That goes back to my previous point on the amount and type of information
our brain is able to extract from a visual input. It would be truly
difficult I say, even using advanced types of neural nets, to give a set of
examples of chairs, such as the ones Mike linked to, and let the machine
recognize any subsequent object as chiar, given *only* the visual stimuli.

That's why I think it's amazing what kind of info we extract from visual
input, in fact it is anything but visual. Suppose for example that you
wanted to take pictures of the concept 'force'. When we see a force we can
recognize one, and the concept 'force' is very clearly defined.. i.e.
something is either a force or is not, there is no fuzzyiness in the
concept. But teaching a machine to recognize it visually.. well that's a
different story.

A practical example: before I learned rock-climbing I saw not only rocks,
but the space around me in a different way. Now just by looking at a room or
space I see all sorts of hooks, places to hang on that I would never have
thought of before.. I learned to 'read' the image differently, that is, to
extract different types of information from before.

In the same manner, my mother who is a Sommelier, by smelling wine can
extract all sorts of information about its provenience, the way it was made,
the kind of ingredients, that I could never think of (thus now I am shifting
from visual input to odor input). To me it just smells like wine :)

My point is that our brain *combines* visual or other stimuli with a bank of
*non-*visual data in order to extract relevant information. That's why
talking about AGI from a purely visual perspective (or purely verbal) takes
it out of context from the way we experience the world.

But you could prove me wrong by building a machine that using *only* visual
input can recognize forces :)



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agi
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