> ... But researchers are struggling to apply these systems beyond the arcade.
It hasn't been 2 years since AlphaGo v Sedol, and there was a gap of 5 years since Watson, about 5-10 years since self-driving AI (Google, DARPA challenges), and about 19 years since Deep Blue v Kasparov.
Zero-knowledge AI, at the level of arcade games and Go, is barely a few months old.
What is that 'struggle' that you speak of? Does it go by the name 'media wanting a new sensational story every week'?
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].
It hasn't been 2 years since AlphaGo v Sedol, and there was a gap of 5 years since Watson, about 5-10 years since self-driving AI (Google, DARPA challenges), and about 19 years since Deep Blue v Kasparov.
Zero-knowledge AI, at the level of arcade games and Go, is barely a few months old.
What is that 'struggle' that you speak of? Does it go by the name 'media wanting a new sensational story every week'?