Harnad's symbol grounding paper has been criticized some
times, but it remains a seminal idea. The problem faced
by many tradicional artificial cognitions is the exclusive reliance 
on arbitrary symbols, such as linguistic inputs. That approach
is appealing, and has fooled (it still fools) many researchers 
of the field. But it is very difficult to associate intelligent 
behavior with the manipulation of arbitrary (amodal) symbols. Another
way of seeing this is by reading about Lawrence Barsalou's 
"perceptual symbol systems". Symbolic architectures could live
if one thinks about using symbols that maintain some properties
of the proximal sensory data captured by the agent. That will
allow the "scaffolding" of such symbols with no danger of
incurring in the problems reported by Harnad. And that also
means that the architecture must have some kind of "statistical
layer" capable of creating symbols (and fade to extinction 
inappropriate ones). This happens, for instance, with blind
humans, which are living examples of this possibility.

So now commenting on Waser's question, one may be able to build
a system that has "symbolic anchors" instead of real statistical
experience (the ones that are directly derived from sensory inputs).
However that doesn't preclude the use of statistical methods, latter 
in the architecture. This is because in order to *create* knowledge
(and it's all about self-creation, not of "external insertion"), it 
is imperative to use statistical (inductive) methods of some sort. 
In my way of seeing things, any architecture based solely on logical 
(deductive) grounds is doomed to fail.

Sergio Navega.
  

  ----- Original Message ----- 
  From: Mark Waser 
  To: agi@v2.listbox.com 
  Sent: Tuesday, June 12, 2007 9:33 AM
  Subject: Re: [agi] Symbol Grounding


  >> a question is whether a software program could tractably learn language 
without such associations, by relying solely on statistical associations within 
texts. 

  Isn't there an alternative (or middle ground) of starting the software 
program with a seed of initial structure and then letting it grow from there 
(rather than relying only on statistical associations -- which I believe will 
be intractable for quite some time).


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