RE: [agi] Do the inference rules.. P.S.Edward,
Thanks for interesting info - but if I may press you once more. You talk of
different systems, but you don't give one specific example of the kind of
useful (& significant for AGI) inferences any of them can produce -as I do with
my cat example. I'd especially like to hear of one or more from Novamente, or
Copycat.
Can you think of a single analogy or metaphor, in addition, that is purely
symbolic?
I don't, and didn't, deny that logical thought is important. But it's only a
small part, I'm arguing of most productive, AGI-type reasoning.
Nor BTW are am I arguing at all against symbols, (you might care to look at
the "Picture Tree" thread I started a few months ago to better understand my
thinking here) - the brain (and any true AGI, I believe) uses symbols, outline
graphics [or Johnson's image schemata] and images in parallel, interdependently
and continuously, to reason about the world. (Note: "continuously." You seem to
think that some occasional sensory grounding of an AGI system here and there
will do. No, I'm arguing, it has to be, and is, continuous and applied to all
information and subjects).
What I am arguing against, rather than symbols, is what you might call the
"bookroom illusion" - which you saw graphically illustrated in John Rose's post
- the illusion that you can "learn about the world just from books" - or, to be
precise, that you can learn, and think about and build models of the world with
symbols/ text alone. It's an understandable illusion given that we often spend
hours apparently doing nothing but read text. But it is an illusion. The brain
does, and has to, continuously make sense (in images) of everything we read.And
if it can't then that text won't make sense - & it's a case of "I can't see
what you are talking about."
Edward:This is in response to Mike Tintner's 10/11/2007 7:53 PM post. My
response is in all-caps.
Vladimir: .and also why can't 3D world model be just described abstractly,
by
> presenting the intelligent agent with bunch of objects with attached
> properties and relations between them that preserve certain
> invariants? Spacial part of world model doesn't seem to be more
> complex than general problem of knowledge arrangement, when you have
> to keep track of all kinds of properties that should (and shouldn't)
> be derived for given scene.
>
Vladimir and Edward,
I didn't really address this idea essentially common to you both, properly.
The idea is that a network or framework of symbols/ symbolic concepts can
somehow be used to reason usefully and derive new knowledge about the
world - a network of classes and subclasses and relations between them, all
expressed symbolically. Cyc and Nars are examples.
OK let's try and set up a rough test of how fruitful such networks/ models
can be.
Take your Cyc or similar symbolic model, which presumably will have
something like "animal - mammals - humans - primates - cats etc " and
various relations to "move - jump - sit - stand " and then "jump -
on - objects" etc etc. A vast hierarchy and network of symbolic concepts,
which among other things tell us something about various animals and the
kinds of movements they can make.
Now ask that model in effect: "OK you know that the cat can sit and jump on
a mat. Now tell me what other items in a domestic room a cat can sit and
jump on. And create a scenario of a cat moving around a room."
I suspect that you will find that any purely symbolic system like Cyc will
be extremely limited in its capacity to deduce further knowledge about cats
or other animals and their movements with relation to a domestic room - and
may well have no power at all to create scenarios.
IT DEPENDS WHAT YOU MEAN BY "PURELY SYMBOLIC". IN THE PAST "SYMBOLIC"
GENERALLY REFERRED TO SYSTEMS WITHOUT MUCH GROUNDING, SO THE SYMBOLS HAD
RELATIVELY LITTLE SEMANTIC "MEANING." OFTEN SUCH SYSTEMS RELIED ON RELATIVELY
BRITTLE DEFINITIONS AND RULES OF INFERENCE.
THAT IS NOT THE APPROACH I ADVOCATE. (AND PLEASE DON'T HOLD CYC UP AS A GOOD
EXAMPLE OF THE APPROACH I ADVOCATE. THERE IS A WORLD OF DIFFERENCE BETWEEN A
RELATIVELY OLD-FASHIONED AI SYSTEM LIKE CYC AND A STATE OF THE ART AGI SYSTEM
LIKE NOVAMENTE.)
THE STATE OF THE ART AGI APPROACH I FAVOR IS BASED ON (1) MASSIVE AMOUNTS OF
EXPERIENCES OF SOME SORT TO PROVIDE GROUNDING TO SYMBOLS AND (2) FLEXIBLE RULES
FOR MATCHING, INSTANTIATING, GENERALIZATION, AND INFERENCING IN A
CONTEXT-SPECIFIC WAY FROM SUCH MASSIVE EXPERIENCE, SO AS TO ENABLE SOMETHING
APPROACHING -- AND ULTIMATELY SURPASSING -- HUMAN-LEVEL INTELLIGENCE.
