Flying spaceships is actually pretty easy for computers, because it involves fairly straightforward physics calculations and a limited number of decisions to make (e.g. which engines to fire at which time to reach point B.) A spacecraft autopilot is quite a bit less complicated than a system like Watson, so it's a bad example.
1. Expert systems can learn more information based on the rules and associations they already have. Depending on how fuzzy its logic is, it could have relations like the following:
* All mammals have hair.
* All mammals are animals.
* All dogs are mammals.
* Yorkshire terrier is a breed of dog.
* A breed is a subcategory/type.
* All Yorkshire terriers are dogs, mammals, animals, and have hair.
That's a very simple example, but it's relations like these that make up the knowledge of a system like Watson.
2. That's a poor question because computers interact with humans in all kinds of ways. If you mean carrying on a conversation with humans, well, programs like ELIZA remain perennially popular despite not being all that advanced. They are just human enough
to fake it. Alice
is a more recent example that's good at conversing with humans, albeit in text form. Speech recognition is pretty good these days, so it wouldn't be hard to make a talkie version.
Computational speed is less relevant than memory access strategies. Watson's uniqueness is in the vast amount of information it can sort through quickly, not how fast its CPU is. It is far more data-bound. So, that means very fast (and ample) memory, with fast CPUs being of secondary importance for its purposes.