Christopher Reynolds's Profile
Thanks for this interesting take on an established company recognizing the potential disruption from Machine Learning and making that a core focus to maintain its competitive advantage. One thing that strikes me as part of Google and Alphabet’s competitive advantage is their ability to be right more often than not when it comes to investing in new opportunities or through M&A with promising up and coming companies. With their uptick in M&A with a focus on ML and AI many of these investments seem to have resulting in value add for their core and even tangential products and services. While I grasp the importance of ML in google’s context and also the continued importance of machine learning to continuously iterate and improve its processes and products, I wonder how significantly its strategy and investment might shift as new disruptive trends arise. Will google truly continue to be a machine learning company or is this merely a titanic company’s response to emerging trends and competitive threats?
Thanks for this article. This strikes at the heart of something that’s been concerning me with machine learning for quite some time which has to do with the objectiveness and quality of the inputs to the algorithm. I don’t see any way that historical inputs do not perpetuate structural and societal biases or even more so confirm biases in people when they see a high SSL score. I think there is an inherent danger in the unchecked feedback loop within the ML system as well as an unchecked feedback loop from the SSL score to the individual interpreting the score. I think this could be a good tool in the toolkit if it yields responses of increased support and resources to mitigate potential crimes rather than reinforce societal expectations. I would focus on training the user of the data to understand what the output means and what actions they should take based on that output, which I fear will not happen considering significant resource constraints as well as a low prioritization.
Thanks for sharing this article. We’ve often discussed the notion that AI is not replacing human expertise but rather being used as a supplement to that human expertise. I think using machine learning early in the drug development process to help address attrition is fantastic in theory but it definitely raises some concerns. My main concerns around this have to do with biases. Would the inputs to this ML algorithm further proliferate existing biases and unknowns thus limiting a portion of the funnel of possible breakthrough ideas in the field of medicine? I understand the costliness of drug development and the sunk costs of attrition in this process which is why I think this is good idea, however, I think there needs to be a way to supervise, monitor and check against some of the unknowns and biases that might be stunting the effectiveness of the AI.
Thanks for writing this article. I think the ethical debate around additive manufacturing is just beginning to occur. As with many disruptive advances of the past, it is easy and convenient to think about all of the good the innovation can bring to business and society, but incredibly difficult to consider what steps need to be taken to ensure its ‘proper use.’ I believe that laws and regulations in this space are certainly a hot discussion topic and will be necessary moving forward. My issue with that is it might stifle the growth and development of the technology necessary to reduce up-front investment costs and use for large scale and extremely beneficial applications in various fields. Since you’ve caused me to pause and consider some of the risks of this new and innovative technology, instead of just the benefits, I think others to consider are: (1) the potential for monopolization (2) issues with different laws and regulations across borders resulting in high development in some areas and little to no development in others.
Interesting and thought provoking piece.
Thanks for sharing this article about MoS. It is my understanding that this company is on the cutting edge of this technology in the fashion sector. You’ve clearly laid out some of the operational benefits of using additive manufacturing and machine learning such as reduced waste and optimized user experience. I think one of the issues of remaining accessible and practical for their consumers is their price point (which you mentioned) as well as the innovation itself.
I don’t know enough about the company to say what they should or shouldn’t do, however, I think they find themselves in a dichotomous situation in terms of their innovation. I believe a portion of their customers explicitly seek out MoS due to their cutting edge innovation and use of machine learning and will always want them to continue pushing their innovation as far as possible. However, their clothing needs to be similar enough in use and treatment to normal, every day clothing for the broader market of consumers to be interested and find it practical, perhaps putting pressure on MoS to rein in its innovation to reach the mass market.
You raise some fantastic points in this article and it has me wondering if MoS is viewed as an innovation company that sells clothing or a clothing company that uses innovation and how that might influence their strategy moving forward.
Interesting take and a creative solution to some of the persistent issues plaguing the public sector. I think crowdsourcing provides two significant benefits to the city of Boston here; (1) gathering and leveraging innovative ideas in the face of time and resource constraints and (2) increasing constituent engagement. However, I agree that it should not be overused – I see crowdsourcing as means to help identify issues from the bottom up or to gather potential ideas to solve specific issues which in essence widens the funnel of ideas entering the product or service development process at the city level. I have one major concern with your recommendation to learn and partner with the private sector which could cause issues with privacy and trust between the government and its constituents. For that reason, I think it would be unwise to share data with and use data from private sector companies. I do see this trend expanding beyond Boston as governments seek to engage citizens, learn the true issues facing their constituents and more effectively utilize its resources to solve the important and pressing issues facing them and I’m interested in seeing where this goes next.