Jim :This illustrates one of the things wrong with the dreary instantiations of
the prevailing mind set of a group. It is only a matter of time until you
discover (through experiment) how absurd it is to celebrate the triumph of an
overly simplistic solution to a problem that is, by its very potential, full of
possibilities]
To put it more succinctly, Dave & Ben & Hutter are doing the wrong subject -
narrow AI. Looking for the one right prediction/ explanation is narrow AI.
Being able to generate more and more possible explanations, wh. could all be
valid, is AGI. The former is rational, uniform thinking. The latter is
creative, polyform thinking. Or, if you prefer, it's convergent vs divergent
thinking, the difference between wh. still seems to escape Dave & Ben & most
AGI-ers.
Consider a real world application - a footballer, Maradona, is dribbling with
the ball - you don't/can't predict where he's going next, you have to be ready
for various directions, including the possibility that he is going to do
something surprising and new - even if you have to commit yourself to
anticipating a particular direction. Ditto if you're trying to predict the path
of an animal prey.
Dealing only with the "predictable" as most do, is perhaps what Jim is getting
at - predictable. And wrong for AGI. It's your capacity to deal with the open,
unpredictable, real world that signifies you are an AGI - not the same old,
closed predictable, artificial world. When will you have the courage to face
this?
Sent: Sunday, June 27, 2010 4:21 PM
To: agi
Subject: Re: [agi] Huge Progress on the Core of AGI
On Sun, Jun 27, 2010 at 1:31 AM, David Jones <davidher...@gmail.com> wrote:
A method for comparing hypotheses in explanatory-based reasoning:Here is a
simplified version of how we solve case study 1:
The important hypotheses to consider are:
1) the square from frame 1 of the video that has a very close position to the
square from frame 2 should be matched (we hypothesize that they are the same
square and that any difference in position is motion). So, what happens is
that in each two frames of the video, we only match one square. The other
square goes unmatched.
2) We do the same thing as in hypothesis #1, but this time we also match the
remaining squares and hypothesize motion as follows: the first square jumps
over the second square from left to right. We hypothesize that this happens
over and over in each frame of the video. Square 2 stops and square 1 jumps
over it.... over and over again.
3) We hypothesize that both squares move to the right in unison. This is the
correct hypothesis.
So, why should we prefer the correct hypothesis, #3 over the other two?
Well, first of all, #3 is correct because it has the most explanatory power
of the three and is the simplest of the three. Simpler is better because, with
the given evidence and information, there is no reason to desire a more
complicated hypothesis such as #2.
So, the answer to the question is because explanation #3 expects the most
observations, such as:
1) the consistent relative positions of the squares in each frame are
expected.
2) It also expects their new positions in each from based on velocity
calculations.
3) It expects both squares to occur in each frame.
Explanation 1 ignores 1 square from each frame of the video, because it can't
match it. Hypothesis #1 doesn't have a reason for why the a new square appears
in each frame and why one disappears. It doesn't expect these observations. In
fact, explanation 1 doesn't expect anything that happens because something new
happens in each frame, which doesn't give it a chance to confirm its hypotheses
in subsequent frames.
The power of this method is immediately clear. It is general and it solves
the problem very cleanly.
Dave
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Nonsense. This illustrates one of the things wrong with the dreary
instantiations of the prevailing mind set of a group. It is only a matter of
time until you discover (through experiment) how absurd it is to celebrate the
triumph of an overly simplistic solution to a problem that is, by its very
potential, full of possibilities.
For one example, even if your program portrayed the 'objects' as moving in
'unison' I doubt if the program calculated or represented those objects in
unison. I also doubt that their positioning was literally based on moving
'right' since their movement were presumably calculated with incremental
mathematics that were associated with screen positions. And, looking for a
technicality that represents the failure of the over reliance of the efficacy
of a simplistic over generalization, I only have to point out that they did not
only move to the right, so your description was either wrong or only partially
representative of the apparent movement.
As long as the hypotheses are kept simple enough to eliminate the less useful
hypotheses, and the underlying causes for apparent relations are kept
irrelevant, over simplification is a reasonable (and valuable) method. But if
you are seriously interested in scalability, then this kind of conclusion is
just dull.
I have often made the criticism that the theories put forward in these groups
are overly simplistic. Although I understand that this was just a simple
example, here is the key to determining whether a method is overly simplistic
(or as in AIXI) based on an overly simplistic definition of insight. Would
this method work in discovering the possibilities of a potentially more complex
IO data environment like those we would expect to find using AGI?
Jim Bromer.
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