BWV said:
An algorithm still needs a well defined problem and rules, real life does not fit this description so strong AI remains a pipe dream
The state of art and most promising AI currently is neurosymbolic learning; it's a combination of symbolic logic with statistical learning. Basically, the key is taking chances/guessing, learning from mistakes, and building new concepts by compressing raw information into multi level abstractions, then working with those abstractions and continuously refining them, similar to what humans do.
IBMs explanation is pretty concise.
https://mitibmwatsonailab.mit.edu/category/neuro-symbolic-ai/
Google is also making leaps in this area.
I would say the dream is already realized. Right now we are most likely about at the beginning of an exponential curve.
The thing is though, it's people who are the model. We are achieving our most advanced AI by trying to copy the mechanisms people use. And people have pretty sophisticated organs to do these things (that go beyond what we really understand).
AI on the other hand can be specialized, expandable, and immortal (imagine a 1 billion year old human that just kept learning aggressively the whole time).
Quantum computing will usher in a new era for AI as well, possibly pushing AI unthinkably farther ahead in some areas than people. Nano technology in combination with robotics will play a major role also.
I expect that at some time in the next few hundred years, we will be able to seed megastructure supercomputers with quantum chips in space (completely automated). Imagine a human brainlike machine the size of Jupiter that's been doing nothing but mathematics for 1,000 years.
I think pure math isn't at a big risk though. A lot of it is esoteric, and done without any certainty it will ever have applications. We only ever scratch the surface of an infinitely deep ocean. I think we do it more for the exercise of the mind (AI would do this too), and to build up abstract concepts that we can use, to advance our conceptual framework in general.
AI doing this stuff for us will be of little use and wouldn't replace our experience, unless we could make it a process we are intimately involved in and we can integrate what's discovered. But learning takes doing, exercising creativity, and making mistakes. So in any case, no matter how far behind we are, we should still do it.