> 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.
While the knowledge base is complex, I disagree with the way in which you're
attempting to use the first sentence. The knowledge base *isn't* so complex
that it causes a truly insoluble problem. The true problem is that the
knowledge base will have a large enough size and will grow and change quickly
enough that you can't maintain 100% control over the contents or even the
integrity of it.
I disagree with the second but believe that it may just be your semantics
because of the third sentence. The question is what we mean by "debug". If
you mean remove all incorrect knowledge, then the answer is obviously "yes, we
can't remove all incorrect knowledge" because odd sequences of observed events
and incomplete knowledge means that globally incorrect knowledge *is* the
correct deduction from experience. On the other hand, we certainly should be
able to debug how the knowledge base operates, make sure that it maintains an
acceptable degree of internal integrity, and responds correctly when it detects
a major integrity problem. The *process* and global behavior of the knowledge
base is what is important and it *can* be debugged. Minor mistakes and errors
are just the cost of being limited in an erratic world.
> An AGI with a correct learning algorithm might still behave badly.
No! An AGI with a correct learning algorithm may, through an odd sequence of
events and incomplete knowledge, come to an incorrect conclusion and take an
action that it would not have taken if it had perfect knowledge -- BUT -- this
is entirely correct behavior, not bad behavior. Calling it bad behavior
dramatically obscures what you are trying to do.
> You can't examine the knowledge base to find out why.
No, no, no, no, NO! If you (or the AI) can't go back through the causal chain
and explain exactly why an action was taken, then you have created an unsafe
AI. A given action depends upon a small part of the knowledge base (which may
then depend upon ever larger sections in an ongoing pyramid) and you can debug
an action and see what lead to an action (that you believe is incorrect but the
AI believes is correct).
> You can't manipulate the knowledge base data to fix it.
Bull. You should be able to correctly come across a piece of incorrect
knowledge that lead to an incorrect decision. You should be able to find the
supporting knowledge structures. If the knowledge is truly incorrect, you
should be able to provide evidence/experiences to the AI that leads it to
correct the incorrect knowledge (or, you could just even just tack the correct
knowledge in the knowledge base, fix it so that it temporarily can't be
altered, and run your integrity repair routines -- which, I contend, any AI
that is going to go anywhere must have).
> At least you can't do these things any better than manipulating the inputs
> and observing the outputs.
No. I can find structures in the knowledge base and alter them. I would
prefer not to. I would strongly prefer that it take the form of a conversation
where I "ask" the AGI what it's reasoning was, where it answers, where I point
out where I believe it's knowledge is incorrect and provide proof, and where it
can then alter its own knowledge base appropriately.
> 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.
No. I disagreed with your previous statement and I disagree with the reason.
The knowledge base is not that complex. It is that large. And the AI should
not be a black box *at all*. You should be able to examine any given piece to
any given level of detail at will -- you just can't hold all of it in mind at
once. Yes, that will lead to circumstances where it surprises you -- but we're
not looking for 100% predictability. We're looking for an intelligence with
*bounded* behavior.
> You need to design your goals, learning algorithm, data set and test program
> with this in mind.
Prove to me that the AGI knowledge base is a black box and I will. However,
you have already told me that I "can examine it and edit it" -- so what the
heck do *you* mean by a black box?
> Trying to build transparency into the data structure would be pointless.
> Information theory forbids it.
Bull, information theory does not forbid transparency into the data structure.
Prove this and you would invalidate a huge swath of AGI research. What makes
you say this? I believe that this is the core of your argument and would like
to see *any* sort of evidence/argument to support this grandiose claim.
> 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?
Hmmm. Let's see . . . . Codd's paper was published in 1970, so the first
fifteen years were devoted to getting to that point. And SQL wasn't even
commercially available until Oracle was released in 1979, so we're down to only
about half that time. Cyc didn't start until 1984 after Machine Learning
started in the early 1980s. Many people took horrible detours into neural
networks while a lot of the rest were forced to constrain their systems by the
limited computing ability available to them (I remember spending thousands of
dollars per month running biochemistry simulations on a Vax that I was able to
easily run on a PC less than five years later). In the past twenty years,
people have continued to make financially-proven progress in applications like
genome databases.
It looks to me like 50 years of building structured knowledge bases has worked
and that were getting better at it all the time and also that we can do more
and more as space and computing power is getting cheaper and cheaper and
languages and techniques are getting more and more powerful. What hasn't
worked *yet* is self-structuring databases and we're learning more all the time
. . . .
So *prove* to me why information theory forbids transparency of a knowledge
base.
Mark
P.S. Yes, yes, I've seen that Google article before where the author believes
that he "proves" that "Google is currently storing multiple copies of the
entire web in RAM." Numerous debunking articles have also come out including
the facts that Google does not store HTML code, that what is stored *is* stored
in compressed form, and --from Google, itself -- that it does *not* store
sections that do not include "new instances of significant terms". But I can
certainly understand your personal knowledge base deriving the "fact" that
"Google DOES keep the searchable part of the Internet in memory" if that
article is all that you've seen on the topic (though one would have hoped that
an integrity check or a reality check would have prompted further evaluation --
particularly since the article itself mentions that that would require an
unreasonably/impossibly large amount of RAM.)
----- Original Message -----
From: "Matt Mahoney" <[EMAIL PROTECTED]>
To: <agi@v2.listbox.com>
Sent: Wednesday, November 15, 2006 6:41 PM
Subject: Re: [agi] A question on the symbol-system hypothesis
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]
-----
This list is sponsored by AGIRI: http://www.agiri.org/email
To unsubscribe or change your options, please go to:
http://v2.listbox.com/member/?list_id=303
-----
This list is sponsored by AGIRI: http://www.agiri.org/email
To unsubscribe or change your options, please go to:
http://v2.listbox.com/member/?list_id=303
