Akash Section H
Great choice of subject matter, Matt! “Open innovation in government security” sounds like a great title to a dystopian novel, but if done correctly, the program could potentially be quite helpful. A few thoughts on your questions:
1. My primary concern with the incentives is that citizens are most likely to report their neighbors out of fear, which could lead to politicians ushering in the next wave of McCarthyism. By the same token, the government might be able to improve screening and reach a point where most of the red flags are legitimate.
2. Another application of crowdsourcing could be infrastructure improvement. Governments would have an easier time addressing public needs if they understood where to fix roads or add a new stoplight.
Terrific read! Bioprinting makes even the most difficult 3D printing techniques look like a cake walk, but you’ve correctly pointed out that they will be invaluable to the pharmaceutical field. A few thoughts on your questions:
1. Adding “bio” in front of any word automatically increases its complexity, and the question of bio-inequality (coining a new term here) is no different. We see many of the same issues with drug companies today, but the right mix of government and the free market can make medicine more affordable for the masses. When printable organs become a reality, I imagine that price will become a hotly contested issue.
2. The possibility of new organs is especially exciting! Ideally, bioprinting companies would be able to remedy congenital defects.
Great work, Ian! Given that I’ve never worked in public markets, I found your article to be particularly helpful. A couple of thoughts on your question:
Seeing that Neighborly works closely with public municipalities, I think open innovation would be highly beneficial. The threat of competition is a powerful deterrent in most cases, but with Neighborly, I would expect competing organizations to cooperate in pursuit of public good. In fact, I’m a bit surprised that Neighborly hasn’t engaged more organizations in their innovation process. Perhaps their investor relations preclude certain partnerships with other organizations.
Awesome work! I’ve often thought about the possibility of robots beating humans at creating music. Even though it’s tempting to claim that humans will always understand other humans better, the evolution of music recommendation engines suggests otherwise.
1. Machine learning has certainly curated some spot-on playlists, but as with most trends, people will probably tire of the perfect playlist and want more variety. On the other hand, DJ’s may want to pick up an instrument or two, given that algorithms are beating them at their own game.
2. Most companies seem to avoid the issue of data privacy through anonymity, but one might find that musical tastes are highly correlated with geography, culture, and other unique identifiers. Spotify will probably find itself defending its data practices periodically as they scale.
Awesome topic, Dan! My capstone project in undergrad involved 3D printing for petrochemical processes, and I remember thinking about the structural of 3D printed objects. A few thoughts:
1. The 3D printed approach is certainly more precise than the current spray method for applying concrete. My concern is that a 3D printed wall could fail after a few layers start moving horizontally due to uneven stresses, similar to a slipped disc in the human spine. However, I think Winsun could account for non-uniform stress when designing the CAD model and build the layers accordingly.
2. Winsun should stay small for now and tackle residential buildings. After completing several projects and earning the trust of structural engineers and designers, Winsun could consider taking on larger buildings of greater complexity.
Great read, Andrew! I remember building a “wind turbine” out of household materials through an outreach program at work–design is no joke. A couple of thoughts on your questions:
1. Climate change is an interesting intersection of first-principles models and statistics. Even though scientists understand the underlying physics of weather, the outcomes are quite sensitive to initial conditions in models, simulations, etc. Data science is immensely helpful in refining current weather models, but I think the one weakness is the difficulty of including long-term macro trends, such as global warming and El Nino/La Nina. For other projects in the public sector, machine learning could supplement years of similar fundamental research to provide an extra layer of refinement.
2. I’m actually quite confident in the integrity of data from electricity suppliers. Given that electricity consumption is critical for utility pricing, I would expect many stakeholders to be keeping a close eye. If there were any cheating or wrongdoing in the consumption data, investors would be calling up their lawyers within the hour!