Hi Ben,
I agree with everything that you're saying; however, looking at the
specific task:
Create a compressed version (self-extracting archive) of the 100MB file
enwik8 of less than 18MB. More precisely:
a.. Create a Linux or Windows executable archive8.exe of size S L :=
18'324'887 =
Yes, but the compression software could have learned stuff before
trying the Hutter Challenge, via compressing a bunch of other files
... and storing the knowledge it learned via this experience in its
long-term memory...
-- Ben
On 8/15/06, Mark Waser [EMAIL PROTECTED] wrote:
Hi Ben,
I
I
don't see any point in this debate over lossless vs. lossy
compression
Lets see if I can simplify it.
The stated goal is compressing human
knowledge.
The exact, same knowledge can always be expressed
in a *VERY*large number of different bit strings
Not being able to reproduce
I think that our difference is that I am interpreting without input
from other sources as not allowing that bunch of other files UNLESS that
long-term memory is counted as part of the executable size.
- Original Message -
From: Ben Goertzel [EMAIL PROTECTED]
To: agi@v2.listbox.com
On Tuesday 15 August 2006 09:03, Ben Goertzel wrote:
Yes, but the compression software could have learned stuff before
trying the Hutter Challenge, via compressing a bunch of other files
... and storing the knowledge it learned via this experience in its
long-term memory...
This could have a
I've read Chaniak's book, Statistical Language Learning. A lot of researchers
in language modeling are using perplexity (compression ratio) to compare
models. But there are some problems with the way this is done.
1. Many evaluations are done on corpora from the LDC which are not free, like
A further example is:
S1 = The fall of the Roman empire is due to Christianity.
S2 = The fall of the Roman empire is due to lead poisoning.
I'm not sure whether S1 or S2 is more true. But the question is how can
you define the meaning of the NTV associated with S1 or S2? If we can't,
why
I realize it is tempting to use lossy text compression as a test for AI because that is what the human brain does when we read text and recall it in paraphrased fashion. We remember the ideas and discard details about the _expression_ of those ideas. A lossy text compressor that did the same thing
1. The
test is subjective.
I disagree. If you have an automated test
with clear criteria like the following, it will be completely
objective:
a)the compressing program must be able to output all
inconsistencies in the corpus (in their original string form)AND
b)the decompressing program
On 8/15/06, Ben Goertzel [EMAIL PROTECTED] wrote:
Phil, I see no conceptual problems with using probability theory to
define context-dependent or viewpoint-dependent probabilities...
Regarding YKY's example, causation is a subtle concept going beyond
probability (but strongly probabilistically
On 8/15/06, Mark Waser [EMAIL PROTECTED] wrote:
Ben Conceptually, a better (though still deeply flawed) contest would be:
Compress this file of advanced knowledge, assuming as background
knowledge this other file of elementary knowledge, in terms of which
the advanced knowledge is defined.
On 8/15/06, Matt Mahoney [EMAIL PROTECTED] wrote:
Ben wrote:
Conceptually, a better (though still deeply flawed) contest would be:
Compress this file of advanced knowledge, assuming as background
knowledge this other file of elementary knowledge, in terms of which
the advanced knowledge is
How about using OpenCyc?
Actually, instructing the competitors to compress both the OpenCyc corpus
AND then the Wikipedia sample in sequence and measuring the size of both
*would* be an interesting and probably good contest.
- Original Message -
From: Philip Goetz [EMAIL PROTECTED]
On 8/15/06, Mark Waser [EMAIL PROTECTED] wrote:
Actually, instructing the competitors to compress both the OpenCyc corpus
AND then the Wikipedia sample in sequence and measuring the size of both
*would* be an interesting and probably good contest.
I think it would be more interesting for it to
I proposed knowledge-based text compression as a dissertation topic,
back around 1991, but my advisor turned it down. I never got back to
the topic because there wasn't any money in it - text is already so
small, relative to audio and video, that it was clear that the money
was in audio and
You could use Keogh's compression dissimilarity measure to test for inconsistency.http://www.cs.ucr.edu/~eamonn/SIGKDD_2004_long.pdf CDM(x,y) = C(xy)/(C(x)+C(y)).where x and y are strings, and C(x) means the compressed size of x (lossless). The measure ranges from about 0.5 if x = y to about 1.0
Hi,
Phil wrote:
There isn't a problem in doing it, but there's serious doubts whether
an approach in which symbols have constant meanings (the same symbol
has the same semantics in different propositions) can lead to AI.
Sure, but neither Novamente nor NARS (for example) has the problematic
I think it would be more interesting for it to use the OpenCyc corpus
as its knowledge for compressing the Wikipedia sample. The point is
to demonstrate intelligent use of information, not to get a wider
variety of data.
:-) My assumption is that the compression program is building/adding to
On 8/15/06, Matt Mahoney [EMAIL PROTECTED] wrote:
I realize it is tempting to use lossy text compression as a test for AI
because that is what the human brain does when we read text and recall it in
paraphrased fashion. We remember the ideas and discard details about the
expression of those
You
could use Keogh's compression dissimilarity measure to test for
inconsistency.
I don't think so. Take the following strings:
"I only used red and yellow paint in the painting", "I painted the rose in my
favorite color", "My favorite color is pink", "Orange is created by mixing red
and
Mark wrote:Huh? By definition, the compressor with the best
language model is the one with the highest compression ratio.
I'm glad we finally agree :-) You
could use Keogh's compression dissimilarity measure to test for
inconsistency.
I don't think so. Take the following strings:
"I only used
On 8/15/06, Mark Waser [EMAIL PROTECTED] wrote:
I think it would be more interesting for it to use the OpenCyc corpus
as its knowledge for compressing the Wikipedia sample. The point is
to demonstrate intelligent use of information, not to get a wider
variety of data.
:-) My assumption is
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