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On November 15, 2018, momo commented on Open Innovation at the Bank of Canada :

It’s interesting to compare and contrast this with the open innovation program that I wrote about (US DoD). In both cases, the organization is a steward of some hugely important component of the national interest, and with limited resources recognizes the need to welcome external innovation. Organizationally, I agree with your idea that they should better define the scope of the solutions they are looking for; the DoD states very specific scope in terms of the cybersecurity problems/solutions it wants outsiders to tackle, mostly driven by the sensitive nature of the data at hand. It seems the Bank of Canada has more room to play here, but should also be motivated to define scope more specifically if only to make sure people aren’t interested in things they don’t want the public (especially the non-Canadian public) to be investigating too closely.

Given that this program is very much looking for in-bound ideas, and the outbound/exchange aspect is a little less clear, I think they’ll also need to very actively demonstrate to the public that the selected inbound ideas are being taken seriously and being acted upon in a timely matter (could be a challenge for a bureaucratic central bank). This is especially important given the lack of compensation/incentives currently tied to idea submission. I actually think that public recognition of successful initiatives by the BoC could be enough incentive for people who are motivated by contributing to the public good to continue providing an inflow of ideas, but to your point about reputation risk, they’ll need to have a more rigorous vetting and evaluation process in order to use this as an effective incentive.

To your question about how the company should think about licensing/leveraging/donating the technology to serve those in need, I think they’d have to monetize (instead of donate) given the large upfront investment this requires, and would envision a contracted licensing agreement with governments. I question though the feasibility of achieving profitability by going this route, given the limited budgets of municipal governments as well as the rate of adoption that they might face in working with governments. I do think there’s a broader affordable housing need that could be tackled, even if it’s more upmarket (and therefore serves less acute housing need) than the applications related to homelessness and natural disasters. To your second question, I think Contour Crafting will run into enough roadblocks and challenges as it is driving innovation for housing on Earth, and should focus almost entirely on that in the next 10 years; the iterative learning they will go through building processes that they can disseminate across the world will surely inform their approach more generally.

On November 15, 2018, momo commented on Hinge: A Data Driven Matchmaker :

I actually wonder whether Hinge might go as far as to scan the content of the “personal interactions” that do happen over the app (e.g., keywords in messages, speech and reply patterns, etc.) to determine compatibility as another input into the machine learning. This of course would imply that they would want to encourage more connections in-app between people, and it’s debatable whether the best way to achieve this is to encourage their users to provide more data, or to get more users overall. Re: your point about this potentially further feeding people’s biases, I think they could leverage the huge amounts of data they have about people’s liking behavior on Hinge and present that in a clean, summarized way that would make people more aware of their preferences in aggregate and in turn think more deeply about their biases. Definitely a sensitive subject to broach, but could be something that users find valuable and informative.

On November 14, 2018, momo commented on NYC BigApps: Crowdsourcing Civic Innovation :

This is a great example of the public sector embracing a form of innovation that has the potential to help underserved communities. The private sector is often not incentivized to solve these problems and the public sector often doesn’t have the resources to solve these problems, so it’s refreshing to see a city government recognize the power of crowdsourcing talent. To address the problem that you raise about furthering inequality, the city could take the open innovation one step further and have the public vote on high-potential app ideas. It could also back specific projects that are most consistent with the priorities that the city most wants to promote and signal to the public the types of projects that are especially welcome.

On November 14, 2018, momo commented on Will you marry me (if I ask with a 3D-printed ring)? :

Very interesting application of 3D printing. I agree with your assessment that the greatest potential exists in the middle of the market, though I think it could go two ways. The iterative possibilities of designing custom jewelry might open up the higher end of the middle of the market given the higher level of “service.” Alternatively, if jewelry makers seek a value advantage, perhaps they will leverage the technology and pass on the cost savings to the consumer and make upper-middle market jewelry more accessible at greater scale.

On November 14, 2018, momo commented on The Future of Venture Capital: Humans vs. Machines :

I’d definitely be concerned about the problem you raise about whether a “machine learning investor” would be able to identify a successful investment that bears huge risk, without regressing to the mean. I also suspect that a large part of the venture investing decision is based on the quality of the team, which would be represented by qualitative factors that aren’t necessarily perceptible in an algorithm. Machine learning could identify patterns (e.g., Stanford computer science major = good), but it would be difficult to act on inputs such as someone’s trustworthiness, value system, etc. Regardless, this is a great case for the combined power of human and machine, and there definitely seems to be a place for the machine to outperform humans in making purely data-driven decisions without bias in the context of an investment decision.