> The problem isn't getting a computer to ride a bike, it's getting a computer to learn to ride a bike how a human learns.
This is just a more sophisticated way of assuming that there's something human-intrinsic about bike riding. Human-like is not the only way to approach doing or learning. Whatever works, works.
I could not agree more there is no evidence of any kind that there is some process that is human-intrinsic from perspective of neuroscience and computer science. Furthermore learning is just subset of intelligence, it seems to me most arguments are about concept of intelligence where such concept has different meaning for each participant.
>Human-like is not the only way to approach doing or learning.
I would also say that we shouldn't agree that there's an irreducibly human-like way to do things that only belongs to humans. The things we think 'belong' to humans, such as our intrinsic bike-riding ability, may well turn out to be not intrinsic at all, and able to be modeled in all salient ways by a machine.
I think if we allow that distinction to be made, and proceed to argue that machines can learn in different ways, it allows a very strange human-essentiallism to go unchallenged.
But humans learn by themselves, from observing the world around them and drawing conclusions. Which is a major advantage and a core problem in strong AI.
The only reason it works is that a lot of humans put a lot of effort into more or less figuring out exactly how to ride a bike. It doesn't generalize at all, teaching the same robot to ride a skateboard would mean doing it all over again.
Yes, and not just hypothetically; more generally, transfer learning is definitely a thing, and has been shown to reduce training requirements for unrelated tasks.
This is just a more sophisticated way of assuming that there's something human-intrinsic about bike riding. Human-like is not the only way to approach doing or learning. Whatever works, works.