In the context of AIXI, intelligence is measured by an accumulated reward 
signal, and compression is defined by the size of a program (with respect to 
some fixed universal Turing machine) guessed by the agent that is consistent 
with the observed interaction with the environment.  I don't believe it is true 
that better compression implies higher intelligence (by these definitions) for 
every possible agent, environment, universal Turing machine and pair of guessed 
programs.  I also don't believe Hutter's paper proved it to be a general trend 
(by some reasonable measure).  But I wouldn't doubt it.
 
-- Matt Mahoney, [EMAIL PROTECTED]

----- Original Message ----
From: Mark Waser <[EMAIL PROTECTED]>
To: agi@v2.listbox.com
Sent: Thursday, November 16, 2006 12:18:46 PM
Subject: Re: [agi] A question on the symbol-system hypothesis



 
 


1. The fact that AIXI is intractable is not 
relevant to the proof that compression = intelligence, any more than the fact 
that AIXI is not computable.  In fact it is supporting because it says that 
both are hard problems, in agreement with observation.

 

Wrong.  Compression may (and, I might even be 
willing to admit, does) equal intelligence under the conditions of perfect and 
total knowledge.  It is my contention, however, that without those 
conditions that compression does not equal intelligence and AIXI does 
absolutely 
nothing to disprove my contention since it assumes (and requires) 
those conditions -- which emphatically do not exist.


2. Do not confuse the two compressions.  
AIXI proves that the optimal behavior of a goal seeking agent is to guess the 
shortest program consistent with its interaction with the environment so 
far.  This is lossless compression.  A typical implementation is to 
perform some pattern recognition on the inputs to identify features that are 
useful for prediction.  We sometimes call this "lossy compression" because 
we are discarding irrelevant data.  If we anthropomorphise the agent, then 
we say that we are replacing the input with perceptually indistinguishable 
data, 
which is what we typically do when we compress video or sound.

 

I haven't confused anything.  Under perfect 
conditions, and only under perfect conditions, does AIXI prove 
anything.  You don't have perfect conditions so AIXI proves absolutely 
nothing.


----- Original Message ----- 
From: "Matt Mahoney" <[EMAIL PROTECTED]>

To: <agi@v2.listbox.com>

Sent: Wednesday, November 15, 2006 7:20 
PM

Subject: Re: [agi] A question on the symbol-system 
hypothesis




1. The fact that AIXI^tl is intractable is not relevant to the proof that 
compression = intelligence, any more than the fact that AIXI is not 
computable.  In fact it is supporting because it says that both are hard 
problems, in agreement with observation.

2. Do not confuse the two 
compressions.  AIXI proves that the optimal behavior of a goal seeking 
agent is to guess the shortest program consistent with its interaction with the 
environment so far.  This is lossless compression.  A typical 
implementation is to perform some pattern recognition on the inputs to identify 
features that are useful for prediction.  We sometimes call this "lossy 
compression" because we are discarding irrelevant data.  If we 
anthropomorphise the agent, then we say that we are replacing the input with 
perceptually indistinguishable data, which is what we typically do when we 
compress video or sound.
 
-- Matt Mahoney, [EMAIL PROTECTED]

----- 
Original Message ----
From: Mark Waser <[EMAIL PROTECTED]>
To: 
agi@v2.listbox.com
Sent: Wednesday, 
November 15, 2006 3:48:37 PM
Subject: Re: [agi] A question on the 
symbol-system hypothesis

>> The connection between intelligence and 
compression is not obvious.

The connection between intelligence and 
compression *is* obvious -- but 
compression, particularly lossless 
compression, is clearly *NOT* 
intelligence.

Intelligence compresses 
knowledge to ever simpler rules because that is an 
effective way of dealing 
with the world.  Discarding ineffective/unnecessary 
knowledge to make 
way for more effective/necessary knowledge is an effective 
way of dealing 
with the world.  Blindly maintaining *all* knowledge at 
tremendous 
costs is *not* an effective way of dealing with the world (i.e. 
it is *not* 
intelligent).

>>1. What Hutter proved is that the optimal behavior 
of an agent is to guess 
>>that the environment is controlled by the 
shortest program that is 
>>consistent with all of the interaction 
observed so far.  The problem of 
>>finding this program known as 
AIXI.
>> 2. The general problem is not computable [11], although Hutter 
proved 
>> that if we assume time bounds t and space bounds l on the 
environment, 
>> then this restricted problem, known as AIXItl, can be 
solved in O(t2l) 
>> time

Very nice -- except that O(t2l) time 
is basically equivalent to incomputable 
for any real scenario.  
Hutter's proof is useless because it relies upon the 
assumption that you 
have adequate resources (i.e. time) to calculate AIXI --  
which you 
*clearly* do not.  And like any other proof, once you invalidate 
the 
assumptions, the proof becomes equally invalid.  Except as an 

interesting but unobtainable edge case, why do you believe that Hutter has 

any relevance at all?


----- Original Message ----- 
From: 
"Matt Mahoney" <[EMAIL PROTECTED]>
To: 
<agi@v2.listbox.com>
Sent: 
Wednesday, November 15, 2006 2:54 PM
Subject: Re: [agi] A question on the 
symbol-system hypothesis


Richard, what is your definition of 
"understanding"?  How would you test 
whether a person understands 
art?

