It's very unclear to me that there are a large number of companies that can increase their efficiency by much more than 5% through ML. And it's not clear to me that there is more than a couple-year's-worth of projects in turning data exhaust into money for each company.
So if I were a new grad looking to get into ML, that might somewhat concern me.
Not going to pick up on your arbitrary 5% constant here, but please elaborate how these ML engineers are any different from anyone with a quantitative background?
At scale it makes a big difference.
If research stagnates, application willl also stagnate. For the industry money to keep pouring in there has to be growth and for there to be growth there has to be progress- scientific progress.
So if something is "not ideal for fundamental research" it is also "not ideal" for business.
ML is pure alchemy for a business operating at scale. It’s as if a coal plant could turn its waste emissions into profit. You have this “data exhaust” from all the activity happening within your system that can be used to optimize your system Atleast a few percentage points beyond what is other wise possible. A team of 5 ML engineers can improve an ad targeting system by 5 % and if the company is google that’s billions.
ML creates feedback loops of improvements in product that improve usage that lead to more data which further strengthens the moat a business has.
It totally makes sense to jump on this train. It won’t solve AI but will make a lot of people wealthy optimizing systems.