Ben,
Many thanks for refs. and detailed reply. Much appreciated and v. interesting.
After reading around this area, and cog sci re analogy, here are my v. cursory
- & as usual tendentious - impressions (so blitz me down). Basically, my ideas
about the importance of sensory/visual graphics and images for both analogy and
AGI were enriched but not fundamentally changed.
1) Formal ("Literate") Analogy - nearly all the science seems to be about
analogies using formal sign systems, esp. language. But as Gentner
acknowledges, there is little work re
2) Informal ("Pre-Literate") Analogy (esp animals but also pos. infants) -
which has to be primary. That worm working out that many different-shaped
objects would stop up his burrow, was using analogy - i.e. saying in effect:
"these objects are loosely shaped like/ will fit into, that hole." Ditto the
bird that fashions a hook to manipulate objects. But I don't see symbols
playing any part in their (evolutionarily primary) analogical reasoning.
3) Structural Mapping - the research seems to concentrate mainly on mapping
sets of symbolic relationships, and that means depending on one-to-one,
identical elements to draw analogies (whereas sensory/ visual mapping/analogy
is not at all so constrained), and ...
4) Symbolically-Derived Analogies are largely trivial (!) - that's my
definitely cursory conclusion, but I'm betting that none of the computational
systems so far have produced any striking analogies. It all seems to be about
simple numerical, alphabetic and verbal/logical analogies, Can you give
instances of any interesting analogical results here either from computers or
ordinary human intelligence that show any promise for adaptive intelligence?
(First impressions of Gentner and Hofstadter's work here - not impressed).
5)Striking analogies seem to be all Sensory/visual derived - all the classic
sci. discovery examples used, eg Kepler, Kekule, Duncker all seem to me
obviously derived by way of sensory/visual graphics/images not symbols.
Reading through the rich Indurkhya book, all the metaphors listed (that I've
read so far) seem to me similarly derived. "Skies crying" (rain- tears) etc do
not seem to be in any way symbolically derived.
6)Any Visual Analogy Computers? -
a)can any computers draw visual analogies? - e.g can any do what a human
can do - think of a fortress tower/turret and quickly (as I did) come up with
loose sensory analogies: "fork", "teeth (with gaps)" "fingers" ?
b)can any digital computers truly map, period? - put one shape on top of
another and see immediately that they fit exactly/loosely, (without first
breaking them down into bits/formulae?) - or would it take an analogical
computer to do this?
c)has anyone incorporated in their AI/AGI system, as my ideas suggest they
should, a cartoon unit and a movie unit, for the purposes of reasoning? (I'm
still not sure re yours).
7)Facility of Analog Retrieval:
"What is more difficult is: Given a large body of knowledge, fish out
the analogies that are going to be relevant and useful (because there
are VERY many possible analogies, and most will be very dumb)"
a) off the top of my head - I wonder whether any sensorily derived analogies
are dumb? Not-so-great like my turret analogies, but still relevant, legitimate.
- precisely because they literally fit.
b) much more importantly - you may be asking for the impossible - analog
retrieval, it seems to me, is fundamentally a RISKY, UNCERTAIN business -
hit-and-miss search. Creatives get paid many thousands of dollars to sit around
for weeks and come up with new analogies to "Coke (whatever product) is as
refreshing as..." And that process is very laborious. With lots of trite,
non-strking analogies coming up and long pauses. The adaptive drawing of
sensory analogies is fundamentally an adventurous exploration, because you are
trying to connect up domains that have never been connected before. There is no
formula for it by definition or guarantee that it will work. Perhaps if we ever
do have truly superintelligent robots, they will be able to do it all much
faster, (although by then we may link into their brains), but they will still
be engaged in uncertain exploration with no definable time period
Only just beginning to get into this! But better stop there. Thanks again P.S.
If interested, can send copy of Grounding Cognition, ed D Pecher, R Zwaan,
C.U.P. - v. recent sci research which tends, broadly, to support my ideas..
----- Original Message -----
From: Benjamin Goertzel
To: agi@v2.listbox.com
Sent: Saturday, May 12, 2007 7:22 PM
Subject: Re: [agi] All these moments will be lost in time, like tears in rain
Mike Tintner,
Firstly, to ground your discussion of analogy in the AI field, you might like
to look at
"Fluid Concepts and Creative Analogies" by Douglas Hofstadter
and "Metaphor and Cognition" by Bipin Indurkhya, online at
http://www.iiit.ac.in/~bipin/
Like many natural language words, "analogy" is a bit ambiguous....
