Justin Mundt's Profile
Really interesting post, Ti. I also lean towards getting closer to the hospital and patient in order to optimize responsiveness to the end customer (in this case, the hospital). Part of my thinking in this regard relates to a recent healthcare development on the pharma side of things, where Intermountain Healthcare and other large delivery networks have teamed up to launch a generic drug manufacturing company. They chose to do so primarily to combat (1) the high prices of pharmaceuticals and (2) to ensure more stable supply. While the medical device industry has not come under fire for its pricing practices like pharma has, this development would drive me to work even more closely with my customers with higher service and quality in order to dissuade them from bringing manufacturing in-house.
In addition to the concern of counterfeits, I wonder if additive manufacturing could also harm the perceived quality of Chanel’s products. Like many couture brands, part of Chanel’s appeal is the high quality of its products and the craftsmanship that goes into each. While quality and craftsmanship are less relevant concerns for mascara and other makeup, Chanel could nonetheless experience a degree of perceived quality erosion by turning to 3D printing in its production process. If such a practice were to extend beyond makeup and into clothes, handbags, and shoes, Chanel would run a significant risk of losing much of the prestige that allows it to innovate in the first place.
ES&S’s refusal to engage in more aggressive product improvement at a time when multiple stakeholders demand creates significant questions around their ability to compete in the market for voting technology in the long run. With such attention fixed on the question of election security following the 2016 presidential election, many are endeavoring to “build a better mousetrap.” As this post appropriately notes, the stringent security requirements for voting technology will slow the adoption of any new technologies. But unless ES&S (1) is covertly working on significant improvements to its technology covertly or (2) plans to change course on its willingness to collaborate with voting technology innovators, it is risking its own long-term viability.
I wonder if the Citywide Analytics team’s current reactive projects are a sort of “initiation tax” that they are required by the city to pay. That is, before city governments (which do not have the reputation as being the most innovative bodies in the world…) embrace a prospective analytics vision, maybe they first need to see the real results that such approaches can deliver in a reactive fashion (such as the health inspections). Hopefully, the passage of time and successful projects will allow the data science team to shift its focus to more prospective challenges. By doing so, the city would ostensibly increase the challenge and engagement for members of the analytics team, potentially providing a needed boost to employee retention.
This is a fascinating post reflecting the ways that increased security risks have across business and consumer settings. Jumio’s platform seems poised to leverage its neural network learning model to develop a robust ability to identify fake IDs, but the question of how Jumio can stay relevant in an era of evolving identification methods (e.g., facial recognition, etc.) looms large. As identification methods continue to evolve, so too will efforts to falsify identities, be it with detailed masks, fake fingerprints, and so on. Will Jumio be able to adapt to determine what is a ‘real’ vs a ‘fake’ face? Or will identity verification ultimately require more steps (e.g., two-factor authentication) and/or increasingly advanced identification techniques?
First off: what a great last sentence! I’m sure that AICO could come up plenty of options, but maybe not the best option (only humans can do that…for now!). The questions posed at the end of this post are through provoking in the context of the increasing role that AI will play not only in advertising but also in consumer decision making. I would offer that the greatest opportunity to accelerate adoption of AI in advertising would be from increasing penetration of AI in consumer decision making. Were this to occur, we could see a situation where automated ads adapt quickly to the preferences of automated consumers, hastening the pace of AI’s entrance into advertising.
While exploring the questions posed at the end of this post, I cannot help but think of the dichotomy between exploitation and exploration in product development. The example suggested above lives in the land of exploitation, where each incremental improvement championed by the ‘defense’ machine is countered by an improvement from the attacker that renders the defense’s improvement useless. Where, then, does exploration factor in? One possibility is that humans could out-explore the machines, even as machines out-exploit humans (and other machines) – basically, once the attacking machine has outmaneuvered (or, better yet, out-exploited) the ‘defense’ machine, human intervention could make a more wholesale change that requires the attacking machine to recalibrate, thereby regaining the upper hand. Of course, the vicious cycle of machine-outsmarting-machine would then begin anew, raising legitimate questions of the long-term viability of the human intervention strategy suggested above.