BUT... if we only care about the outcome and the utility of the AGI, and don't expect it to be fully "human like" then I think that Watson (and Deep Blue, etc.) do represent steps on that road.
You can probably guess that I lean towards the latter viewpoint. I fall back on the oldchestnut that "We didn't build airplanes that fly by flapping their wings like birds, but they fly nonetheless" as an analogy. We may not build machines that think like humans, but they will still be intelligent, nonetheless.
I'm not even sure whether that's a meaningful distinction. Searle's Chinese Room et cetera.
I'm with you in the wing analogy, I fully agree. I just don't think Watson or Deep Blue is a significant approach even in that direction.
Human-like or not, we have certain expectations from our hypothetical AGI in terms of the kind of new problems it can solve without too much help. I guess what it mean is: I think it needs to be able to solve a problem it wasn't designed upfront to solve. No serious system I've ever seen attempts to do that.
The man in the room may not understand Chinese, but he is using a dictionary, which to a casual observer appears suspiciously thick. Just how thick would the dictionary need to be in order to provide a sensible response for all possible lines of enquiry?
Searle's argument reduces a room which understands Chinese to a dictionary which understands Chinese. A strange loop indeed.
With regard to serious systems attempting to solve AGI — are you aware of Jeff Hawkins' work on hierarchical temporal memory?
I haven't followed any recent research, sorry.