On Mon, Aug 4, 2008 at 2:55 PM, Harry Chesley <[EMAIL PROTECTED]> wrote:
> the argument being that a dictionary cannot be complete because it is only 
> self-referential, and *has* to be grounded at some point to be truly 
> meaningful. This argument is used to claim that abstract AI can never 
> succeed, and that there must be a physical component of the AI that connects 
> it to reality. I have never bought this line of reasoning.

The "physical component" is not necessarily needed, but grounding
NL-only input well enough to support reasoning is nearly impossible
(unless the system already knows a lot). When your AGI starts
learning, you need to provide some extra support for grounding /
categorical perception. You can do it through embodiment (real-world
or simulated) which is IMO a risky approach because of the level of
implementation difficulty (which could easily kill your project), OR
you can use a formal language which will help your AGI to semantically
sort out the input(=possibly [initially] less user friendly, but fewer
resources needed for implementation + you can go for NL support later,
after implementing input-understanding, reasoning & possibly scaling).

Regards,
Jiri Jelinek


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