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On November 14, 2018, Litmus commented on Machine Learning in the Fragmented Construction Industry :

This is very interesting! I wonder – it seems like gathering data in this context (videos/photos) is very labor-intensive, but in order to continuously improve the system’s ability to detect, say a worker not wearing a hard-hat, would Skanska need to perpetually film/photograph the work site? I imagine data would need to be continuously gathered even after the model is “perfected” because there may be shifts in the construction industry (e.g. safety regulation changes). Is there an effort or option that improves upon the ease of data collection?

Reading about Chanel’s commitment to incorporating 3D-printing was very interesting. Thank you for the insight! It has made me curious what the ROI on this project might have been. How much are they saving and at what upfront cost? Likely not answers you can provide of course. I also like the fact that they produced an entirely new product to be 3D-printed instead of attempting to recreate one of their existing products. This allowed them to take advantage of design features only a 3D-printer could implement. However, I do think it’s also a little bit risky because consumers may not like the type of material of the 3D-printed product, which would render the equipment either underutilized or completely useless.

This is a very in-depth experience of how 3D-printing can facilitate rapid prototyping. Thanks for the unique insights! I am curious what the next step is after Centimeo has a prototype prepared. When it moves forward to scale-up, how might the company leverage 3D-printing? Would it be possible?

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

Interesting read! I had not thought about IP or privacy issues when it comes to crowdsourcing ideas. I am curious about the idea of crowdsourcing full recipes: when I think about it, this could almost be a type of snack startup accelerator. For those recipes that do best, Pepsico could partner with the innovator and realize his/her aspirations for the snacking world. At the very least, it would be a great marketing move!

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

Great piece on Tesla and an interesting question about whether the organization should take its IP and technology development back behind closed doors. I recall my reaction when I read about Tesla opening up its innovations and IP to the public years ago. I was positively surprised, really supporting the idea that EV development was something important to society and should be embraced by other auto manufacturers.

I would argue that Tesla should remain open. This is mainly because it makes no difference if they close their innovation – competitors will still be able to reverse engineer Tesla products. They would then begin leveraging Tesla’s pioneering tech to improve their own and consequently will catch up eventually. The only way Tesla maintains its competitive edge is to continue being the icon for innovation in EV technology, which is supported by its open innovation policy.

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

With a 20% chance that an account is incorrectly flagged, there is significant risk to shutting down real consumer accounts by mistake. Until the machine learning model that Twitter uses hits an acceptable accuracy, which I imagine would be an error rate in the ballpark of 1 per 100,000 (giving roughly 3000 wrongly flagged accounts), Twitter will need to augment its machine learning model by manually back checking these accounts. It would be far too damaging to the company’s reputation if a fifth of its user base was labeled as fraudulent.