I think that humans abuse discovered patterns and structure in language and meaning to search through possible interpretations very quickly.
Right, but does that structure really represent a "deeper" understanding or just vast and meticulous optimizations of statistical algorithms similar to Watson's? Or is there a difference?
We feel like we know how we think, but we can't actually explain it in enough detail to reproduce. Humans have a bad history of rationalization and tunnel vision. And now we discover that all the "wrong" ways to think deeply are actually the right ways to make a working AI.
If the AI can fool us into believing that it "understands" then maybe we can fool ourselves in the same way.
I don't honestly feel like we know how we think at all. I do think that statistics is a pretty good bet for the "math of learning" in that it's a sensible way to track how information flows through a model. Furthermore, the combinatorial problems involved need to be tackled just the same by humans so we can maybe try to say that we're studying similar phenomena as the workings of the brain.
Of course, the implementations we build will always be vastly different from their appearance in the brain since the architectures are so extraordinarily different!
Right, but does that structure really represent a "deeper" understanding or just vast and meticulous optimizations of statistical algorithms similar to Watson's? Or is there a difference?
We feel like we know how we think, but we can't actually explain it in enough detail to reproduce. Humans have a bad history of rationalization and tunnel vision. And now we discover that all the "wrong" ways to think deeply are actually the right ways to make a working AI.
If the AI can fool us into believing that it "understands" then maybe we can fool ourselves in the same way.