I mostly agree with you - there is a lot of "something something machine learning!" going on without any concrete use-cases that it enables that wasn't possible

I mostly agree with you - there is a lot of "something something machine learning!" going on without any concrete use-cases that it enables that wasn't possible before. I think it will just be another tool available and we'll slowly see it creep into places without making earth shattering improvements. An example that comes to mind is IDE auto-complete that GitHub and others are doing - better than what we had before, but not exactly revolutionary Apart from within academia and niche places like deepmind, I get the feeling that ML roles and positions will become the new DBA roles of the past - ultimately super-dull jobs where you are just shuffling around storage space, migrating data, or provisioning new tables/models etc without actually adding any business value or getting involved in any of the user-facing work etc. It is already starting to get heavily commoditized with downloadable pretrained models etc... I see a lot of interns etc who want to come solve cancer with machine learning. I hope that there are not too many youngsters better their career on making it big in machine learning (unless as a researcher) because I genuinely feel like machine learning will just be some library/black box that 99.9% of the time will just be a downloadable pre-trained model that you add like you would if you needed to add OCR support to your product. I am sure there will be some exciting new things that we'll see of course, but I think they'll be step-wise improvements, rather than huge leaps that open up entirely new worlds of opportunities. E.g. CNNs made image recognition much better (and anyone can now download a really powerful pre-trained model and beat state-of-the-art from just a few years prior etc), but it only made image recognition better - we could do it before, just not as well.