Try ping-pong - as per the computer game. Just a line (/bat) and a
square(/ball) representing your opponent - and you have a line(/bat) to play
against them
Now you've got a relatively simple true AGI visual problem - because if the
opponent returns the ball somewhat as a real human AGI does, (without the
complexities of spin etc just presumably repeatedly changing the direction (and
perhaps the speed) of the returned ball) - then you have a fundamentally
*unpredictable* object.
How will your program learn to play that opponent - bearing in mind that the
opponent is likely to keep changing and even evolving strategy? Your approach
will have to be fundamentally different from how a program learns to play a
board game, where all the possibilities are predictable. In the real world,
"past performance is not a [sure] guide to future performance". Bayes doesn't
apply.
That's the real issue here - it's not one of simplicity/complexity - it's that
your chosen worlds all consist of objects that are predictable, because they
behave consistently, are shaped consistently, and come in consistent, closed
sets - and can only basically behave in one way at any given point. AGI is
about dealing with the real world of objects that are unpredictable because
they behave inconsistently,even contradictorily, are shaped inconsistently and
come in inconsistent, open sets - and can behave in multi-/poly-ways at any
given point. These differences apply at all levels from the most complex to the
simplest.
Dealing with consistent (and regular) objects is no preparation for dealing
with inconsistent, irregular objects.It's a fundamental error
Real AGI animals and humans were clearly designed to deal with a world of
objects that have some consistencies but overall are inconsistent, irregular
and come in open sets. The perfect regularities and consistencies of
geometrical figures and mechanical motion (and boxes moving across a screen)
were only invented very recently.
From: David Jones
Sent: Sunday, June 27, 2010 5:57 PM
To: agi
Subject: Re: [agi] Huge Progress on the Core of AGI
Jim,
Two things.
1) If the method I have suggested works for the most simple case, it is quite
straight forward to add complexity and then ask, how do I solve it now. If you
can't solve that case, there is no way in hell you will solve the full AGI
problem. This is how I intend to figure out how to solve such a massive
problem. You cannot tackle the whole thing all at once. I've tried it and it
doesn't work because you can't focus on anything. It is like a Rubik's cube.
You turn one piece to get the color orange in place, but at the same time you
are screwing up the other colors. Now imagine that times 1000. You simply can't
do it. So, you start with a simple demonstration of the difficulties and show
how to solve a small puzzle, such as a Rubik's cube with 4 little cubes to a
side instead of 6. Then you can show how to solve 2 sides of a rubiks cube,
etc. Eventually, it will be clear how to solve the whole problem because by the
time you're done, you have a complete understanding of what is going on and how
to go about solving it.
2) I haven't mentioned a method for matching expected behavior to observations
and bypassing the default algorithms, but I have figured out quite a lot about
how to do it. I'll give you an example from my own notes below. What I've
realized is that the AI creates *expectations* (again). When those
expectations are matched, the AI does not do its default processing and
analysis. It doesn't do the default matching that it normally does when it has
no other knowledge. It starts with an existing hypothesis. When unexpected
observations or inconsistencies occur, then the AI will have a *reason* or
*cue* (these words again... very important concepts) to look for a better
hypothesis. Only then, should it look for another hypothesis.
My notes:
How does the ai learn and figure out how to explain complex unforseen behaviors
that are not preprogrammable. For example the situation above regarding two
windows. How does it learn the following knowledge: the notepad icon opens a
new notepad window and that two windows can exist... not just one window that
changes. the bar with the notepad icon represenants an instance. the bar at the
bottom with numbers on it represents multiple instances of the same window and
if you click on it it shows you representative bars for each window.
How do we add and combine this complex behavior learning, explanation,
recognition and understanding into our system?
Answer: The way that such things are learned is by making observations,
learning patterns and then connecting the patterns in a way that is consistent,
explanatory and likely.
Example: Clicking the notepad icon causes a notepad window to appear with no
content. If we previously had a notepad window open, it may seem like clicking
the icon just clears the content by the instance is the same. But, this cannot
be the case because if we click the icon when no notepad window previously
existed, it will be blank. based on these two experiences we can construct an
explanatory hypothesis such that: clicking the icon simply opens a blank
window. We also get evidence for this conclusion when we see the two windows
side by side. If we see the old window with the content still intact we will
realize that clicking the icon did not seem to have cleared it.
Dave
On Sun, Jun 27, 2010 at 12:39 PM, Jim Bromer <jimbro...@gmail.com> wrote:
On Sun, Jun 27, 2010 at 11:56 AM, Mike Tintner <tint...@blueyonder.co.uk>
wrote:
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.
Well, I agree with what (I think) Mike was trying to get at, except that I
understood that Ben, Hutter and especially David were not only talking about
prediction as a specification of a single prediction when many possible
predictions (ie expectations) were appropriate for consideration.
For some reason none of you seem to ever talk about methods that could be
used to react to a situation with the flexibility to integrate the recognition
of different combinations of familiar events and to classify unusual events so
they could be interpreted as more familiar *kinds* of events or as novel forms
of events which might be then be integrated. For me, that seems to be one of
the unsolved problems. Being able to say that the squares move to the right in
unison is a better description than saying the squares are dancing the irish
jig is not really cutting edge.
As far as David's comment that he was only dealing with the "core issues," I
am sorry but you were not dealing with the core issues of contemporary AGI
programming. You were dealing with a primitive problem that has been
considered for many years, but it is not a core research issue. Yes we have to
work with simple examples to explain what we are talking about, but there is a
difference between an abstract problem that may be central to your recent work
and a core research issue that hasn't really been solved.
The entire problem of dealing with complicated situations is that these
narrow AI methods haven't really worked. That is the core issue.
Jim Bromer
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