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On November 15, 2018, Nametagfriday commented on Will 3-D Printing Take Us to Mars? Relativity Space Thinks So. :

I really liked reading this one – thanks Mario. This application of AM seems strange to me. As I understand it, the advantage of AM over traditional approaches to product assembly (apart from the supply chain benefits that you mentioned) is that AM lowers the cost of building one of something – so prototypes and customization are both cheaper. Once Relativity has a working rocket that uses ~100x fewer parts that what SpaceX is doing, why can’t someone else build rockets the same way?

It’s amazing that this could really be a $500M business in 5 years. One more (novelty) use case that might be possible using this technology is “branded food.” I’m imagining a pancake that has Hershey’s name printed into it in chocolate ink. They could extend that to other brands too. Maybe food is the next advertising medium?

On November 15, 2018, Nametagfriday commented on Leveraging Machine Learning to Reduce Spam on Twitter :

I agree with you that it would just be easier to stop bots from creating accounts than it is to find them. I wonder why they aren’t already using reCAPTCHA (if they aren’t)? It seems like a widely-adopted technology at this point.

This article also made me thing about the uses for ML to filter on other social media sites. Can Facebook use it to filter out fake news?

On November 15, 2018, Nametagfriday commented on Irrational Exuberance: Machine Learning at the Federal Reserve :

Really interesting, well written article – thanks! Before reading this, I hadn’t thought of using ML to fill in the gaps between the GDP/inflation/unemployment estimates that are spaced out. I wonder whether ML can really make policy recommendations. My understanding is that ML algorithms work by guessing the relationship between a set of many input variables and a single output variable by repeatedly guessing and receiving feedback about the quality of the guesses. It can take a lot of iterations before the algorithm “learns” to guess well, it needs a lot of data. I wonder whether 50 years of monthly economic statistics (measured with error) would be enough. There might also be a problem where the algorithm would never recommend policies that hadn’t previously been tried.

Thanks for writing this – I enjoyed the read. In the case of toilets and India, I wonder why it has been hard to take products to market. Who was the intended buyer (families, NGOs, government)? Did the buyer not see value in the products, or couldn’t they afford to buy them? Were the products too hard to produce and distribute for some reason? Toilets are bulky and heavy, so they might be hard to transport in a place without good roads.

On November 15, 2018, Nametagfriday commented on Grand Challenges at Bill and Melinda Gates Foundation :

Interesting article – this left me wanting to learn more. I’d imagine there are some ideas presented through the Grand Challenges program that are worthwhile but don’t receive funding because they don’t align with Gates Foundation’s criteria (e.g. they are hard to scale). I wonder what happens to those projects. If they are abandoned, there might be an opportunity for the Foundation to find more partners to keep those ideas alive. I’d also be curious whether some kinds of problems are easier to solve with open innovation than others.