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On November 15, 2018, scuba_steve commented on Nike’s 3D Printing: Just Do It :

This is great! I could definitely see printing shoes live in a Nike store becoming a hit product. No more massive stock of various sizes and colors, instead just raw materials at the store level that can be used across the product line. Not sure I ever see this transitioning to the house level though, not sure it’s really required when someone can simply have a centralized large scale printer capable of producing huge swathes of products.

On November 15, 2018, scuba_steve commented on Using machine learning to bend the healthcare cost curve :

Curious to understand what this means at the health clinic level. Does less time spent on importing information mean more time with patients, or is it simply cost savings in terms of fewer positions. Agree that the ability to have this information in a database type platform opens a lot of possibilities in terms of tracking and reporting on diagnosises based on conditions – but there’s probably a fair bit of data merging required for that which introduces some intricacies.

Curious to what degree the WHOOP platform would benefit from a standardized baselining workout in order to provide more reliable information. It seems to me that having standardized workouts that can be tracked and compared would provide a better baseline for athletic performance. From there individuals can be categorized based on performance and then assigned more meaningful steps to improve.

On November 14, 2018, scuba_steve commented on Data is the new oil: Rio Tinto builds new intelligent mine :

A man after my heart! Great write up Gavriel. This space is very interesting and a lot of the major mining companies are following Rio’s lead. Barrick Gold Corp and BHP to name a few. With the automation of equipment in open pit settings the ability to interact with the type of data you’re describing and even intervene is significantly higher than it has traditionally been. However, the point around needing expertise to get these types of systems up and running is well founded. The reality is even with the data coming from existing system (mine dispatch software – similar to what Uber dispatching) that the horsepower and expertise does not exist on the company level to make use of it.

A good place to start is programs like Tableau. They’re intuitive and thereby quick to learn. Mining Engineers with a little training can be brought up to speed quickly and used to begin tapping in to the wealth of data available today. Somewhere to start at least!

On November 14, 2018, scuba_steve commented on Machine learning ruined baseball. Machine learning will save baseball. :

We might also want to consider the human element here – are we simply approaching the limit of what’s physically possible given today’s rules? Data might help us here too. How many guys are throwing 100+ mph today? It’s higher than it was even 10 years ago. Perhaps we need a few more feet than the 60′ 6″ to home to get the same performance out of the boys at the plate.

Curious to know what specific surgeries/repairs are prime for this type of technology. Is it simply any sort of implant? Or are specific areas where customization may be particularly useful and/or is a step change in performance and comfort compared to what’s done today.