--- David Clark <[EMAIL PROTECTED]> wrote:
Turing's test is obviously not sufficient for AGI. Why would an AGI
waste
it's time learning to lie, miscompute numbers, simulate a forgetful
memory
etc, to pass a test? Why would the creators of an AGI spend time and
money
to create the worst aspects of being human?
I agree, these are all good reasons to use a different test.
I use a simple metaphor for *understanding*. If information was X/Y
pairs
of numbers, and they were plotted on a graph, the Y intercept and slope
of
the resulting line would be *understanding*.
Now you are talking about compression. Encode the X,Y points as the X
points
plus a function for computing Y.
> A common argument against compression as a test for AI is that humans
don't
> compress like a zip program. Compression requires a *deterministic*
model. A
> compressor codes string x using a code of length log 1/p(x) bits. The
> decompressor must also compute p(x) exactly to invert the code. Humans
can't
> do this because they use noisy neurons to compute p(x) that varies a
> bit
each
> time.
Any test that requires the AGI to jump through hoops that a human (or any
human) can't pass, is a poor test. The idea isn't to make the potential
AGI
fail but to recognize when something approximating human level
intelligence
is achieved. To make a test so hard as to fail, obviously intelligent
and
useful programs wouldn't have much value.
I don't think this is a hard hoop for a deterministic machine. The hard
part
is figuring out how to compute p(x). Once you can do this, computing it a
second time is trivial.
--- Andrew Babian <[EMAIL PROTECTED]> wrote:
It occurs to me the problem I'm having with this definition of AI as
compression. There are two different tasks here, recognition of
"sensory"
data and reproduction of it. It sounds like this definition proposes
that
they are exactly equivalent, or that any recognition system is
automatically
invertable. I simply doubt that this can be true, using a principle (I
have
no proof for but I hold) that "meaning"--something we use to recognize
equivalence-- is just not the same for different peceptual events.
An another example I use to think about it is how difficult it is trying
to
draw a reproduction of a picture from memory, and how different the task
is
from drawing a copy is from analyzing the elements in a picture.
Reproducing
visual information is different from conceptual scene decomposition.
I should have discussed my motiviation for using lossy video compression
as a
test for AGI, as I did for lossless text. The idea is that lossy
compression
is not possible without an accurate model of human perception. Humans
receive
sensory information at about 1 Gb/s (b = bits, B = Bytes), and somehow
filter
and compress this down to about 10 b/s by the time it reaches long term
memory.
A lossy compressor given input x must first compute the lossy function y =
f(x) that models human perception, then compress y using a lossless model
p(y). All lossy compressors work this way. For example, JPEG performs a
color transform and downsamples the two chroma components because the eye
is
less sensitive to high spatial frequencies in chroma than in luma. It
uses 3
primary colors because the eye has 3 types of cones. Thus, there is no
need
to distinguish the pure spectral yellow in a rainbow from the yellow you
see
on a monitor that results from mixing red and green. After the lossy
transform (which also involves quantization that varies by spatial
frequency),
the remaining features are compressed losslessly (using e.g. run-length
and
Huffman coding).
Humans are not capable of inverting visual perception (i.e. producing real
time video from memory), but nearly all compuer models, whether lossy or
lossless, can decompress at least as fast as they can compress, and often
faster (e.g. JPEG and MPEG). So it was my assumption that this would not
be a
hardship in an AGI test, once the hard problem of computing p(f(.)) was
solved. Decompression means computing p(y) again, then inverting f(.). I
can't say for certain that inverting f(.) is not hard, but I don't believe
it
will be.
--- Mark Waser <[EMAIL PROTECTED]> wrote:
> If a sentence can be rewritten in 1000 different ways without changing
> its meaning, then that only adds 10 bits.
Yes, provided that you have an efficient encoding/decoding scheme for
that particular sentence. Now, what is the overhead for having efficient
encoding/decoding schemes for *all* possible sentences?
You state that "The amount of extra knowledge needed to encode the
choice of representations is small." I strenuously disagree with this
statement. While the number of bits required in the encoded text is
small,
the amount of extra knowledge required in the encoder and decoder is
much,
*MUCH* larger. What model did you have in mind that joins both deep
knowledge and the very shallow lossless algorithms that you cite? I
don't
believe that you can cite *any* deep knowledge algorithm/model that
doesn't
suffer when you try to add losslessness.
Statistical language models such as n-gram backoff (aka PPM),
distant-bigram
models and LSA, and combinations thereof using information fusion
approaches
such as maximum entropy or context mixing, are all lossless. The
knowledge
learned by such models is the same size as the compressed output,
typically 1
to 2 bits per character of the training data. These models are generally
regarded as efficient: they learn thousands of times faster than humans.
Of
course the models are low level, modeling only semantics and simple, flat
grammar perhaps at the level of a 2 or 3 year old child. But there is
currently nothing better.
Can you cite any lossy text compression models or models that separate
deep
knowledge from representation? Do you have any figures on how much
information is needed to encode meaning vs. representation? Can you argue
that the representation is at least half of the information? For example,
can
you think of a 100 character sentence that can be expressed in 2^50
different
ways without changing its meaning? (assuming 1 bpc entropy)
-- Matt Mahoney, [EMAIL PROTECTED]
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