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On November 15, 2018, RC commented on Printing: Speed :

Fashion is one of the industries that would be most quickly disrupted by 3D printing as it is most easily implementable and the industry already is driven primarily by design. The bottleneck I see would be the development of printable material incorporating the technologies that Adidas is trying to realize, and whether the printing process would change the material given the heating/cooling treatment of it — it would be interesting to know what limitations exist currently in what can be printed and not. I think it is in Adidas’ interest to expand the use of 3D printing, in the form of customizing shoes regardless of need; the increased use of 3D printing would contribute to reducing the cost of producing the technology and encourage its widespread use to meet more complex needs.

I enjoyed reading about technology enabling the democratization of the financial system, enabling more widespread and efficient use of capital. I would say that open innovation is a great way to customize fintech to each municipality and project; furthermore, Neighborly institutionalizes peer to peer lending by using financial instruments in the form of municipal bonds. Can the generation of new ideas and capital matching happen organically, or does there need to be a middleman to manage the process and formalize investments? Does machine learning need to be incorporated to optimize the process of open innovation?

Interesting article juxtaposing an open innovation method we would expect in mostly software development in a tangible product business as Lego. I have been noticing more and more complex Lego products in the rare case I see them in a store. It’s a great way to involve consumers into product development; I can see a parallel in the way Snapchat allows users to design filters for the location they are in. Can open innovation go further than creating new toy designs and into new materials as well? Plastic building blocks are Lego’s trademark, but there may be a way to reinterpret this in an idea generation method as radical and divergent as open innovation.

On November 15, 2018, RC commented on Waymo: The future of (not) driving :

I am personally very excited to see autonomous cars on the road because I don’t know how to drive! As we have seen with the accident in Arizona that you mentioned, scrutiny on the safety rate of autonomous driving will be even more severe despite the well-known dangers of human driving; it’s difficult to place the blame on a human over a machine, even if it is the human that violated traffic rules. Is the only way to convince regulators of its safety continued testing?

I am surprised to see the progress that autonomous driving is seeing on the regulatory front, being allowed recently to drive without a monitoring human passenger — we may see widespread use of this technology sooner than we think, especially as more people are using ride-hailing services. I agree with you that a robust regulatory infrastructure must be established before this happens, though, with accountability determined for all possible outcomes.

On November 15, 2018, RC commented on Machine Learning and AI Impacts on the Financial Markets :

Quantitative hedge funds have been one of the fastest growing strategies in recent times; 56% of hedge fund respondents to a BarclayHedge poll say they use artificial intelligence or machine learning in their investment process [2]. I agree with you that they add volatility to markets, and despite their ability to process more data rapidly they are not always right. In March 2018, the AI Index tracking performance of quant funds fell 7.3%, vs a 2.4% drop in the overall Hedge Fund Research Index aggregating all strategies [1]. Established hedge fund funds are increasingly looking to hire investment professionals with quantitative PhD backgrounds, but enforcing more stringent risk limits that limit portfolio managers’ ability to generate alpha, resulting in these portfolio managers being fired and hired within a pool of quant funds.

[1] Burger, Dani. 2018. “Bloomberg – Are You A Robot?”. Bloomberg.Com. https://www.bloomberg.com/news/articles/2018-03-12/robot-takeover-stalls-in-worst-slump-for-ai-funds-on-record.
[2] Whyte, Amy. 2018. “More Hedge Funds Using AI, Machine Learning”. Institutional Investor. https://www.institutionalinvestor.com/article/b194hm1kjbvd37/More-Hedge-Funds-Using-AI-Machine-Learning.

On November 14, 2018, RC commented on Man or Machine? Does AI have a place in Venture Capital? :

You raise an interesting point about the need to disrupt an industry that supports disruptive innovations. AI could truly reshape the way in which VCs source and evaluate deals, and could facilitate the allocation of capital into more startups. Could training data for this AI be gathered in an open source format, given the collaborative nature of VC investing? You suggest gathering consumer survey information to predict the success of a product idea; I wonder how accurate their feedback may be, as we often see customers not actually purchasing products they envision that they would be willing to. Given that technologies have been developed to evaluate qualities of job candidates who are interviewing virtually, perhaps data on more qualitative aspects of founder success could be used as well.

On November 14, 2018, RC commented on Organovo: bioprinting tissue to speed up drug development :

It is incredible that 3D printing has advanced to the level of printing layers of living cells. Along with improvements to the drug development method as you mentioned, the social implications of printing organs for testing are expansive—such as eliminating the need to test products on animals. It would be interesting to know the cost of printing these organs currently, and the what timeline exists to reduce production costs to a level where the printing method could be more widely used. Another question to consider is whether there are any logistical or ethical issues around sourcing cells that would be turned into printing material? It would also be interesting to see what measures Organovo is taking to collaborate with other startups that are developing this technology.

It is interesting to see the 30% usage of Spotify’s machine-generated playlists; this is indicative of machine learning’s potential to significantly impact the music streaming industry’s profitability. I would be interested to see if there are any layers in the way Spotify’s machine learning technology understands the music taste of its user, especially as streaming has exposed consumers to an increasing variety of genres and music tastes across the world have generally started to converge. Spotify could more accurately recommend playlists for users if it could also account for other external factors, like the user’s time of day and location, that could affect what music the user chooses. Could it also integrate its service with technologies such as Fitbit, which track physical data that could signal the user’s mood? Adding a feedback feature to the songs or playlists suggested, even as simple as a thumbs up or down, could address your point about asking the user for direct input in the music he/she prefers.