As someone who is constantly trying to plan my next vacation, the idea of simplifying the planning process is incredibly attractive. I typically search for and book flights through an online flight search or online travel booking website – rarely do I actually go to an airline’s website to book a flight. I’m curious how this process of booking influence the volume and type of data that JetBlue has access to and uses in its machine learning algorithms. I know you mentioned JetBlue used a dataset of 27K situations, is that enough? Is it representative of customer booking scenarios?
I’m also particularly interested in your point about personalized pricing. I’m always searching for the best deal, and I’ve heard that the prices shown to me differ on a number of factors (location, computer type, how many times I’ve searched). Is that considered a type of machine learning that the airlines are already using? I also know that airlines segment their passengers and prices on a number of similar factors. Can machine learning be used to help make that process more efficient and accurate for airlines?
This is a really interesting topic that everyone in the industry – from providers to payers to policymakers – is trying to address. There’s so much data in the healthcare system that it only makes sense to run some analysis on it and gain insights from aggregated data that could help to inform an individual patient’s treatment plan. Given the current organization of the industry, I see several challenges that may arise when trying to analyze patient data from within a hospital or provider system. For example, how do we ensure that data is actually being shared across systems in a way that gives the system a full picture of the patient’s health? Right now the data is incredibly siloed, and patients seem to be the sole owners of their full health history. Furthermore, how might regulations (HIPAA, 42 CFR around mental health data) influence access to data and what can be done with the data? As we look toward the future, I’m also curious how data generated outside of the healthcare system (from social media or wearables) could improve the value of machine learning in healthcare.
The benefits of additive manufacturing in the construction industry are clear. In addition to addressing the labor gap, rising material costs, and pressure to create sustainable infrastructure, 3D printing could also eliminate raw material waste in the industry.
As you mentioned, customer perception of quality may be a big hindrance to the increased adoption of 3D printing in this space. When I first read that this process was used to create the bridge, my first thought was “is it safe?!” This is an area where I think having some early and visible successes can be instrumental in increasing the use of 3D printing in construction as well as user comfort with the practice. However, the opposite is true as well; highly publicized failures could prevent the use of 3D printing in the construction industry. Additionally, while the bridge does not have the most aesthetically pleasing design, that could contribute to its popularity and make it a piece of art and a conversation starter.
This is a really fascinating topic, and honestly, using 3D printing for food makes a lot of sense. While this process may be slower and more expensive than traditional methods right now, I can imagine a future where 3D printing of food is more efficient and widespread. There are several use cases that immediately come to mind, but few of them relate to addressing food shortages or nutritional issues and restrictions. For example, I think there is a huge opportunity to use additive manufacturing to reduce and eliminate waste in the mass production of food. I also see additive manufacturing becoming popular in higher-end restaurants where the creation and consumption of food have become more of an art than a functional necessity.
To agree with Jimmy Dimon, I appreciate your somewhat skeptical approach to Sidewalk Labs’ use of crowdsourcing. In many ways, it seems like a glorified town hall meeting. The Sidewalk Labs approach may have the cool bells and whistles of technology and design, but how do we make sure that the crowdsourced ideas are being implemented? How do we make sure that the voices that are contributing ideas represent the city and its culture? How do we balance what the people want with government’s plans for development? I also think we have a tendency to see some mix of technology, data, and analytics as the solution to many social issues, but there is an intangible aspect to a city and its culture that can’t be captured, replicated, or created using these tools, similar to what Ricky Burdett said.
What I do like about this scenario, is that Toronto is piloting this idea with a small plot of land, instead of going all out. Urban planning, real estate, and smart cities can be expensive. It is important to get a sense of what works well, what doesn’t, and why before dedicating expensive resources to a project like this.
LittleBits seems like a great product/platform and is something that I wish was around when I was a kid. What’s interesting to me is that in addition to crowdsourcing design ideas through the online community, LittleBits also crowdsourced idea selection by allowing users to vote on design kits that they found useful and would want to purchase.
In response to your first question, I don’t think that open innovation and open imagination have to be at odds, especially with LittleBits. It appears that a lot of the value of LittleBits comes from lowering the barriers to entry into STEM and making STEM attractive and accessible to a broader population. While this may allow for copycats in the beginning, it could also ignite some sparks that lead to true innovation and advancement over time. I’m excited to see where LittleBits goes in the future, and I’m curious if the company has plans to reinstate bitLab as it grows.