Mark Waser wrote:
>Are you conceding that you can predict the results of a Google 
search?


OK, you are right.  You can type the same query twice.  Or if you live long 
enough you can do it the hard way.  But you won't.

>Are you now conceding that it is not true that "Models that are simple  enough 
>to debug are too simple to scale."?


OK, you are right again.  Plain text is a simple way to represent knowledge.  I 
can search and edit terabytes of it.

But this is not the point I wanted to make.  I am sure I expressed it badly.  
The point is there are two parts to AGI, a learning algorithm and a knowledge 
base.  The learning algorithm has low complexity.  You can debug it, meaning 
you can examine the internals to test it and verify it is working the way you 
want.  The knowledge base has high complexity.  You can't debug it.  You can 
examine it and edit it but you can't verify its correctness.

An AGI with a correct learning algorithm might still behave badly.  You can't 
examine the knowledge base to find out why.  You can't manipulate the knowledge 
base data to fix it.  At least you can't do these things any better than 
manipulating the inputs and observing the outputs.  The reason is that the 
knowledge base is too complex.  In theory you could do these things if you 
lived long enough, but you won't.  For practical purposes, the AGI knowledge 
base is a black box.  You need to design your goals, learning algorithm, data 
set and test program with this in mind.  Trying to build transparency into the 
data structure would be pointless.  Information theory forbids it.  Opacity is 
not advantagous or desirable.  It is just unavoidable.

I am sure I won't convince you, so maybe you have a different explanation why 
50 years of building structured knowledge bases has not worked, and what you 
think can be done about it?

And Google DOES keep the searchable part of the Internet in memory
http://blog.topix.net/archives/000011.html

because they have enough hardware to do it.
http://en.wikipedia.org/wiki/Supercomputer#Quasi-supercomputing

-- Matt Mahoney, [EMAIL PROTECTED]





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