Why is the infant *surprised*? And then *delighted*?
It has a thesis/conception that is contradicted. The conception is then
altered. And the new conception needs to be reinforced several times,
because it is so radical a change.
I can't be sure, but your probabilistic approach seems to be totally
after-the-event, looking backwards.
The infant is indeed a scientist posing theories, checking them against the
evidence, and then altering them.
There are no probabilities here. Your approach seems to assume that the
infant already knows exactly what he is looking for - he doesn't.
(I don't think I've encountered any AGI-er who doesn't approach
problemsolving from the narrow AI perspective - starting from the solution
backwards - that's not AGI. AGI is about solving problems when you don't
have a method of solution).
-----Original Message-----
From: Ben Goertzel
Sent: Saturday, April 06, 2013 12:49 PM
To: AGI
Subject: Re: Complexity of vision (was Re: [agi] Utilizing kickstarter.com?)
MIke,
How would your approach replicate the classic visual problem for infants
of:
where has the toy car gone? (when the parent puts it behind a box} - and
oh
look, it's still there, when the parent takes away the box ?
Not sure why you think that learning "object permanence" via experience
would be hard for any probabilistic learning system.... That is
pretty elementary
probabilistic reasoning, after the infant sees a few examples of
previously obscured
objects still being there...
The first idea that my recent musings suggest about looking at any scene -
is that the first thing a real world agent must look for in any scene, is
what is moving - because that is the greatest source of both potential
danger and interest.
Salience detection is a well known component of computer vision systems,
and ironically (in light of your negative view of probability theory) is
often
handled using information-theoretic methods (which involve logarithms
of probabilities..)
-- Ben
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