John Smith

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On November 14, 2018, John Smith commented on Taste the Future: 3D Printing Chocolate at Hershey :

Very interesting article! While reading this, I could not help but wonder if the end-goal of the chocolate making was only a novelty for fans. Definitely interesting and cool, but I could not wrap my head around how it could possibly get any bigger than what it was.
However, I was very intrigued by the premise of nutritional property design. I have always considered 3D-printing in a manufacturing setting, but never in such a intriguing culinary atmosphere. This technology could truly tailor personalized meal plans which could benefit so many people. I love the idea of a company chasing this end goal. The ultimate question though is the time of production. Will 3D-printing ever achieve the speed required to produce on a grand scale. If so – this has potential to go far beyond a novelty.

Thanks for the really interesting insights. I can definitely understand for the start-up why it was so important to iterate quickly and I think it was a great idea to utilize 3D-printing. What I found particularly insightful was the synergies mentioned between different products. Although I understand how 3D-printing could improve development speed for separate products individually, I was unaware how one product’s development (chewing gum) could aid the speed at which an entirely separate product could be developed (nail polish).
Although the development is fascinating, I am curious about how much scale is possible. The speed of prototyping does make sense, but once I product is complete and tested, does 3D-printing work as well on a large scale as typical manufacturing?

On November 14, 2018, John Smith commented on Leveraging Machine Learning to Reduce Spam on Twitter :

Thanks for the interesting insights! The article certainly highlights the interesting position that Twitter is in. On one hand, it must continue to live its brand proposition of instant & condensed communication. However, in an era of increasing spam, it must also be the company’s priority to substantially monitor its content. Using machine learning makes sense. However, I was struck that is accuracy rate is only 80%. I worry that as “real” accounts are labeled as “fake”, Twitter could lose users and negatively impact their entire community that is already the subject of increasing backlash. I would hope that the author’s premise is correct and that as this is a new technology, there is much room to grow in terms of accurate “fake” account identification. At the end of the day though, I still think Twitter will need its user base to also help police the site as I think the computer learning can only go so far while trying to balance on the delicate line of mislabel too many accounts.

On November 14, 2018, John Smith commented on Airbnb: Utilizing Machine Learning to Optimize Travel :

Great read with interesting insights! Being an AirBnb user over the years, I have sensed improvements in the search functionality. However, I never realized that the improvements were based on the connections between my searches. As I think about my experiences going forward, I really like the idea of the image classifications based on what I care about most. I assume this would be a major time save more me and would help me analyze options in a fashion that better suits what I am after. However, I am a little worried about how AirBnb might over-assume what I am after. Specifically, I have used AirBnb for very large groups as well as for two people. In each case, I am concerned about different things. How will AirBnb recognize the differences in what I am after in any given search?

On November 14, 2018, John Smith commented on Open Innovation at Tesla – Time for a Change? :

Thanks for the insights on an interesting topic and company.
After reading this, I think Tesla is in a difficult position. Although I can see the initial logic in its open innovation policy (rising tides raises all boats), I think its current financial position and new competitor base leaves them vulnerable.
In terms of the financial position, Tesla is currently sitting on a massive amount of debt and is burning cash at alarming amounts. At the same time, they are falling behind on production promises and are asking their suppliers for longer payment terms.
At the same time, their competitor base is not coming from additional start-ups, but instead from major automakers. These companies have significant capital and manufacturing know-how to leverage in their electric vehicle development.
Looking at this landscape, I think Tesla should very much consider going towards a closed-innovation policy in an effort to maintain their advantage of being more advanced. That head start may not last long.

Thanks! This was a great read. As Walmart continues to compete in an industry that is becoming more and more competitive, this investment in machine learning makes a lot of sense. Particularly, it is certainly a mechanism that can enhance profitability while top-line sales grow at a slower rate. Food waste through the supply chain is enormous and the more advance a company can become in managing it, the better they will be in the long-term. Concerning next steps though, I do think further developing Eden in developed markets in the right way to go. Working out the glitches in a more robust supply chain is probably safer than moving directly to developing countries. However, I do see long-term value at helping multiple geographies better manage their food supply chains.
As for the consumer angle, I am not sure I agree with the article’s stance. Although Walmart may be able to improve their quality of fresh produce, I believe that the Whole Foods customer is a completely different audience. And pulling that target market to Walmart may be a bigger ask than just fresher produce.

On November 14, 2018, John Smith commented on It’s all open at Wikimedia Foundation :

Thanks for sharing! As a Wikipedia user, I enjoyed reading this article and gaining more insight into the entire Wikimedia ecosystem. The positive feedback loop in the center of crowd-sourcing structure makes a lot of sense and I can see why there was a shift in the initial strategy of the company to embrace this model 100%.
While I understand the use of crowd-sourcing to do product development, I am very surprised to hear about how the company is using it to set it strategic direction and execution. I would imagine management playing a much larger role here in order to provide the best boundaries and structure to the platform for its users. However, I can also see the other side of the coin how involving its users beyond just content generation will further enhance the entire product.
I am interested to see where the company goes from here. Bias in content is definitely a concern and that bias leaking over to strategic execution should be something that management is equally keeping an eye on.

On November 14, 2018, John Smith commented on Crowdsourcing snack food trends at PepsiCo :

Thanks for sharing this! I really enjoy the idea of bringing product development closer to the consumer and this campaign is a pretty creative way to increase engagement across the Lay’s portfolio. Although I think it would be pretty cool to move from solely idea generation to prototyping, I do think it may add more complexity to the process than it is worth. Perhaps if you could socialize the prototyping exercise wider than just the Pepsi corporate office, then that might be a wider form of engagement. Regardless, I agree it would be great to find a way to translate these crowd-sourced ideas into longer term success on the shelves!