>>>
Absolutely.  We are not aware of most of our assumptions that are based in 
our common heritage, culture, and embodiment.  But an external observer 
could easily notice them and tease out an awful lot of information about us 
by doing so.
<<<<

You do not understand what I mean.
There will be lot of implementation details (e.g. temporary variables )
within the patterns which will never be send by linguistic messages.

>>>
I disagree with a complete distinction between D and L.  L is a very small
fraction of D translated for transmission.  However, instead of arguing that
there must be a strict separation between language model and D, I would
argue that the more similar the two could be (i.e. the less translation from
D to L) the better.  Analyzing L in that case could tell you more about D
than you might think (which is what Pinker and Hauser argue).  It's like
looking at data to determine an underlying cause for a phenomenon.  Even
noticing what does and does not vary (and what covaries) tells you a lot
about the underlying cause (D).
<<<

This is just an assumption of you. No facts. My opinion remains: D and L are
separated.


>>>
How do you go from a formal language to a competent description of a messy,
ambiguous, data-deficient world?  *That* is the natural language question.
<<<

Any algorithm in your computer is written in a formal well defined language.
If you agree that AGI is possible with current programming languages then
you have to agree that the ambiguous, data-deficient world can be managed by
formal languages.


>>>
What happens if I say that language extensibility is exactly analogous to
learning which is exactly analogous to internal model improvement?
<<<

What happens? I disagree.

>>>
So translation is a pattern manipulation where the result isn't stored?
<<<

The result isn't stored in D

>>>
The domain of mathematics is complete and unambiguous.  A mathematics AI is
not a GI in my book.  It won't generalize to the real world until it handles
incompleteness and ambiguity (which is my objection to your main analogy).
<<<

If you say mathematics is not GI then the following must be true for you:
The universe cannot be modeled by mathematics.
I disagree.

>>>
The communication protocol needs to be extensible to handle output after
learning or transition into a new domain.  How do you ground new concepts?
More importantly, it needs to be extensible to support teaching the AGI.  As
I keep saying, how are you going to make your communication protocol
extensible?  Real GENERAL intelligence has EVERYTHING to do with
extensibility.
<<<

For mathematics you just need a few axioms. There are an infinite number of
expressions which can be written with a final set of symbols and a finite
formal language.

But extensibility is no crucial point in this discussion at all. You can
have extensibility with a strict separation of D and L. For first AGI with
mathematics I would hardcode an algorithm which manages an open list of
axioms and definitions as a language interface.

>>>
I keep pointing out that your model separating communication and database
updating depends upon a fully specified model and does not tolerate
ambiguity (i.e. it lacks extensibility and doesn't handle ambiguity).  You
continue not to answer these points.
<<<

Once again:
The separation of communication and database updating does not contradict
extensibility and ambiguity. Language data and domain data can be strictly
separated.
I can update the language database without communicating (e.g. just by
changing the hard disk) or with communicating. The main point is that the
model *D* needs not to be changed during communicating. Furthermore,
language extension would be a nice feature but it is not necessary.

The model D needs not to be fully specified at all.
If the model L is formal and without ambiguities this does not imply at all
that problems with ambiguities cannot be handled.













-------------------------------------------
agi
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