On 01/04/2013 05:42 PM, Anastasios Tsiolakidis wrote:
On Fri, Jan 4, 2013 at 10:25 PM, Piaget Modeler
<[email protected] <mailto:[email protected]>> wrote:
http://www.marcpickett.com/RepLearn2013/
Right, they prefer automatically derived rather than hand-crafted
"learning". I just want to be on record again saying "it can't be done".
Sure, General Intelligence excels at finding structure in unstructured
data and enriching "fluid", continuous domains with rather quantized
concepts and superstructures. But for probabilistic and other reasons I
do not expect a blank slate to develop anthropomorphic concepts, ever.
AT
I tend to agree that "automatically derived" is not the way to go. The
knowledge is needed, but we ought to think in terms of "accumulated"
rather than derived.
... on a similar note...
I was pleased to see the way the organizer's phrased the problem -
"A human-level artificially intelligent agent must be able to represent
and reason about the world, at some level, in terms of high-level
concepts such as entities and relations. The problem of acquiring these
rich high-level representations, known as the knowledge acquisition
bottleneck, has long been an obstacle for achieving human-level AI. "
It fits well with my recent thoughts about "what is so hard..." In my
thinking, the "knowledge acquisition bottleneck" is the battle to fight.
I refer to the problem as the "Scheme of things" issue.
Before an intelligent unit is able to make choices about general things,
the unit will need to see the overall scheme of things. If there is an
option in consideration, how does it "fit" the scheme of things.
(My opinion )"Scheme of things" knowledge is not being accumulated, in
part, because we don't have a format for the data. We don't have the
format for the data because we don't have a "design" that says "this is
how the data will be used by components x, y, z." My emphasis is on
determining what I think xyz should be.
Stan
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AGI
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