On Wed, Apr 30, 2008 at 7:41 PM, Matt Mahoney <[EMAIL PROTECTED]> wrote:
>  "Understanding" can be as simple as matching terms in two documents, or
>  something more complex, such as matching a video clip to a text or
>  audio description.  However, there is an incentive to develop
>  sophisticated solutions (e.g. distinguish TV programming from
>  commercials).  This is the S part of the problem, essentially a
>  hierarchical adaptive pattern recognition problem that could be
>  implemented as a neural network or something similar on each peer.  For
>  language, the pattern hierarchy is letters -> words -> semantic
>  categories -> grammatical structures.  The task is divided by pattern.
>  A peer whose expertise is recognizing when a picture contains an animal
>  could route the message to peers that recognize cats or dogs.  I
>  believe that extremely narrow domains are practical in a network with
>  billions of peers.
>
>  The D part is "old school" AI, calculators, databases, theorem provers,
>  programs that play chess, etc.  Interfacing these to natural language
>  is a job for the S peers, matching the most common expressions to their
>  formal equivalents.  This is not a hard problem in narrow domains.

So my distinction between S-first and D-first isn't particularly
relevant to you, because you're not proposing a monolithic AGI; you're
instead proposing a community or marketplace of narrow AI modules
(some S-oriented, some D-oriented), that will hopefully constitute a
sort of loosely bound collective intelligence. Would that be an
accurate paraphrase of your view?

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agi
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