What was "solved" with AlphaGo was using deep learning machine learning which are effectively black boxes. There was a certain assumption in the question for AI researchers academically that it would be an understood algorithm as an AI agent like a Prolog application, not a brute forced model. That's still not the case that we have a "solved" strategy and all we can do is watch it play as if it is a deaf mute player.
So there still is no "tic-tac-toe" known winning strategy to Go or anything.
That doesn't make AlphaGo any less impressive or any less practical, but it even has its own readout issues. It can't even read ladders without hard coding it in, for instance, because it becomes a long enough depth search. This is one of the first things a newbie would learn.
It's just a 19x19 board, so it was always known if you could read all the possible outcomes you could see all the possibilities and win. This is just looking at all possible outcomes and picking the best one, not knowing how to play. Creating models of data that is 2, 3, or even 4+ dimensions is always possible, just depends on how much computing power you can throw at it. The created models are essentially aggregate simplifications to play quicker.
Generalized intelligence is so much different. You have to define the problems themselves that you are trying to solve, figure out what the variables are, and solve it. Then you have to operate and run the machinery to create those experiments. Outside of a scenario that you've taken actual physical territory as an intelligence, I can't see how it would get there (think Terminator or BSG, doesn't have to be malicious but they'd have to be in control of the physical area autonomously).
But the hardest part is defining the problems independently given the sheer number of problems they'd need to define second to second just to solve basic tasks, and they'd likely have millions of variables with millions of possible values.