Mark wrote:
I'm glad we finally agree :-)
You group the strings into a fixed set and a variable set and concatenate them. The variable set could be just "I only used red and yellow paint in the painting", and you compare the CDM replacing "yellow" with "white". Of course your compressor must be capable of abstract reasoning and have a world model.
To answer Phil's post:
Text compression is only near the theoretical limts for small files. For large files, there is progress to be made integrating known syntactic and semantic modeling techniques into general purpose compressors. The theoretical limit is about 1 bpc and we are not there yet. See the graph at http://cs.fit.edu/~mmahoney/dissertation/
The proof that I gave that a language model implies passing the Turing test is for the ideal case where all people share identical models. The ideal case is deterministic. For the real case where models differ, passing the test is easier because a judge will attribute some machine errors to normal human variation. I discuss this in more detail at http://cs.fit.edu/~mmahoney/compression/rationale.html (text compression is equivalent to AI).
It is really hard to get funding for text compression research (or AI). I had to change my dissertation topic to network security in 1999 because my advisor had funding for that. As a postdoc I applied for a $50K NSF grant for a text compression contest. It was rejected, so I started one without funding (which we now have). The problem is that many people do not believe that text compression is related to AI (even though speech recognition researchers have been evaluating models by perplexity since the early 1990's).
-- Matt Mahoney,
[EMAIL PROTECTED]
>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 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 yellow", "Pink is created by mixing red and white". How is Keogh's
measure going to help you with that?
You group the strings into a fixed set and a variable set and concatenate them. The variable set could be just "I only used red and yellow paint in the painting", and you compare the CDM replacing "yellow" with "white". Of course your compressor must be capable of abstract reasoning and have a world model.
To answer Phil's post:
Text compression is only near the theoretical limts for small files. For large files, there is progress to be made integrating known syntactic and semantic modeling techniques into general purpose compressors. The theoretical limit is about 1 bpc and we are not there yet. See the graph at http://cs.fit.edu/~mmahoney/dissertation/
The proof that I gave that a language model implies passing the Turing test is for the ideal case where all people share identical models. The ideal case is deterministic. For the real case where models differ, passing the test is easier because a judge will attribute some machine errors to normal human variation. I discuss this in more detail at http://cs.fit.edu/~mmahoney/compression/rationale.html (text compression is equivalent to AI).
It is really hard to get funding for text compression research (or AI). I had to change my dissertation topic to network security in 1999 because my advisor had funding for that. As a postdoc I applied for a $50K NSF grant for a text compression contest. It was rejected, so I started one without funding (which we now have). The problem is that many people do not believe that text compression is related to AI (even though speech recognition researchers have been evaluating models by perplexity since the early 1990's).
----- Original Message ----
From: Mark Waser <[EMAIL PROTECTED]>
To: agi@v2.listbox.com
Sent: Tuesday, August 15, 2006 5:00:47 PM
Subject: Re: Mahoney/Sampo: [agi] Marcus Hutter's lossless compression of human knowledge prize
From: Mark Waser <[EMAIL PROTECTED]>
To: agi@v2.listbox.com
Sent: Tuesday, August 15, 2006 5:00:47 PM
Subject: Re: Mahoney/Sampo: [agi] Marcus Hutter's lossless compression of human knowledge prize
>> 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 yellow", "Pink is created by mixing red and white". How is Keogh's
measure going to help you with that?
The problem is that Keogh's measure is intended for
data-mining where you have separate instances, not one big entwined Gordian
knot.
>> Now if
only we had some test to tell which compressors have the best language
models...
Huh? By definition, the compressor with the best
language model is the one with the highest compression ratio.
----- Original Message -----From: Matt MahoneySent: Tuesday, August 15, 2006 3:54 PMSubject: Re: Mahoney/Sampo: [agi] Marcus Hutter's lossless compression of human knowledge prizeYou 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 if x and y do not share any information. Then,
CDM("it is hot", "it is very warm") < CDM("it is hot", "it is cold").
assuming your compressor uses a good language model.
Now if only we had some test to tell which compressors have the best language models...
-- Matt Mahoney, [EMAIL PROTECTED]
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