Abram,
The key distinction here is probably that some approach to AGI may be widely
accepted as having great *promise*. That has certainly been the case,
although I doubt actually that it could happen again. There were also no
robots of note in the past. Personally, I can't see any approach being
accepted now - and the general responses of this forum, I think, support
this - until it actually delivers on some form of GI.
Mike,
There are at least 2 ways this can happen, I think. The first way is
that a mechanism is theoretically proven to be "complete", for some
less-than-sufficient formalism. The best example of this is one I
already mentioned: the neural nets of the nineties (specifically,
feedforward neural nets with multiple hidden layers). There is a
completeness result associated with these. I quote from
http://www.learnartificialneuralnetworks.com/backpropagation.html :
"Although backpropagation can be applied to networks with any number
of layers, just as for networks with binary units it has been shown
(Hornik, Stinchcombe, & White, 1989; Funahashi, 1989; Cybenko, 1989;
Hartman, Keeler, & Kowalski, 1990) that only one layer of hidden units
suces to approximate any function with finitely many discontinuities
to arbitrary precision, provided the activation functions of the
hidden units are non-linear (the universal approximation theorem). In
most applications a feed-forward network with a single layer of hidden
units is used with a sigmoid activation function for the units. "
This sort of thing could have contributed to the 50 years of
less-than-success you mentioned.
The second way this phenomenon could manifest is more a personal fear
than anything else. I am worried that there really might be partial
principles of mind that could seem to be able to do everything for a
time. The possibility is made concrete for me by analogies to several
smaller domains. In linguistics, the grammar that we are taught in
high school does almost everything. In logic, 1st-order systems do
almost everything. In sequence learning, hidden markov models do
almost everything. So, it is conceivable that some AGI method will be
missing something fundamental, yet seem for a time to be
all-encompassing.
On Mon, Aug 18, 2008 at 5:58 AM, Mike Tintner <[EMAIL PROTECTED]>
wrote:
Abram:I am worried-- worried that an AGI system based on anything less
than
the one most powerful logic will be able to fool AGI researchers for a
long time into thinking that it is capable of general intelligence.
Can you explain this to me? (I really am interested in understanding your
thinking). AGI's have a roughly 50 year record of total failure. They have
never shown the slightest sign of general intelligence - of being able to
cross domains. How do you think they will or could fool anyone?
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