AGI,
I don't recall having explained granularity on this blog before. My neglect
has caused
misunderstandings. So here it is.
When we describe our world, we arbitrarily select the *granularity* of our
description.
(A) "The buttler is the assasin. He was at the scene, and he has a motive."
That's a very coarse granularity, but it is already a causal set.
Still, we can already take action. We can arrest the buttler and find more
facts.
(B) "The crime took place at 7:30."
(C) "The buttler left the scene at 7:29."
And we can draw a conclusion:
(D) "The buttler was not at the scene at the time of the crime.
We have now a better description, a finer granularity.
But what does it mean to "draw a conclusion?"
It means to *bind* or *associate* B and C to generate D.
This is a logical step, and requires a mathematical logic operating on the
causal set.
The logic removes uncertainty from the causal set and creates structure.
The set {B, C} is uncertain. We know B and C, but we ask "so what?"
To reach D we have to remove the uncertainty.
Removing uncertainty is easy for our brains.
We don't even notice it happened because we are not aware of it happening.
It is unconscious, and our brains do it all the time.
You the reader can continue the excercise.
Every bit of information we acquire is followed by a "remove uncertainty"
step.
Our brains are constantly removing uncertainty.
AGI will never work unless we learn how to "remove uncertainty" on a
computer.
No adaptive system will work unless we learn how to "remove uncertainty" on
a computer.
Have courage, don't leave the uncertainty there.
I must have tired everyone by saying entropy all the time. But entropy is
just a measure of uncertainty. If someone is 6 feet tall, I must use "feet"
to say that.
Sergio
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AGI
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