Thanks!

It's worthwhile being specific about levels of interpretation in the 
discussion of self-modification. I can write self-modifying assembly code 
that yet does not change the physical processor, or even its microcode it 
it's one of those old architectures. I can write a self-modifying Lisp 
program that doesn't change the assembly language interpreter that's running 
it. 

So it's certainly possible to push the self-modification up the interpretive 
abstraction ladder, to levels designed to handle it cleanly. But the basic 
point, I think, stands: there has to be some level that is both controlling 
the way the system does things, and gets modified.

I agree with you that there has been little genetic change in human brain 
structure since the paleolithic, but I would claim that culture *is* the 
software and it has been upgraded drastically. And I would agree that the 
vast bulk of human self-improvement has been at this software level, the 
level of learned representations.

If we want to improve our basic hardware, i.e. brains, we'll need to 
understand them well enough to do basic engineering on them -- a self-model. 
However, we didn't need that to build all the science and culture we have so 
far, a huge software self-improvement. That means to me that it is possible 
to abstract out the self-model until the part you need to understand and 
modify is some tractable kernel. For human culture that is the concept of 
science (and logic and evidence and so forth).

This means to me that it should be possible to structure an AGI so that it 
could be recursively self improving at a very abstract, highly interpreted 
level, and still have a huge amount to learn before it do anything about the 
next level down.

Regarding machine speed/capacity: yes, indeed. Horsepower is definitely going 
to be one of the enabling factors, over the next decade or two. But I don't 
think AM would get too much farther on a Blue Gene than on a PDP-10 -- I 
think it required hyper-exponential time for concepts of a given size.

Josh


On Wednesday 03 October 2007 12:44:20 pm, Edward W. Porter wrote:
> Josh,
> 
> Thank you for your reply, copied below.  It was – as have been many of
> your posts – thoughtful and helpful.
> 
> I did have a question about the following section
> 
> “THE LEARNING PROCESS MUST NOT ONLY IMPROVE THE WORLD MODEL AND WHATNOT,
> BUT MUST IMPROVE (=> MODIFY) *ITSELF*. KIND OF THE WAY CIVILIZATION HAS
> (MORE OR LESS) MOVED FROM RELIGION TO PHILOSOPHY TO SCIENCE AS THE
> METHODOLOGY OF CHOICE FOR ITS SAGES.”
> 
> “THAT, OF COURSE, IS SELF-MODIFYING CODE -- THE DARK PLACE IN A COMPUTER
> SCIENTIST'S SOUL WHERE ONLY THE KWISATZ HADERACH CAN LOOK.   :^)”
> 
> My question is: if a machine’s world model includes the system’s model of
> itself and its own learned mental representation and behavior patterns, is
> it not possible that modification of these learned representations and
> behaviors could be enough to provide what you are talking about -- without
> requiring modifying its code at some deeper level.
> 
> For example, it is commonly said that humans and their brains have changed
> very little in the last 30,000 years, that if a new born from that age
> were raised in our society, nobody would notice the difference.  Yet in
> the last 30,000 years the sophistication of mankind’s understanding of,
> and ability to manipulate, the world has grown exponentially.  There has
> been tremendous changes in code, at the level of learned representations
> and learned mental behaviors, such as advances in mathematics, science,
> and technology, but there has been very little, if any, significant
> changes in code at the level of inherited brain hardware and software.
> 
> Take for example mathematics and algebra.  These are learned mental
> representations and behaviors that let a human manage levels of complexity
> they could not otherwise even begin to.  But my belief is that when
> executing such behaviors or remembering such representations, the basic
> brain mechanisms involved – probability, importance, and temporal based
> inference; instantiating general patterns in a context appropriate way;
> context sensitive pattern-based memory access; learned patterns of
> sequential attention shifts, etc. -- are all virtually identical to ones
> used by our ancestors 30,000 years ago.
> 
> I think in the coming years there will be lots of changes in AGI code at a
> level corresponding to the human inherited brain level.  But once human
> level AGI has been created -- with what will obviously have to a learning
> capability as powerful, adaptive, exploratory, creative, and as capable of
> building upon its own advances at that of a human -- it is not clear to me
> it would require further changes at a level equivalent to the human
> inherited brain level to continue to operate and learn as well as a human,
> any more than have the tremendous advances of human civilization in the
> last 30,000 years.
> 
> Your implication that civilization had improved itself by moving “from
> religion to philosophy to science” seems to suggest that the level of
> improvement you say is needed might actually be at the level of learned
> representation, including learned representation of mental behaviors.
> 
> 
> 
> As a minor note, I would like to point out the following concerning your
> statement that:
> 
> “ALL AI LEARNING SYSTEMS TO DATE HAVE BEEN "WIND-UP TOYS" “
> 
> I think a lot of early AI learning systems, although clearly toys when
> compared with humans in many respects, have been amazingly powerful
> considering many of them ran on roughly fly-brain-level hardware.  As I
> have been saying for decades, I know which end is up in AI -- its
> computational horsepower. And it is coming fast.
> 
> 
> Edward W. Porter
> Porter & Associates
> 24 String Bridge S12
> Exeter, NH 03833
> (617) 494-1722
> Fax (617) 494-1822
> [EMAIL PROTECTED]

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