I imagine it's similar to the struggle that the researchers that created those successes you speak of were going through before they had their success.
Of course, the article goes to great length to describe how this struggle is different, specifically referring to the fact that most game AI have involved perfect information and an easily stated win scenario to optimize for.
The real-world problems people expect more advanced AI, or AGI, to solve (better than humans) involve imperfect information and objectives that aren't as clearly defined.
Of the 4 examples you give, 3 are board games involving perfect information that AI are now better than the best humans, clear wins. The other you're referring to involves a self-driving car challenge where the first place winner managed to drive 60 miles in an urban environment in just over 4 hours[0]. 5-10 years later we still aren't talking about self-driving cars winning the Cannonball Run[1].
Of course, the article goes to great length to describe how this struggle is different, specifically referring to the fact that most game AI have involved perfect information and an easily stated win scenario to optimize for.
The real-world problems people expect more advanced AI, or AGI, to solve (better than humans) involve imperfect information and objectives that aren't as clearly defined.
Of the 4 examples you give, 3 are board games involving perfect information that AI are now better than the best humans, clear wins. The other you're referring to involves a self-driving car challenge where the first place winner managed to drive 60 miles in an urban environment in just over 4 hours[0]. 5-10 years later we still aren't talking about self-driving cars winning the Cannonball Run[1].
[0] https://en.wikipedia.org/wiki/DARPA_Grand_Challenge#2007_Urb...
[1] https://en.wikipedia.org/wiki/Cannonball_Baker_Sea-To-Shinin...