Jarrad,

(Ignoring Mike T for the moment and trying to have a serious
conversation ;)

> From what I've read - for the water example, say we took that photo
> and gave it to an AGI. Wouldn't PLN and some visual processing be able
> to infer (based on past experience) that all that blue is probably
> sky, theres multiple shapes that appear to be on a plane (inferring
> the plane is transparent, probably glass) - would bring up a bunch of
> nodes of things that could be related/attached to glass and sky one of
> them being water - from other experiences/knowledge they're would be
> networks that would branch out to water being particles/h20/fluid
> dynamics etc etc (should probably cut off the algorithm before this
> point).. and therefore all those distinctly different shapes are
> probably some kind of fluid, probably water (unless in the context
> there is a higher probability of it being, i dunno, urine?)

I believe that "PLN and some visual processing" could do this, yeah.

However, integrating OpenCog with a good vision processing system
is going to be a big job....

I wrote a conceptual paper for an AAAI symposium, on integrating
OpenCog with the DeSTIN vision system, titled "A Novel Strategy for
Hybridizing Subsymbolic and Symbolic Learning and Representation"

http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=10&ved=0CHUQFjAJ&url=http%3A%2F%2Fwww.aaai.org%2Focs%2Findex.php%2FFSS%2FFSS11%2Fpaper%2Fdownload%2F4217%2F4569&ei=pNvRT_eQA4uA6QHqwpCjAw&usg=AFQjCNHQMi4vZurInaIROQHsLBQw3H7T0A&sig2=fsGoblzB0peuNSTSEUtimw

[note, that paper just gives the concept, not any results yet...]

More recently, I wrote some more concrete thoughts about how to modify
DeSTIN to make this integration easier.

http://wiki.opencog.org/w/DestinOpenCog

All this is IMO do-able, but not trivial....  If you want to help, let
us know ;)

-- Ben


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