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It's important to keep in mind that the recent progress of AI has been in solving specific tasks, rather than in the development of more general abilities (e.g. general intelligence). And that our solutions to these tasks don't exhibit the abilities humans leverage to solve them.
There are key differences between being able to perform a task, and possessing an ability -- in particular generalization (being able to perform entire categories of related tasks without further human intervention) and open-endedness (figuring out what new task to tackle)
It's a very anthropomorphic cognitive fallacy to believe "I use my intelligence to perform X (e.g. multiplication and addition), therefore if we were to develop a machine that could do X, it would have to exhibit the same intelligence too".
A single shortcut trail through a corn field is a fundamentally different beast than a road-building company.
Hint: Given a true general purpose problem solving machine (GPPSM) that has an understanding of the whole domain not just one of the current problem, the problem of attention, i.e. it working out what to do next including both what sub task and what new task, is just a problem =>
for it to solve that it can solve in the same way as it solves other problems. (I spotted this around 2008 and found it to be a key aspect of the machine going by itself). It must achieve attention using intelligent problem solving and not some hardwired [executive] process =>
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