They need certainty or confidence values, and a list of possibilities, not just 
a single outcome. Then reasoning can choose which interpretation(s) make the 
most sense in context. But for their purposes -- automated video logging & 
alerts -- this works fine. Once the work is done, attaching confidence vaues 
and multiple possibilities should be relatively minor.



-- Sent from my Palm Pre
On Oct 30, 2012 12:33 PM, Alan Grimes <[email protected]> wrote:

Aaron Hosford wrote:

> http://www.rec.ri.cmu.edu/about/news/11_01_minds.php



> This project's approach is to use 3D simulation to detect and classify

> behavior, and then generate symbolic information about the events that

> were observed. I'm encouraged to see someone doing work on this stage of

> cognition, as I see perception as the "missing link" that's stopping AGI

> from developing.



> I wonder, will a certain naysayer feel vindicated that someone else sees

> simulation as vital to intelligence (and is using it to solve precisely

> the problems he says it's needed to solve), or will he be annoyed that

> the ultimate form the information takes is symbolic, which is compatible

> with semantic nets or any number of other existing AGI approaches?



The approach you are describing is both a revolutionary advancement, 

along the lines of what I've been trying to advocate for the last 6-8 

months... (which is usually the case)...



But, at the same time, tying it to classic approaches is a crippling 

limitation because it robs the system of the types of introspection that 

are the hallmark of human cognition. Intelligence is kinda a 'turtles 

all the way down' kinda thing... The cortex is an organized network of 

basically one type of algorithm applied to itself again and again... So 

yeah, you would need to generalize your simulation layer so that it can 

work in abstract domains in addition to domains for which graphics 

programming techniques are already reasonably well developed.



-- 

E T F

N H E

D E D



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