On 04/11/2017 10:00 PM, Nanograte Knowledge Technologies wrote:
The moment relationships of any functional value (associations), and
any framework of hierarchy (systems) can be established and tested
against all known (domain) knowledge, and even changed if the rules
driving such a hierarchy should change (adapted), it may be regarded
as a concrete version of a probabilistic framework.
Okay, but this begs the question of how you define AGI. Domain
knowledge is the distinguishing point of what might be called regular
AI. It is the General part of AGI that doesn't allow a domain intense
approach.
Is it accepted that the "general" indicates that we are looking across
domains into the realm of all domains? And, we have to choose between
actions coming from multiple domains. One might call this
"meta-domain" knowledge. Such knowledge, I believe, would require
abstraction. That is the abstraction problem of A*G*I. For example, is
health a more significant domain than finance? Is public service better
for the AGI than bettering the skills of the AGI? Choices, choices,
choices...
To contend: Probability may not be a "good" basis for AGI, similarly
as love may not be a good basis for marriage, but what might just be a
"good" basis is a reliable engine (reasoning and unreasoning
computational framework) for managing relativity with. This is where
philosophy started from, unraveling a reasoning ontology.
I don't think probability is a problem. A piece of knowledge may
increase the chance that we see the situation accurately, and accuracy
will help us be more specific about our response. That said, it is the
way we put assumptions together that will determine our final action.
Probability has been used in that we think our assumptions are
"probably" right. It is the qualifying of our assumptions that
distinguishes the quality of our actions. Adopt sloppy assumptions and
your results will probably not always be appropriate or best - not super
intelligent.
An "advanced" system will have some mechanism for adopting assumptions
(most currently rely on the judgment of the programmer.) It is this
process of evaluating assumptions that we tend to get abstract. Since
we are calling these "heuristics" assumptions, there is an implication
that we can't prove this premise that we are adopting. Most likely we
can't prove because the premise we choose to build on is abstract - at
least has elements of abstraction that won't allow a clear logical
conclusion.
The issue is, where will AGI get the assumptions? And, how rigorous
will the process be for accepting a new assumption?
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