A point that the article doesn't touch on directly, but it's part of the bottlenecks mentioned: a lot of jobs already are bullsh*t. They are there because a scapegoat is needed or because the nephew of the CEO needs a job, etc. In theory these jobs could have been removed long ago but they were not, and AI won't change that.
Why would driverless cars mill around? They would just wait around in underground garages. They can even block each other, so they don't need that much space to park.
I was reminded of "software is eating the world", which was basically the idea that everything needs software. It's still true. In that light, programming is not cooked. We can do things much faster, yes, but there are so many things to do that it will still take a ton of (let's call them) qualified people to build them all.
A lot of the software produced in big corps is mission-critical. Self-driving cars are an extreme example but I think the same principle applies to banking, infrastructure, even things like maps, since they are used by billions.
Agree, but I wanted more. What is the intuition behind the optimality proof? I realize you cannot summarize a 119-page paper in two paragraphs, but still.
I have an issue with the first point as well, but differently. Having worked on a user-facing product with millions of users, the challenge was not finding user problems, but finding frequent user problems. In a sufficiently complex product there are thousands of different issues that users encounter. But it's non-trivial to know what to prioritize.
reply