>But there's a very large amount of people focused entirely on aims that are very, very distant from actually making human lives genuinely better.
I can't speak to each and every person working on ML, but I thought I would share a fun use case I ran across the other day.
There is a business in some foreign country that is similar to Uber Eats: customer goes to an app, browses for food from various restaurants, orders, it gets delivered.
The business was using ML to help the restaurants: the restaurants upload a pic of the dishes, a title, and a description (usually all from an existing menu). The business would parse the description to guess at what was in the dish. Scan the picture to guess at the quantity of food (entre, side, desert, etc). Compare ingredients against publicly available nutrition info. Now the end consumer can do things like: search for gluten free, vegetarian, pork free, <300 calories, desert, etc.
Almost all of this was "possible" before, but it would have required enormous effort from the restaurants inputting the data or customers reading each item. Now it is "easy", and it actually helps the end customers - and the restaurants.
Sounds like this would be an epic fail. I mean, just add a spoon of oil more, and your calorie guess is totally off. This is a clear cut case of SEEMINGLY helping. It certainly does not help the end customer. It might help the restaurant, as they don't care if the customer is receiving valid information, as long as they are buying.
You get a whole lot of points for discovering something, designing something, or a proof.
But there's a very large amount of people focused entirely on aims that are very, very distant from actually making human lives genuinely better.
Mostly because everyone quietly understands all the extraordinarily complicated mathematics is actually extraordinarily complicated.
Hence the ROI isn't worthwhile.