> From: Matt Mahoney [mailto:matmaho...@yahoo.com]
>
> > How does consciousness fit into your compression
> > intelligence modeling?
>
> It doesn't. Why is consciousness important?
>
I was just prodding you on this. Many people on this list talk about the
requirements of consciousness for AGI a
On Fri, Dec 26, 2008 at 11:56 PM, Abram Demski wrote:
> That's not to say that I don't think some representations are
> fundamentally more useful than others-- for example, I know that some
> proofs are astronomically larger in 1st-order logic as compared to
> 2nd-order logic, even in domains wher
--- On Sat, 12/27/08, Matt Mahoney wrote:
> In my thesis, I proposed a vector space model where
> messages are routed in O(n) time over n nodes.
Oops, O(log n).
-- Matt Mahoney, matmaho...@yahoo.com
---
agi
Archives: https://www.listbox.com/member/arch
--- On Fri, 12/26/08, Philip Hunt wrote:
> > Humans are very good at predicting sequences of
> > symbols, e.g. the next word in a text stream.
>
> Why not have that as your problem domain, instead of text
> compression?
That's the same thing, isn't it?
> While you're at it you may want to chan
--- On Fri, 12/26/08, Ben Goertzel wrote:
> IMO the test is *too* generic ...
Hopefully this work will lead to general principles of learning and prediction
that could be combined with more specific techniques. For example, a common way
to compress text is to encode it with one symbol per wor
--- On Fri, 12/26/08, J. Andrew Rogers wrote:
> For example, there is no general indexing algorithm
> described in computer science.
Which was my thesis topic and is the basis of my AGI design.
http://www.mattmahoney.net/agi2.html
(I wanted to do my dissertation on AI/compression, but funding i
--- On Fri, 12/26/08, John G. Rose wrote:
> Human memory storage may be lossy compression and recall may be
> decompression. Some very rare individuals remember every
> day of their life
> in vivid detail, not sure what that means in terms of
> memory storage.
Human perception is a form of lossy
I wrote down my thoughts on this in a little more detail here (with some
pastings from these emails plus some new info):
http://multiverseaccordingtoben.blogspot.com/2008/12/subtle-structure-of-physical-world.html
On Sat, Dec 27, 2008 at 12:23 AM, Ben Goertzel wrote:
>
>
>> Suppose I take the u
Steve,
When I made the statement about Fourier I was thinking of JPEG
encoding. A little digging found this book, which presents a unified
approach to (low-level) computer vision based on the Fourier
transform:
http://books.google.com/books?id=1wJuTMbNT0MC&dq=fourier+vision&printsec=frontcover&so
> Much of AI and pretty much all of AGI is built on the proposition that we
> humans must code knowledge because the stupid machines can't efficiently
> learn it on their own, in short, that UNsupervised learning is difficult.
>
No, in fact almost **no** AGI is based on this proposition.
Cyc is b
>
> Suppose I take the universal prior and condition it on some real-world
> training data. For example, if you're interested in real-world
> vision, take 1000 frames of real video, and then the proposed
> probability distribution is the portion of the universal prior that
> explains the real vide
From: "Ben Goertzel"
>I think the environments existing in the real physical and social world are
>drawn from a pretty specific probability distribution (compared to say, the
>universal prior), and that for this reason, looking at problems of
>compression or pattern recognition across general prog
On Fri, Dec 26, 2008 at 8:31 PM, J. Andrew Rogers
wrote:
> Never mind discovering "a small number of clever algorithms" for AI, we
> have not even discovered a great many basic algorithms for routine computer
> science.
> For example, there is no general indexing algorithm described in computer
>
2008/12/27 Ben Goertzel :
>
> And this is why we should be working on AGI systems that interact with the
> real physical and social world, or the most accurate simulations of it we
> can build.
Or some other domain that may have some practical use, e.g.
understanding program source code.
