Stathis Papaioannou wrote:
On Fri, Feb 4, 2011 at 12:05 PM, Colin Hales
<> wrote:

Can the behaviour of the neurons including the electric fields be
simulated? For example, is it possible to model what will happen in
the brain (and what output will ultimately go to the muscles via
peripheral nerves) if a particular sequence of photons hits the
retina? If that is a theoretical impossibility then where exactly is
the non-computable physics, and what evidence do you have that it is

Lots of aspects to your questions.... and I'll try and answer Bruno at the
same time.

1) I am in the process of upgrading neural modelling to include the fields
in the traditional sense of simulation of the fields. The way to think of it
is that the little capacitor in the Hodgkin-Huxley equilvalent circuit is
about to get a whole new role.

Great! That is another step towards simulating brains.

2) Having done that, one can do simulations of single unit,  multiple unit,
populations etc etc...You may be able to extract something verifiable in the

3) However, I would hold that no matter how comprehensive the models, no
matter how many neurons ... even the whole brain and the peripheral
nerves...they will NOT behave like the real thing in the sense that such a
brain model cannot ever 'be' a mind. The reason is that we 'BE' the fields.
We do not 'BE' a description of the fields. The information delivered by
'BE'ing the field acts in addition to that described by the
3rd-person-validated system of classical partial differential equations that
are Maxwell's equations.

I understand that this is your position but I would like you to
consider a poor, dumb engineer who neither knows nor cares about
philosophy of mind. All he cares about is making an accurate model
which will predict the pattern of motor neuron firings for a human
brain given a certain initial state. Doing this is equivalent to
constructing a human level AI, since the simulation could be given
information and would respond just as a human would given the same
information. Now, I take it that you don't believe that such
predictions can be made using a mathematical model. Is that right?
I am also a poor dumb engineer (that has examined far too much philosophy of mind. Enough to be quite irritated by it :-). I started as an engineer with the 'black box' idea and eventually found enough evidence in human behaviour (specifically scientific behaviour) to doubt we can make an AGI that can do science like us when the black box is full of computer running software. I use the scientist as my target because its behaviour is testable. I conclude that I am more likely to succeed if the 'black box' includes more than mere software models of a brain in it.

I think perhaps the key to this can be seen in your requirement...

" Doing this is equivalent to constructing a human level AI, since the simulation 
could be given information and would respond just as a human would given the same 

I would say this is not a circumstance that exemplified human level intellect. Consider a human encounter with something totally unknown but human and AI. Who is there to provide 'information'? If the machine is like a human it shouldn't need someone there to spoon feed it answers. We let the AGI loose to encounter something neither human nor AGI has encountered before. That is a real AGI. The AGI can't "be given" the answers. You may be able to provide a software model of how to handle novelty. This requires a designers to say, in software, "everything you don't know is to be known like this ....". This, however, is not AGI (human). It is merely AI. It may suffice for a planetary rover with a roughly known domain of unknowns of a certain kind. But when it encounters a cylon that rips it widgets off it won't be able to characterize it like a human does. Such behaviour is not an instance of a human encounter with the unknown. Humans literally encounter the unknown in our qualia - an intracranial phenomenon. Qualia are the observation. We don't encounter the unknown in the single or collective behaviour of our peripheral nerve activity. Instead we get a unified assembly of perceptual fields erected intra-cranially from the peripheral feeds, within which the actual distal world is faithfully represented well enough to do science.
These perceptual fields are not always perfect. The perceptual fields can be 
fooled. You can perhaps say that a software-black-box-scientist could guess 
(Bayesian stabs in the dark). But those stabs in the dark are guesses at (a) 
how the peripheral siganlling measurement activity will behave, or perhaps (b) 
a guess at the contents of a human-model-derived software representation of the 
external world. Neither (a) or (b) can be guaranteed identical to the human 
qualia version of the the external distal world _in a situation of encounter 
with radical novelty (that a human AI designer has never encountered). This 
observational needs of a scientist are a rather useful way to cut through to 
the core of these issues.

The existence or nature of 'qualia' that give rise to (by grounding in observation) 
empirical laws, are not predicted by any of those empirical laws. However, the CONTENTS 
of qualia _are_ predicted. The system presupposes their existence in an observer. The 
only way to get qualia, then, is to do what a brain actually does, not what a model 
(empirical laws) of the brain does. Even if we 100% locate and model the atomic-level 
correlates of consciousness (qualia), that MODEL of correlates is NOT the thing that 
makes qualia. It's just a model of it. We are  not a model of a thing. We are, literally, 
something else, the actual natural world that merely appears to behave (in our qualia) 
like a model. In the case of the brilliant model "electromagnetism", our brains 
have electromagnetism predicted by the model. My PhD thesis is all about it. Humans get 
to BE whatever it is that behaves electromagnetically. Conflating these two things (BEing 
and APPEARing) is the bottom line of the COMP is true belief.

Note that none of this discussion need even broach issue of the natural world 
AS computation (of or not of the kind of computation happening in a digital 
computer made of the natural world). This latter issue might be useful to 
understand the 'why' of qualia origins. But it changes nothing in a decision 
process leading to real AGI.

BTW my obsession with AGI originates in the practical need to make a 'generic 
AI making machine'. One AGI makes all domain specific AI.


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