Mollie – this is a great overview of machine learning and a very exciting application to a very suitable field (cyber security)! You did a great job of highlighting how pervasive cyber security threats have become – basically anyone connected to the Internet (either within or outside of any organization) can pose a threat to an organization’s network. Also, as you mentioned, I completely agree that human monitoring of potential threats is impractical given the explosive growth and complexity of recent threats. I personally think that the edge for these machine learning players is scale – you need the most amount of data to detect the most patterns and that ultimately will drive a virtuous cycle / self-reinforcing network effect (better algorithms will attract more customers that bring in more data and further improves the algorithms). Getting that flywheel into motion is the tough part, but it seems like CloudFlare has scaled very nicely. (There are recent rumors of a potential IPO that would value the company at $3.5Bn+.) Will be interested to see how the company continues to grow and benefits from scale advantages!
Great job at outlining a very novel way that a quasi-VC firm is approaching medical research. I strongly agree that crowdsourcing ideas via an open innovation model can potentially lead to a broader distribution of ideas and have a higher chance of a breakthrough solution being found. One question I have, though, is whether this approach could undermine the traditional longitudinal / cumulative nature of scientific research. There’s a rich history of scientific breakthroughs emerging from learning from the discoveries of others (i.e., standing on the shoulders of giants). Do you think that the DDF should loosen its role as a repository for ideas and have all ideas be publicly accessible? I think there’s a major advantage of cumulative learning and being able to see and react to other people’s ideas could lead to even more innovative solutions than if participants in this open innovation competition were to just submit their ideas in a vacuum / isolated manner. I suppose that one fundamental challenge of opening up all submitted ideas to the public could undermine the financial reward structure where some participants might feel gypped if someone else incrementally improves on the idea they’ve already submitted. I wonder if there could be an alternative reward system to keep people motivated to submit their ideas if all of these ideas were to be accessible to the public…
Great post! My main question / reaction after reading this piece is how GE Aviation should consider the risk of cybersecurity breaches in its deployment of 3D printing across its organization. It seems like a 3rd party hack could severely jeopardize the safety of its production processes (and national safety) as the designs and structural details / potential weaknesses of its aircraft and its components become exposed. Assuming that appropriate encryption and cybersecurity protection measures are in place, however, I agree with your assessment that 3D printing can really revolutionalize the manufacturing process within the aviation industry, especially when it comes to replicating complicated machinery and detailed prototypes. Very insightful analysis!
Viria – very thoughtful piece on open innovation! The OpenIDEO challenges remind me a lot of hackathons and how many tech firms use these types of events to both crowd-source solutions to unique business challenges and scout talent. Does IDEO also wind up hiring or rewarding contributors of the best ideas in some way? Do you think that there could be some benefit of creating financial incentives / prizes for the top ideas? I suppose one concern / problem that emerges from incentivizing more people to submit more ideas is that it takes more IDEO staff members to sift through these ideas. I wonder what the right balance / trade-off is between incentivizing the public to submit as many ideas as possible and the human cost and resources involved in reviewing / evaluating all of these ideas. The selection of the “best” idea is also likely very subjective, so I suppose that introducing financial incentives might be difficult / contentious here. In any event, very interesting read!
Jordan – thanks for a great read; very informative piece. Out of curiosity, have you come across Gartner’s latest hype cycle for 3D printing (https://www.sculpteo.com/blog/2017/08/01/the-3d-printing-hype-cycle-by-gartner-what-does-the-2017-edition-say/)? It seems like a lot of the medical applications of 3D printing are on the rise in the hype cycle (or at their peak, as for medical devices). Overall, I strongly agree with your assessment that achieving widespread buy-in from doctors / key opinion leaders in orthopedic medicine will be one of the biggest barriers / catalysts to mass adoption. The challenge I see is that there’s a circular / chicken-and-egg type of issue at play here – medical leaders aren’t willing to test this new 3D printing method out until it’s proven, but this method can’t really be proven out on a large scale without more buy-in from more medical leaders. Do you think it’s possible to start by addressing certain less invasive / less critical procedures with 3D printed implants to get more doctors comfortable with the process and results before trying to tackle more intense procedures like hip replacements?
Casilda – this is a very well-reasoned and thoroughly researched paper. Well done on understanding the nuances of a very rich and complex payment ecosystem, where (as you point out) many collaborators (e.g., merchant acquirers, issuing banks, card networks, unbundled gateways, etc.) all compete for a sliver of the typically meager 2-3% transaction fees. You’re absolutely right in that addressing fraud through machine learning is the near-term key to success for PayPal in handling online fraud. Rules-based engines just become too unwieldy and are not as flexible as machine learning algorithms. PayPal’s acquisition of Simility also points out a unique approach that many corporations take in first taking on small minority investments in high-potential start-ups (often with potential strategic benefits) to have a seat at the table when these companies grow and are ripe for acquisition or future rounds of funding. (In Dec 2017, PayPal had participated in Simility’s $17.5M round: https://venturebeat.com/2017/12/12/fraud-detection-startup-simility-raises-17-5-million-from-accel-paypal-others/). I also agree with your and Simility’s assessment that human judgment will (and should) continue to play a role in fraud management near-term. A well-structured fraud prevention stack should consist of multiple tools to help fend off various forms of fraud. Well done!
This is a very informative article. I really appreciate how you walked through the specific strategies that Proven is pursuing to obtain robust customer data (e.g., starting at an accessible price point, having bi-monthly check-ins with customers, etc.). One key question I have is related to how Proven can effectively close its data feedback loop. The algorithms that Proven runs will only be as good as it’s able to determine the good outcomes from the bad. As another user, JC, points out, there doesn’t seem to be an explicit mechanism for capturing the bad (“failure”) outcomes, as customers with bad experiences may just stop engaging in the regular check-ins (and these bad outcome users could be conflated with users who might have had good outcomes but just chose not to follow-up). Without requiring (or incentivizing) customers to report back their results (good or bad), I’d imagine this feedback loop will be hard to close. Also, this company will really benefit from having longitudinal (time-series) data for its customers, but given its start-up status, I think there will be a meaningful runway ahead in terms of data collection efforts. I’ll be interested to see how this company manages these issues over time!