Alan,
Welcome back! I am not inclined to guessing, but this time I am going to
make a guess about your possible concern. You think I will claim that no
spatial intelligence is needed to solve the peg problem. I will not.
In plan B, as outlined in my recent posting, inference in the human brain
is
not used. Instead, inference is installed and used on the computer.
This does not mean that inference "knows" everything. Inference works from
knowledge, just as the child does. The child acquires that knowledge by
learning from his "sensors", mostly vision and sensory-motor nerves in
this
case, and using his inference to derive meaning from the observation.
Eventually, he will "know" what a peg is, and how to recognize one, and
how
to know if it is square or round and match it with a hole, and how to
control his muscles to do all that. The inference does all that by finding
associations. The child does not need a programmer to do all that.
With the inference and the computer, it is the same. The robot will need a
camera and a mechanical arm with position sensors besides the inference.
But
it will NOT need a program. It will have to learn step by step, from its
sensors. Knowledge is still necessary, but it comes as input, not as
program.
In the case of the retina, the situation is a little different. You may
have
seen my recent post about the blind climber who can see enough to climb a
mountain with a camera and electrodes attached to his togue. There is no
retina, no optical nerve, not even a vision-specific area of the brain
involved there. This confirms what I already knew from my experience with
causal sets. The anatomical details about the retina or the optical
nerves,
or left-right and upside down, are not needed at all. Not even as input.
We would be living in a fantasy world if we believed that anyone can
understand or explain or prove or guarantee all that. I sure can't.
Because
of that, I have proposed a practical approach. First, before even starting
anything, we need a computer with the inference installed on it. Second, a
simple model of a retina, just a camera with a few hundred pixels,
followed
with the inference. Show it an image, see what it does. Does it compress
the
image as the retina does? By how much? Compare with the real retina. If a
match is found, bingo! I am sure it will.
The wheels I am trying to set in motion even for this super-simplified
test
are so heavy that the pegs don't even appear in the picture. Yet.
Sergio
But please stop diagnosing me. I don't mind myself if you do, but not in
AGI. In AGI, even if I disappeared, the inference would still be there.
Sergio and inference are not the same.
-----Original Message-----
From: Alan Grimes [mailto:[email protected]]
Sent: Friday, July 13, 2012 10:03 AM
To: AGI
Subject: Re: [agi] Emergent "Inference"?
Having diagnosed Sergio's crackpotitis, I have made a point of not talking
to him. =P
But here's a question:
Lets say you had a kiddie's play room. In the room you had toys. One of
the
toys consisted of a set of pegs and a board with holes in it. The task is
to
find the square peg and put it in the correct hole. This requires a fair
amount of spatial intelligence. The peg must be identified, the hand must
be
directed to grasp it, rotate it, lift it into position using nothing but
muscle and visual feedback (trickier than it sounds due to the kinetics of
the arm). ; position it in the correct orientation over the correct hole
(which must also be identified), and then inserted.
Since you fancy yourself an AGI theorist, design a system that is capable
of
doing that.
--
E T F
N H E
D E D
Powers are not rights.
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