Earlier quoted context omitted.
One other thing I'd like to add. The scenario of AI mostly replacing people like doctors and lawyers involves bizarre paradoxes beyond whether deep learning "AI" works as advertised. Suppose you can train an "AI" to read legal papers or diagnose patients based on X-rays. That training is done from the data of real life lawyers and doctors actions. Suppose, best case scenario (very unrealistic imo btw), you have a com…
The only way you could be beguiled by this framing is if you don't understand just how inept most doctors and lawyers really are. A future configuration will probably look something like: far fewer highly talented doctors and lawyers remain employable while the rest are replaced by AI that's shown to be vastly more capable, and that is continually enhanced by the encoded expertise of said highly talented remaining sp…
It doesn't matter how competent or incompetent whatever professional might be. The only thing a deep learning application is going to do is duplicate their behavior. Deep learning involves no "thinking" at all. Just a very elaborate, brute force curve fitting. If the doctors are on average "incompetent" so will be the deep learning app (ie, you kind fall for the sort of "since it's a machine, it will be accurate" fallacy that makes people want to trust self-driving cars)
A future configuration will probably look something like: far fewer highly talented doctors and lawyers.
If you really automated the work of lawyers and doctors with explicit, maybe. BUT that isn't how "deep learning" work. Deep learning just uses data and the problem is you need sufficient data, a sufficiently large corpus of data to show by many, many examples what the thing should do.
Oddly enough, your scenario of high expert adding their expertise to the system is much more like the original Gofai model where a few experts would hypothetically program in their expertise. That scenario fell with difficulty of expertise programming. The present systems can't work that way.