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Great article Mike – though I am quite interested and curious to know how, upon application of ML, the system is trained for a real target. I am asuming that with advancments in technology, what are the typical responses from the targets to deliberately release noise (maybe by generating signals that are no indicators of its position etc.) and how does US Navy trains its systems to identify such objects in real time.

The article, yet again highlights the fact that not every revolutionary idea makes it big. While many ideas may seem promising, what is crucial is a capability to even execute the idea on ground. In my opinion, Quirky did a great job interms of sourcing and evaluating great ideas in Phase-1, but overestimated its capability to launch the implementation that led to its demise.

On November 14, 2018, Sandeep Singh commented on 3D Printing Straighter Smiles :

Interesting artcile. If its a superior product with lesser cost, a dentist who is in most cases the decision maker for which type of aligners to go for, might make more money per patient and should be incentivised to sell more of this less expensive but superior shape product. Are 15% market share more an indicator of the dentist’s reluctance to change in matters related to teeth where patients often dont want to experiment.

On November 14, 2018, Sandeep Singh commented on Open your open innovation to suppliers :

Great article and gives me an insight into how Dell manages its suppliers so well. However, I find a potential conflict with approach. The first step of selection is based on a supplier’s existing technical capabilities. These capabilities are further enhanced post the training which widens the gap between suppliers. If that be the case, unless a supplier had a close idea of getting selected, it would be difficult for them to share.

On November 14, 2018, Sandeep Singh commented on Hinge: A Data Driven Matchmaker :

Great article, makes me understand how the dating apps work. However one limitation with dating apps is that you make a selection (or rejection) based on how one looks and / or what their certain bio traits are. These are good markers to begin with but do we have any data to suggest how other cirtical parameters in chosing a partner are coded – behavorial traits such as being humorous, being understanding etc. which are more important than what’s my hobby while deciding my partner. Also are there ways that one can cross check if self reported facts are true – I would want to belive I am funny even though noone else finds me funny.

Great article and puts into perspective how advance techniques such as ML / AI / 3D-Printing are disrupting the healthcare field. Having worked in a an integrated healthcare company, I have a view on why it makes sense for Medtronics to continue doing what they are doing. Often embracing such technologies is a slower game for healthacare firms, even though the benefits could be manifold, due to probable risks. In such cases, a business always choses to go deeper into existing business segments and use technology to incrementally add value to existing business. Also we have seen how personalised medicines have had huge benefits only in certain disease conditions – applying the same logic to devices, unless there are tested reasons to belive that personalised devices will add value to the clinical outcomes, Medtronic still may want to avoid the topic.