1.Problems that we don't know how to solve yet, but we think we are close to based on "similar" problems we have solved.
2.Problems that we have a solution for, but it currently takes an unreasonable amount of time to use these solutions in practice.
Problems in class 1 are like AI in the 1960-70s, everybody thought we were super close to amazing AI based on discoveries we'd had, but these estimates were very wrong.
Problems in class 2 are like nlp and ml work in the 90s and 00s. A rather large chunk of 'wow' ml/nlp we have in applications today were pretty much solved 20 years ago, but there was no sane way to run them, certainly not on your cell phone.
Problems in class 2 are safer bets, there does seems to be consistant increases in processing power, memory, etc. Problems in class 1 are harder to guess because as history has shown, just because a solution seems similar doesn't mean that it actually is (shortest path is solved, longest path is NP-hard, shortest path touching all points once (ie. tsp) is NP-hard).
I think it's safe to say having Watson on our smart phones is right "around the corner" (20-30 years?) saying that we'll create "creative" AI, not so much.