Turing offered a behavioral test for intelligence.  My 
understanding of 
"understanding" is that it is something that requires 
intelligence.  The 
connection between intelligence and compression is 
not obvious.  I have 
summarized the arguments here.
http://cs.fit.edu/~mmahoney/compression/rationale.html

-- Matt Mahoney, [EMAIL PROTECTED]

----- 
Original Message ----
From: Richard Loosemore <[EMAIL PROTECTED]>
To: 
agi@v2.listbox.com
Sent: Wednesday, 
November 15, 2006 2:38:49 PM
Subject: Re: [agi] A question on the 
symbol-system hypothesis

Matt Mahoney wrote:
> Richard Loosemore 
<[EMAIL PROTECTED]> 
wrote:
>> "Understanding" 10^9 bits of information is not the same as 
storing 10^9
>> bits of information.
>
> That is 
true.  "Understanding" n bits is the same as compressing some 
> 
larger training set that has an algorithmic complexity of n bits.  Once 

> you have done this, you can use your probability model to make 
predictions 
> about unseen data generated by the same (unknown) Turing 
machine as the 
> training data.  The closer to n you can compress, 
the better your 
> predictions will be.
>
> I am not sure what 
it means to "understand" a painting, but let's say that 
> you understand 
art if you can identify the artists of paintings you 
> haven't seen 
before with better accuracy than random guessing.  The 
> relevant 
quantity of information is not the number of pixels and 
> resolution, 
which depend on the limits of the eye, but the (much smaller) 
> number of 
features that the high level perceptual centers of the brain are 
> 
capable of distinguishing and storing in memory.  (Experiments by Standing 

> and Landauer suggest it is a few bits per second for long term memory, 
the 
> same rate as language).  Then you guess the shortest program 
that 
> generates a list of feature-artist pairs consistent with your 
knowledge of 
> art and use it to predict artists given new 
features.
>
> My estimate of 10^9 bits for a language model is based 
on 4 lines of 
> evidence, one of which is the amount of language you 
process in a 
> lifetime.  This is a rough estimate of course.  
I estimate 1 GB (8 x 10^9 
> bits) compressed to 1 bpc (Shannon) and 
assume you remember a significant 
> fraction of 
that.

Matt,

So long as you keep redefining "understand" to mean 
whatever something
trivial (or at least, something different in different 
circumstances),
all you do is reinforce the point I was trying to 
make.

In your definition of "understanding" in the context of art, above, 
you
specifically choose an interpretation that enables you to pick 
a
particular bit rate.  But if I chose a different interpretation (and 
I
certainly would - an art historian would never say they understood 
a
painting just because they could tell the artist's style better than 
a
random guess!), I might come up with a different bit rate.  And if 
I
chose a sufficiently subtle concept of "understand", I would be 
unable
to come up with *any* bit rate, because that concept of 
"understand"
would not lend itself to any easy bit rate analysis.

The 
lesson?  Talking about bits and bit rates is completely pointless
.... 
which was my point.

You mainly identify the meaning of "understand" as a 
variant of the
meaning of "compress".  I completely reject this - this 
is the most
idiotic development in AI research since the early attempts to 
do
natural language translation using word-by-word lookup tables  
-  and I
challenge you to say why anyone could justify reducing the term 
in such
an extreme way.  Why have you thrown out the real meaning 
of
"understand" and substituted another meaning?  What have we gained 
by
dumbing the concept down?

As I said in previously, this is as crazy 
as redefining the complex
concept of "happiness" to be "a warm 
puppy".


Richard Loosemore



> Landauer, Tom (1986), 
“How much do people
> remember?  Some estimates of the 
quantity
> of learned information in long term memory”, Cognitive Science 
(10) pp. 
> 477-493
>
> Shannon, Cluade E. (1950), “Prediction 
and
> Entropy of Printed English”, Bell Sys. Tech. J (3) p. 
50-64.
>
> Standing, L. (1973), “Learning 10,000 Pictures”,
> 
Quarterly Journal of Experimental Psychology (25) pp. 
207-222.
>
>
>
> -- Matt Mahoney, [EMAIL PROTECTED]
>
> 
----- Original Message ----
> From: Richard Loosemore <[EMAIL PROTECTED]>
> To: 
agi@v2.listbox.com
> Sent: 
Wednesday, November 15, 2006 9:33:04 AM
> Subject: Re: [agi] A question on 
the symbol-system hypothesis
>
> Matt Mahoney wrote:
>> I 
will try to answer several posts here. I said that the knowledge
>> 
base of an AGI must be opaque because it has 10^9 bits of 
information,
>> which is more than a person can comprehend. By opaque, 
I mean that you
>> can't do any better by examining or modifying the 
internal
>> representation than you could by examining or modifying the 
training
>> data. For a text based AI with natural language ability, 
the 10^9 bits
>> of training data would be about a gigabyte of text, 
about 1000 books. Of
>> course you can sample it, add to it, edit it, 
search it, run various
>> tests on it, and so on. What you can't do is 
read, write, or know all of
>> it. There is no internal representation 
that you could convert it to
>> that would allow you to do these 
things, because you still have 10^9
>> bits of information. It is a 
limitation of the human brain that it can't
>> store more information 
than this.
>
> "Understanding" 10^9 bits of information is not the 
same as storing 10^9
> bits of information.
>
> A typical 
painting in the Louvre might be 1 meter on a side.  At roughly
> 16 
pixels per millimeter, and a perceivable color depth of about 20 bits
> 
that would be about 10^8 bits.  If an art specialist knew all 
about,
> say, 1000 paintings in the Louvre, that specialist would 
"understand" a
> total of about 10^11 bits.
>
> You might be 
inclined to say that not all of those bits count, that many
> are 
redundant to "understanding".
>
> Exactly.
>
> People 
can easily comprehend 10^9 bits.  It makes no sense to argue
> about 
degree of comprehension by quoting numbers of bits.
>
>
> 
Richard Loosemore
>
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