[AJ007]

Big questions to me are -

a) What is the computational power required? (stuff figured out back in the 80s turned out to work well for deep learning, but it took another two decades until the power was there at an economical price.)

b) What kind of mistakes do human-like thinking machine make? Additionally, are those mistakes actually a fundamental piece of general intelligence?

Some of the core pieces to general intelligence may be solved soon, but it could take a long time for the economics to become compelling or even possible. Presumably you can collapse time by throwing more resources at an "AI", but humans, with their brains running more or less 24/7 take many years before they can start producing really valuable work. We have assumed you can preload an "AI" with concepts or just copy and paste, but this might magnifying weaknesses just as if every employee at Google was a copy of Sergey Brin.

I except we will start seeing things, very soon, that begins to look very much like general intelligence, but the hardware limits will be an issue.