--
Phil
2008/12/27 J. Andrew Rogers :
>
> I think many people greatly underestimate how many gaping algorithm holes
> there are in computer science for even the most important and mundane tasks.
> The algorithm coverage of computer science is woefully incomplete,
Is it? In all my time as a programmer, it'
2008/12/26 Matt Mahoney :
>
> Humans are very good at predicting sequences of symbols, e.g. the next word
> in a text stream.
Why not have that as your problem domain, instead of text compression?
>
> Most compression tests are like defining intelligence as the ability to catch
> mice. They mea
> Most compression tests are like defining intelligence as the ability to
> catch mice. They measure the ability of compressors to compress specific
> files. This tends to lead to hacks that are tuned to the benchmarks. For the
> generic intelligence test, all you know about the source is that it h
On Dec 26, 2008, at 2:17 PM, Philip Hunt wrote:
I'm not dismissive of it either -- once you have algorithms that can
be practically realised, then it's possible for progress to be made.
But I don't think that a small number of clever algorithms will in
itself create intelligence -- if that was
> From: Matt Mahoney [mailto:matmaho...@yahoo.com]
>
> --- On Fri, 12/26/08, Philip Hunt wrote:
>
> > Humans aren't particularly good at compressing data. Does this mean
> > humans aren't intelligent, or is it a poor definition of
> intelligence?
>
> Humans are very good at predicting sequences
Steve,
It is strange to claim that prior PhDs will be worthless when what you
are suggesting is that we apply the standard methods to a different
representation. But that is beside the present point. :)
Taking the derivative, or just finite differences, is a useful step in
more ways then one. You
--- On Fri, 12/26/08, Philip Hunt wrote:
> Humans aren't particularly good at compressing data. Does this mean
> humans aren't intelligent, or is it a poor definition of intelligence?
Humans are very good at predicting sequences of symbols, e.g. the next word in
a text stream. However, humans a
2008/12/26 Ben Goertzel :
>
> 3)
> There are theorems stating that if you have a great compressor, then by
> wrapping a little code around it, you can get a system that will be highly
> intelligent according to the algorithmic info. definition. The catch is
> that this system (as constructed in th
Richard,
Since you are clearly in the mode you routinely get into when you start
loosing an argument on this list --- as has happened so many times before
--- i.e., of ceasing all further productive communication on the actual
subject of the argument --- this will be my last communication with
I'll try to answer this one...
1)
In a nutshell, the algorithmic info. definition of intelligence is like
this: Intelligence is the ability of a system to achieve a goal that is
randomly selected from the space of all computable goals, according to some
defined probability distribution on computab
Philip Hunt wrote:
2008/12/26 Matt Mahoney :
I have updated my universal intelligence test with benchmarks on about 100
compression programs.
Humans aren't particularly good at compressing data. Does this mean
humans aren't intelligent, or is it a poor definition of intelligence?
Although m
2008/12/26 Matt Mahoney :
> I have updated my universal intelligence test with benchmarks on about 100
> compression programs.
Humans aren't particularly good at compressing data. Does this mean
humans aren't intelligent, or is it a poor definition of intelligence?
> Although my goal was to samp
Abram,
On 12/26/08, Abram Demski wrote:
> Steve,
>
> Richard is right when he says temporal simultaneity is not a
> sufficient principle.
... and I fully agree. However, we must unfold this thing one piece at a
time.
Without the dp/dt "trick", there doesn't seem to be any way to make
unsuperv
I have updated my universal intelligence test with benchmarks on about 100
compression programs.
http://cs.fit.edu/~mmahoney/compression/uiq/
The results seem to show good correlation with real data. The best compressors
on this synthetic data are also the best on most benchmarks with real data
Steve,
Richard is right when he says temporal simultaneity is not a
sufficient principle. Suppose you present your system with the
following sequences (letters could be substituted for sounds, colors,
objects, whatever):
ABCABCABCABC...
AAABBBAAABBB...
ABBAAAABB...
ABBCCCEF
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