C. Xu

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On November 15, 2018, C. Xu commented on Improving Public Safety with Machine Learning :

Great read! Besides the bias and discrimination that you mention, another concern is that safety can be used as an excuse to overexert control on the public. For instance, machine vision has developed to a point whereby it is feasible to monitor where each specific individual is located at all times. While this seems like a scene out of a dystopian movie, we need to be careful that we don’t move towards that direction under the premise of creating a safer society.

On November 15, 2018, C. Xu commented on Additive Manufacturing at GE Aviation :

Great article! I think one additional concern to keep in mind for GE is that current 3D printing technologies are far from being fully fleshed out or optimized. Matter of fact, I believe that Arcam and Concept Laser, the two companies GE acquired, currently use two different 3D printing methodologies. Hence, GE needs to be cognizant of the continuously evolving nature of the 3D printing technique and avoid over committing to one particular 3D technique as it looks to scale additive manufacturing.

On November 15, 2018, C. Xu commented on Steam-powered ideas: a market for open innovation :

Very interesting read! One way to potentially increase monitoring of the content while keeping costs manageable is to leverage machine learning solutions. Filtering spam email for instance is one of the oldest and most successful commercial use cases of machine learning, and a more elaborate version could be applied to Steam, whereby a machine learning algorithm would review the content proposed first, and either publish it, reject it, or push it to a human to double check if the confidence level of the algorithm is below a certain threshold.

Great post! One thought to add would be that moving towards better data as you suggest could also allow machine learning to play a larger role in this process. While innovation and creativity are certainly not areas in which machine learning today is very capable, the benefit ML brings is that it does not have any “pre-determined approach to addressing the opioid crisis”, but rather takes a purely correlation driven view of the data, which could potentially help spur original ideas in humans.

On November 15, 2018, C. Xu commented on American Express has Struck Gold with Machine Learning :

Very interesting read! I think the data American Express collects can be used even further beyond the cases you described here. Credit card purchase data is incredibly valuable as it is itemized and has a location and a time stamp associated with it. Using this granular data and machine learning could yield powerful customer profiles that any B2C company would love to get their hands on. The key question is under what circumstances if at all American Express is allowed to share that data with others vs. keeping it for their internal uses only.

Thank you for a great article. I fully agree with you that the short term impact of fully embedding 3D printing into local communities by training community members could be limited. To me, 3D printing as of today is still primarily used for prototyping rather than mass producing a variety of different pieces. Additionally, the logistics of housing and operating a fleet of 3D printers is also no easy feat and currently predominantly achieved by large industrial companies such as GE, which acquired two 3D printing companies just recently. Hence, even if community members could get trained on the technology, a meaningful impact increase is likely still out of reach currently.