I think you're making a mistake.
I *do* feel that lots of special purpose AIs are needed as components of
an AGI, but those components don't summate to an AGI. The AGI also
needs a specialized connection structure to regulate interfaces to the
various special purpose AIs (which probably don't speak the same
language internally). It also needs a control structure which assigns
meanings to the results produced by the special purpose AIs and which
evaluates the current situation to either act directly (unusual) or
assign a task to a special purpose AI.
The analogy here is to a person using a spreadsheet. The spreadsheet
knows how to calculate quickly and accurately, but it doesn't know
whether you're forecasting the weather or doing your taxes. The meaning
adheres to a more central level.
Similarly, the AGI is comparatively clumsy when it must act directly.
(You *could* figure out each time how to add two numbers...but you'd
rather either remember the process or delegate it to a calculator.) But
the meaning is in the AGI. That meaning is what the AGI is about, and
has to do with a kind of global association network (which is why the
AGI is so slow at any specialized task).
Now in this context "meaning" means the utility of a result for
predicting some aspect of the probable future. (In this context the
present and past are only of significance as tools for predicting the
future.) Meaning is given emotional coloration by the effect that it's
contribution to the prediction has on the achievement of various of the
system's goals. (A system with only one goal would essentially not have
any emotions, merely decisions.)
Were it not for efficiency considerations the AGI wouldn't need any
narrow AIs. As a practical matter, however, figuring things out from
scratch is grossly inefficient, and so is dragging the entire context of
meanings through a specialized calculation...so these should get delegated.
Dennis Gorelik wrote:
Linas,
Some narrow AIs are more useful than other.
Voice recognition, image recognition, and navigation are less helpful
in building AGI than, say, expert systems and full text search
(Google).
AGI researcher my carefully pick narrow AIs in a such way, that narrow
AI steps would lead to development of full AGI system.
To be more direct: a common example of "narrow AI" are cruise missles, or the
darpa challange. We've put tens of millions into the darpa challange (which I
applaud)
but the result is maybe an inch down the road to AGI. Another narrow AI example
is data mining, and by now, many of the Fortune 500 have invested at least tens,
if not hundreds of millions of dollars into that .. yet we are hardly closer to
AGI as
a result (although this business does bring in billions for high-end expensive
computers from Sun, HP and IBM, andd so does encourage one component
needed for agi). But think about it ... billions are being spent on narrow AI
today,
and how did that help AGI, exactly?
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