>Legg proved
there is no such thing as a simple, universal learner. So we can stop looking
for one.


With all due
respect to everyone involved this kind of comprehensive sweeping statement is
both narrow minded and counter productive.


>Suppose you
have a simple learner that can predict any computable sequence of symbols with
some probability at least as good as random guessing. Then I can create a
simple sequence that your predictor will get wrong 100% of the time. My
program runs a copy of your program and outputs something different from your 
guess.


This is
nonsensical, especially with regard to AGI, just the temporal aspect of reality
invalidates the premise within any meaningful context.  Predication is not a 
learned quality, it’s a
natural phenomena generated by physical fields/ laws. (Pressure/ inertia/
gravity/ time/ etc)


Take the bow/
pressure wave of a ship as an analogy. The bow wave is an accurate prediction 
generated
by the medium (water) of where the ship will be in the future.  The properties 
of the bow wave are also affected
by the state of the water, waves or wakes from other passing ships. In the big
scheme of things this predictive schema is a very simple system yet it’s 
impossible
to recreate the exact sequence or indeed recreate the qualities of the
predication post happening.


This video
shows a similar effect in a neural schema, the resultant pressure wave or
inertia of the GTP thought pattern is generated by the preceding thought 
patterns, they
are unique to this experience and would have totally different properties post
happening… your program can’t run a copy and create a predictor because the
prediction was partially temporally based… and the time has passed. 


https://www.youtube.com/watch?v=I1Dyj5hgvtc


Language and mathematics
are constructs created by an intelligent system; they are not an insight into
how the intelligent system functions.


 :)



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