It can be used to refer to low-level inference operations like the example
Pei gave...
Or, it can be used to refer to higher-level processes that involve loads
of low-level coordinated inference operations... say
"This dog is attacking me... how can I get rid of it? Well, when the
rabbit was attacking me, I got rid of it by attracting a cat, which scared
away the rabbit. Analogously, maybe I could get rid of the dog by
finding something to scare it away.... Hmm... what about that lion
who lives next door. 'Hey Mr. Lion! Come over to play!' "
The above analogy can be broken down into a series of low-level
inference steps, including among them some that match the simple
logical form that Pei called "analogy" in his example. Doing so, as a
human, is just a simple textbook exercise...
The drawing of analogies, given an appropriate selection of a small
amount of relevant knowledge, is not an incredibly hard problem...
What is more difficult is: Given a large body of knowledge, fish out
the analogies that are going to be relevant and useful (because there
are VERY many possible analogies, and most will be very dumb).
But this is really just the "uncertain inference control" and "attention
allocation" problem in general, not a specific problem to do with
analogies.
Unlike Tintner, I don't see why analogy has to be visual, though it
can be. The scientific study of analogy in cog sci suggests that
some analogies are visually grounded but many are not.
-- Ben
On 5/12/07, Richard Loosemore < [EMAIL PROTECTED]> wrote:
Pei Wang wrote:
> On 5/12/07, Bob Mottram < [EMAIL PROTECTED]> wrote:
>> In a recent interview
>> (http://discovermagazine.com/2007/jan/interview-minsky/ ) Marvin Minsky
>> says that one of the key things which an intelligent system ought to
>> be able to do is reason by analogy.
>>
>> "His thoughts tumbled in his head, making and breaking alliances
>> like underpants in a dryer without Cling Free."
>>
>> Which made me wonder, can Novamente, NARS or any other prospective AGI
>> system do this kind of reasoning?
>
> In a broad sense, almost all inference in NARS is analogy --- in a
> term logic, each statements indicates the possibility of one term
> being used (in certain way) as another, and inference on these
> statements builds new "can be used as" relations (which technically
> are called inheritance, similarity, etc) among terms.
>
> In a narrow sense, NARS has an analogy rule which takes "X and Y are
> similar" and "X has property P" as premises to derive a conclusion "Y
> has property P" (premises and conclusions are all true to various
> degrees). See
http://nars.wang.googlepages.com/NARS-Examples-SingleStep.txt
> for concrete examples by searching for "analogy" in the file.
>
> For the analogy with the form "X:Y = Z:?", NARS needs more than one
> step. It first looks for a relation between X and Y, then looks for
> Z's "image" under the relation.
Hmmmm....
But do you think this captures *all* of the idea of what "analogy" is
the human case? Most of it?
How would you say that this squared with Hofstadter's ideas about what
analogy might be?
Looking for a relation between X and Y is all very well, but one of the
things that DRH is fond of telling us is that not just any old relation
will do. And if there are a (quasi-)infinite number of possible
relations between X and Y, doesn't the selection of the appropriate one
become the heart of the analogy process, rather than just a subsidiary
step? (In just the same way that reasoning systems in general need to
have an inference control mechanism that, in practice, determines how
the system actually behaves)?
What I am getting at here is that I think the concept of an "analogy
mechanism" has not even become clear yet, and so for some people to say
that they believe that their systems already have a kind of analogy
mechanism is to jump the gun a little.
In my system, all relationships can be "opened up" by being operated on,
but there is no fixed class of operators that does the job: instead
they are built on the fly, in a manner that is sensitive to context. So
the nature of X, Y and also Z will be able to have a diffuse effect on
the operator construction process that is trying to find a good
relationship between X and Y. The process of "finding" an operator can
have general meta-operators that govern how the process happens (in
other words there can be "general, analogy-finding strategies"). Those
meta-operators could be called the thing that "is" the analogy
mechanism, but that would be an oversimplification.
Richard Loosemore.
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