2)Why do you think DL doesn't scale? I am curious. It can easily leverage thousands of GPUs, training on 300 millions of images (https://ai.googleblog.com/2017/

2)Why do you think DL doesn't scale? I am curious. It can easily leverage thousands of GPUs, training on 300 millions of images (https://ai.googleblog.com/2017/07/revisiting-unreasonable-effectiveness.html https://ai.googleblog.com/2017/07/revisiting-unreasonable-ef...). No other methods is even close to leverage that amount of computational power. I don't really know about CFD, but at least in ML land and dealing with ML problems, DL is very scalable, maybe only next to random forests style algorithm, where they effectively share nothing. 3)It does matter. In fact most valuable startup around DL are CV based startups, they are mainly located in China though.