I can understand that, for example, a computer simulation of a storm is
not a storm, because only a storm is a storm and will get you wet. But
perhaps counterintuitively, a model of a brain can be closer to the real
thing than a model of a storm. We don't normally see inside a person's
head, we just observe his behaviour. There could be anything in there - a
brain, a computer, the Wizard of Oz - and as long as it pulled the
person's strings so that he behaved like any other person, up to and
including doing scientific research, we would never know the difference.
Now, we know that living brains can pull the strings to produce normal
human behaviour (and consciousness in the process, but let's look at the
external behaviour for now). We also know that brains follow the laws of
physics: chemistry, Maxwell's equations, and so on. Maybe we don't
*understand* electrical fields in the sense that it may feel like
something to be an electrical field, or in some other as yet unspecified
sense, but we understand them well enough to predict their physical effect
on matter. Hence, although it would be an enormous task to gather the
relevant information and crunch the numbers in real time, it should be
possible to predict the electrical impulses that come out of the skull to
travel down the spinal cord and cranial nerves and ultimately pull the
strings that make a person behave like a person. If we can do that, it
should be possible to place the machinery which does the predicting inside
the skull interfaced with the periphery so as to take the brain's place,
and no-one would know the difference because it would behave just like the
At which step above have I made a mistake?
I'd say it's here...
"and no-one would know the difference because it would behave just like
But for a subtle reason.
The artefact has to be able to cope with exquisite novelty like we do.
Models cannot do this because as a designer you have been forced to define
a model that constrains all possible novelty to be that which fits your
model for _learning_. Therein lies the fundamental flaw. Yes... at a given
level of knowledge you can define how to learn new things within the
knowledge framework. But when it comes to something exquisitely novel, all
that will happen is that it'll be interpreted into the parameters of how
you told it to learn things... this will impact in a way the artefact
cannot handle. It will behave differently and probably poorly.
It's the zombie thing all over again.
It's not _knowledge_ that matters. it's _learning_ new knowledge. That's
what functionalism fails to handle. Being grounded in a phenomenal
representation of the world outside is the only way to handle arbitrary
levels of novelty. No phenomenal representation? = You are "model-bound"
and grounded, in effect, in the phenomenal representation of your
model-builders, who are forced to predefine all novelty handling in an "I
don't know that" functional module. Something you cannot do without
knowing everything a-priori! If you already know that you are god so why
are you bothering?
Say you bring an artefact X into existence. X may behave exactly like a
human Y in all the problem domains you used to define you model. Then you
expose both to novelty nobody has seen, including you.... and that is
where the two will differ. The human Y will do better every time. You
can't program qualia. You have to have them and you can't do without them
in a 'general intelligence' context.
Here I am on a sat morning...proving I have no life, yet again! :-)
You received this message because you are subscribed to the Google Groups
"Everything List" group.
To post to this group, send email to firstname.lastname@example.org
To unsubscribe from this group, send email to [EMAIL PROTECTED]
For more options, visit this group at