, and develop more personalized recommendations.
I think there are several ways Glossier could approach this. First, they could use natural language processing to parse the blog comments to reduce the time it takes to sift through all the feedback that customers share. Second, they could develop online quizzes for new customers — similar to StitchFix — to get a sense for the person’s aesthetic, preferences, or skin type before making recommendations for products to sample or purchase. This would allow Glossier to remain true to its value proposition of providing personalized recommendations as they grow and scale.
One reason I find AirBnB’s algorithm to be so problematic is that it is surfacing different information to users on the basis of guest rating — thereby making it more challenging for individuals with low ratings to find accommodations that fulfill their criteria. Given that guest rating could be influenced by a multitude of factors — including any implicit or explicitly held host biases — it at first glance seems irresponsible to incorporate guest ratings into the algorithm. Uber, for example, doesn’t surface a rider’s name or guest rating to a driver until after the driver accepts the trip (and never displays a photograph), consequently reducing the likelihood that the driver will reject the rider on the basis of gender or race. While I recognize that AirBnB believes its host are entitled to additional precautions because they are letting strangers into their homes, I also believe that certain standards need to be in place to reduce the likelihood of discrimination.
While I do think it is possible to leverage AI to reduce or limit discriminatory behavior in the shared economy, I think technology companies will need to approach this issue very thoughtfully. They will need to creative find ways to reduce any inherent bias in each of the inputs to the algorithm. I think there are a couple of ways AirBnb could approach this. They could either remove any inputs that have a subjective component, or, more controversially, they could explicitly ask guests their race, and then calibrate the host’s ratings based on any detected discriminatory past behavior. This wouldn’t address the issues AirBnb has observed in the past with hosts rejecting guests on the basis of race, but it could help reduce the influence of racism on the search algorithm.
This research project poses an interesting question about the role of companies in establishing a safety net for independent contractors. Companies operating in the gig economy are in a regulatory grey area — it’s unclear whether independent contractors working 40+ hours per week are entitled to certain protections like healthcare benefits. Ultimately, I think it is the role of the government to determine whether companies are responsible for providing these protections. Currently, workers are classified as employees (as compared to independent contractors) if their work meets conditions (i.e. requires training, involves scheduled shifts, uniforms, etc.). As a society, we need to determine if we are satisfied with that classification and seek legal recourse if we are not. However, in the absence of any clear regulations, the onus is on individual companies to determine what types of protections they want to establish for their workers. As the gig economy expands, people will have more options for flexible. If TaskRabbit wishes to attract the best talent, I believe it is in their best interest to provide certain benefits for their workers to ensure retention over time.
It’s fascinating to hear how CityMapper is leveraging demand data to more efficiently transport people across London. I’m optimistic that solutions like this will reduce traffic congestion and minimize the number of empty seats in vehicles. I would be curious to learn more about how the cost of CityMapper’s bus service influences the demographics of the riders. Given that CityMapper’s service requires smartphone technology, I suspect that the demographics of the service skew younger and likely middle- to high-income. I therefore echo your concluding concerns. My fear is that privatizing public transportation will disproportionately impact individuals who live in underserved areas with fewer alternative transportation options. Given that public transportation is designed to be accessible to everyone, it’s important that we are not using historic demand as justification for discontinuing public transportation service to parts of cities where fewer users of CityMapper’s service live.