I think that generaliziation via lossless compression could more readily be a 
Requirement for an AGI.

Also I must agree with Matt that you cant have knowledge seperate from other 
knowledge, everything is intertwined, and that is the problem.
There is Nothing, that I know, that humans know that is not in terms of 
something else, that is one thing that adds to the complexity of the issue.  
It is very difficult to teach a computer something without it knowing ALL other 
things related to that, because then Some inference it tries to make will be 
wrong, regardless.
  But that means that an architecture for AI will have to have a method for 
finding these inconsistencies and correcting them with good effeciency.

James Ratcliff

Mark Waser <[EMAIL PROTECTED]> wrote:    DIV {  MARGIN: 0px }    >> 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. 
  
 Which I take to agree with my point.
  
 >> I also  don't believe Hutter's paper proved it to be a general trend (by 
 >> some reasonable  measure). 
  
 Again, which I take to be agreement.
  
 >> But I  wouldn't doubt it.

 Depending upon what you mean by compression, I  would strongly doubt it.  I 
believe that lossless compression is  emphatically *not* part of higher 
intelligence in most real-world conditions  and, in fact, that the gains 
provided by "losing" a lot of data makes a much  higher intelligence possible 
with the same limited resources than an  intelligence that is constrained by 
the requirement to not lose  data.
  
    ----- Original Message ----- 
   From:    Matt    Mahoney 
   To: agi@v2.listbox.com 
   Sent: Thursday, November 16, 2006 2:17    PM
   Subject: Re: [agi] A question on the    symbol-system hypothesis
   

   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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