A nice story but it proves absolutely nothing . . . . .
You developed a pattern-matcher. The pattern matcher worked (and I would
dispute that it worked better "than it had a right to"). Clearly, you do
not understand how it worked. So what does that prove?
Your contention (or, at least, the only one that continues the previous
thread) seems to be that you are too stupid to ever understand the pattern
that it found.
Let me offer you several alternatives:
1) You missed something obvious
2) You would have understood it if the system could have explained it to
you
3) You would have understood it if the system had managed to losslessly
convert it into a more compact (and comprehensible) format
4) You would have understood it if the system had managed to losslessly
convert it into a more compact (and comprehensible) format and explained it
to your
5) You would have understood it if the system had managed to lossily
convert it into a more compact (and comprehensible -- and probably even,
more correct) format
6) You would have understood it if the system had managed to lossily
convert it into a more compact (and comprehensible -- and probably even,
more correct) format and explained it to you
My contention is that the pattern that it found was simply not translated
into terms you could understand and/or explained.
Further, and more importantly, the pattern matcher *doesn't* understand it's
results either and certainly could build upon them -- thus, it *fails* the
test as far as being the central component of an RSIAI or being able to
provide evidence as to the required behavior of such.
----- Original Message -----
From: "Philip Goetz" <[EMAIL PROTECTED]>
To: <agi@v2.listbox.com>
Sent: Friday, December 01, 2006 7:02 PM
Subject: Re: [agi] A question on the symbol-system hypothesis
On 11/30/06, Mark Waser <[EMAIL PROTECTED]> wrote:
With many SVD systems, however, the representation is more
vector-like
and *not* conducive to easy translation to human terms. I have two
answers
to these cases. Answer 1 is that it is still easy for a human to look at
the closest matches to a particular word pair and figure out what they
have
in common.
I developed an intrusion-detection system for detecting brand new
attacks on computer systems. It takes TCP connections, and produces
100-500 statistics on each connection. It takes thousands of
connections, and runs these statistics thru PCA to come up with 5
dimensions. Then it clusters each connection, and comes up with 1-3
clusters per port that have a lot of connections and are declared to
be "normal" traffic. Those connections that lie far from any of those
clusters are identified as possible intrusions.
The system worked much better than I expected it to, or than it had a
right to. I went back and, by hand, tried to figure out how it was
classifying attacks. In most cases, my conclusion was that there was
*no information available* to tell whether a connection was an attack,
because the only information to tell that a connection was an attack
was in the TCP packet contents, while my system looked only at packet
headers. And yet, the system succeeded in placing about 50% of all
attacks in the top 1% of suspicious connections. To this day, I don't
know how it did it.
-----
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
