Hi,
About the Hutter Prize (see the end of this email for a quote of the
post I'm responding to, which was posted a week or two ago)...
While I have the utmost respect for Marcus Hutter's theoretical work
on AGI, and I do think this prize is an interesting one, I also want
to state that I don't
Ben,So you think that, Powerful AGI == good Hutter test resultBut you have a problem with the reverse implication,good Hutter test result =/= Powerful AGIIs this correct?
Shane
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A common objection to compression as a test for AI is that humans can't do
compression, so it has nothing to do with AI. The reason people can't compress
is that compression requires both AI and deterministic computation. The human
brain is not deterministic because it is made of neurons,
Howdy Shane,
I'll try to put my views in your format
I think that
Extremely powerful, vastly superhuman AGI == outstanding Hutter test result
whereas
Human-level AGI =/= Good Hutter test result
just as
Human =/= Good Hutter test result
and for this reason I consider the Hutter test a
See the paper at
http://www.cogsci.rpi.edu/CSJarchive/Proceedings/2006/docs/p2059.pdf
ABSTRACT:
The Human Speechome Project is an effort to observe
and computationally model the longitudinal course of
language development of a single child at an unprecedented
scale. The idea is this: Instrument
That seems clear.Human-level AGI =/= Good Hutter test result
just asHuman =/= Good Hutter test resultMy suggestion then is to very slightly modify the test as follows: Instead of just getting the raw characters, what you get is thesequence of characters and the probability distribution over the
Matt,
To summarize and generalize data and to use the summary to predict the
future is no doubt at the core of intelligence. However, I do not call
this process compressing, because the result is not faultless, that
is, there is information loss.
It is not only because the human brains are
Yes, I think a hybridized AGI and compression algorithm could dobetter than either one on its ownHowever, this might result in
an incredibly slow compression process, depending on how fast the AGIthinks.(It would take ME a long time to carry out this process overthe whole Hutter
I don't think it's anywhere near that much. I read at about 2 KB
per minute, and I listen to speech (if written down as plain text)
at a roughly similar speed. If you then work it out, buy the time
I was 20 I'd read/heard not more than 2 or 3 GB of raw text.
If you could compress/predict
On 8/12/06, Matt Mahoney [EMAIL PROTECTED] wrote:
In order to compress text well, the compressor must be able to estimate probabilities over text strings, i.e. predict text.
Um no, the compressor doesn't need to predict anything - it has the entire file already at hand.
The _de_compressor would
First, the compression problem is not in NP. The general problem of encoding strings as the smallest programs to output them is undecidable.Second, given a model, then compression is the same as prediction. A model is a function that maps any string s to an estimated probability p(s). A compressor
On 8/12/06, Matt Mahoney [EMAIL PROTECTED] wrote:
First,
the compression problem is not in NP. The general problem of
encoding strings as the smallest programs to output them is undecidable.
But as I said, it becomes NP when there's an upper limit to decompression time.
Second,
given a model,
But Shane, your 19 year old self had a much larger and more diversevolume of data to go on than just the text or speech that you
ingested...I would claim that a blind and deaf person at 19 could pass aTuring test if they had been exposed to enough information overthe years. Especially if they had
On 8/13/06, Matt Mahoney [EMAIL PROTECTED] wrote:
Whether
or not a compressor implements a model as a predictor or not is
irrelevant. Modeling the entire input at once is mathematically
equivalent to predicting successive symbols. Even if you think
you are not modeling, you are. If you design a
Matt,
So you mean we should leave forgetting out of the picture, just
because we don't know how to objectively measure it.
Though objectiveness is indeed desired for almost all measurements, it
is not the only requirement for a good measurement of intelligence.
Someone can objectively measure a
I think compression isessential tointelligence,but the difference between lossy and lossless may make the algorithms quite different.
But why notlet competitorscompress lossily?As far asprediction goes, the testing part is still the same!
If you guys have a lossy version of the prize I will
Hutter's only assumption about AIXI is that the environment can be simulated by
a Turing machine.
With regard to forgetting, I think it plays a minor role in language modeling
compared to vision and hearing. To model those, you need to understand what
the brain filters out. Lossy compression
On 8/13/06, Matt Mahoney [EMAIL PROTECTED] wrote:
There
is no knowledge that you can demonstrate verbally that cannot also be
learned verbally.
An unusual claim... do you mean all knowledge can be learned verbally,
or do you think there are some kinds of knowledge that cannot be
demonstrated
On Aug 12, 2006, at 6:27 PM, Yan King Yin wrote:
I think compression is essential to intelligence, but the
difference between lossy and lossless may make the algorithms quite
different.
For general algorithms (e.g. ones that do not play to the sensory
biases of humans) there should be
As long as we're talking about fantasy applications that require
superhuman AGI, I'd be impressed by a lossy compression of Wikipedia
that decompressed to a non-identical version carrying the same semantic
information.
--
Eliezer S. Yudkowsky http://singinst.org/
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