On Wed, Jul 14, 2010 at 10:35 PM, Michael Swan ms...@voyagergaming.comwrote:
I'd argue that mathematical operations are unnecesary,
we don't even have integer support inbuilt.
I'd disagree. is a mathematical operation, and in combination can
become an enormous number of concepts.
And yet you dream dreams wh. are broad-ranging in subject matter, unlike all
programs wh. are extremely narrow-ranging.
--
From: Michael Swan ms...@voyagergaming.com
Sent: Thursday, July 15, 2010 5:16 AM
To: agi agi@v2.listbox.com
Subject: Re:
On Wed, Jul 14, 2010 at 7:46 PM, Abram Demski abramdem...@gmail.com wrote:
Jim,
There is a simple proof of convergence for the sum involved in defining the
probability of a given string in the Solomonoff distribution:
At its greatest, a particular string would be output by *all* programs.
Jim Bromer wrote:
Since you cannot fully compute every string that may be produced at a certain
iteration, you cannot make the claim that you even know the probabilities of
any
possible string before infinity and therefore your claim that the sum of the
probabilities can be computed is not
It is no wonder that I'm having a hard time finding documentation on
hypothesis scoring. Few can agree on how to do it and there is much debate
about it.
I noticed though that a big reason for the problems is that explanatory
reasoning is being applied to many diverse problems. I think, like I
Hypotheses are scored using Bayes law. Let D be your observed data and H be
your
hypothesis. Then p(H|D) = p(D|H)p(H)/p(D). Since p(D) is constant, you can
remove it and rank hypotheses by p(D|H)p(H).
p(H) can be estimated using the minimum description length principle or
Solomonoff
:) You say that as if bayesian explanatory reasoning is the only way.
There is much debate over bayesian explanatory reasoning and non-bayesian.
There are pros and cons to bayesian methods. Likewise, there is the problem
with non-bayesian methods because few have figured out how to do it
Make sure you study that up YKY :)
John
From: YKY (Yan King Yin, 甄景贤) [mailto:generic.intellige...@gmail.com]
Sent: Thursday, July 15, 2010 8:59 AM
To: agi
Subject: [agi] OFF-TOPIC: University of Hong Kong Library
Today, I went to the HKU main library:
=)
KY
agi |
I think that Solomonoff Induction includes a computational method that
produces probabilities of some sort and whenever those probabilities were
computed (in a way that would make the function computable) they would sum
up to 1. But the issue that I am pointing out is that there is no way that
On Wed, Jul 14, 2010 at 10:22 AM, David Jones davidher...@gmail.com wrote:
What do you mean by definitive events?
I was just trying to find a way to designate obsverations that would be
reliably obvious to a computer program. This has something to do with the
assumptions that you are using.
Jim,
even that isn't an obvious event. You don't know what is background and what
is not. You don't even know if there is an object or not. You don't know if
anything moved or not. You can make some observations using predefined
methods and then see if you find matches... then hypothesize about
Sounds like a good explanation of why a body is essential for vision - not just
for POV and orientation [up/left/right/down/ towards/ away] but for comparison
and yardstick - you do know when your body or parts thereof are moving -and
it's not merely touch but the comparison of other objects
On screenshots, the point of view is equivalent to the absolute positions
and their relative positions using absolute(screen x and y) measurements.
You don't need a robot to learn about how AGI works and figure out how to
solve some problems. It would be a terrible mistake to spend years, or even
Ok Off topic, but not as far as you might think. YKY has posted in Creating
Artificial Intelligence on a collaborative project. It is quite important
to know *exactly* where he is. You see Taiwan uses the classical character
set, The People's Republic uses a simplified character set.
Hong Kong
Jim,
Yes this is true provable: there is no way to compute a correct error
bound such that it converges to 0 as the computation of algorithmic
probability converges to the correct number. More specifically--- we can
approximate the algorithmic probability from below, computing better lower
We all make conjectures all of the time, but we don't often don't have
anyway to establish credibility for the claims that are made. So I wanted
to examine one part of this field, and the idea that seemed most natural for
me was Solomonoff Induction. I have reached a conclusion about the subject
-Original Message-
From: Ian Parker [mailto:ianpark...@gmail.com]
Ok Off topic, but not as far as you might think. YKY has posted in Creating
Artificial Intelligence on a collaborative project. It is quite important to
know
exactly where he is. You see Taiwan uses the classical
On Wed, Jul 14, 2010 at 10:22 AM, David Jones davidher...@gmail.com wrote:
I don't really understand what you mean here: The central unsolved
problem, in my view, is: How can hypotheses be conceptually integrated along
with the observable definitive events of the problem to form good
Jim,
The statements about bounds are mathematically provable... furthermore, I
was just agreeing with what you said, and pointing out that the statement
could be proven. So what is your issue? I am confused at your response. Is
it because I didn't include the proofs in my email?
--Abram
On Thu,
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