BUT SUCH SYSTEMS WOULD BE COMPOSED ALMOST ENTIRELY OF SYMBOLS. EVEN THE
TYPES OF SYSTEMS YOU SEEM TO BE FAVORING WOULD BE COMPOSED OF SYMBOLS. BITS
AND BYTES ARE, AFTER ALL, SYMBOLS. SO PLEASE LET'S STOP KNOCKING SYMBOLS, PER
SE.
THE DISTINCTION SHOULD BE BETWEEN RELATIVELY NAKED SYMBOLS AND SYMBOL
GROUNDED IN NETWORKS OF MEANING - I.E., NETWORKS OF RELATIONSHIPS SUCH AS
SENSORY PATTERNS (YES, I LIKE YOU THINK SENSORY EXPERIENCE IS GENERALLY
IMPORTANT), ASSOCIATIONS, CONDITIONAL PROBABILITIES, TEMPORAL RELATIONS,
CAUSE-AND-EFFECTS, ATTRIBUTES, FUNCTIONS, GOALS, VALUES, IMPORTANCE WEIGHTINGS,
GENERALIZATIONS, SPECIALIZATIONS, AND BEHAVIORAL SCHEMAS, ALL IN THE CONTEXT
POWERFUL INFERENCING AND AUTOMATIC LEARNING.
OF COURSE AS I SAID IN A VERY RECENT POST, GROUNDING COMES IN ALL SORTS OF
DIFFERENT TYPES AND DEGREES. SO DIFFERENT TYPES AND DEGREES OF INTELLIGENCE
CAN BE DERIVED WITH DIFFERENT TYPES OF GROUNDING. EVEN IN A SYSTEM LIKE CYC OR
WORDNET A CONCEPT WOULD NORMALLY HAVE SOME DEGREE OF GROUNDING.
But you or I, with a visual/ sensory model of that cat and that room, will
be able to infer with reasonable success whether it can or can't jump, sit
and stand on every single object in that room - sofa, chair, bottle, radio,
cupboard etc etc. And we will also be able to make very complex assessments
about which parts of the objects it can or can't jump or stand on - which
parts of the sofa, for example - and assessments about which states of
objects, (well it couldn't jump or stand on a large Coke bottle if erect,
but maybe if the bottle were on its side, and almost certainly if it were a
jeroboam on its side). And I think you'll find that our capacity to draw
inferences - from our visual and sensory model - about cats and their
movements is virtually infinite.
And we will also be able to create a virtually infinite set of scenarios of
a cat moving in various ways from point to point around the room.
Reality check: what you guys are essentially advocating is logical systems
and logical reasoning for AGI's - now how many kinds of problems in the real
human world is logic actually used to solve? Not that many. Oh it's an
important part of much problemsolving but only a part. How much scientific
problemsolving depends seriously on logic? Is logic going to help you
understand and have ideas about genetics or how cells work, or the brain
works, or how and why wars start? Is it going to be much use for design
problems? Does it help in telling stories? .. keep on going through the vast
range of human and animal problemsolving (all of which remember are the ONLY
forms of [A]GI that actually work).
That's why I asked you: give me some examples of useful new knowledge or
analogies [especially analogies] that have been derived from logical systems
or logic, period (except about logic itself).
THIS TIME THE ANSWER DEPENDS ON WHAT YOU MEAN BY "LOGICAL." WIKIPEDIA'S
BROAD DEFINITION OF "LOGIC" IS: "THE STUDY OF THE PRINCIPLES AND CRITERIA OF
VALID INFERENCE AND DEMONSTRATION." THUS, THE TERM IS MUCH MORE BROAD THAN THE
BRITTLE FORMAL LOGICS THAT MUCH OF AI WAS HUNG UP ON FOR YEARS.
I AM NOT A BIG FAN OF TRADITIONAL FORMAL LOGIC. SINCE THE EARLY '70'S I HAVE
SAID "FORMAL LOGIC IS TO HUMAN THOUGH WHAT DRESSAGE IS THE MOTION OF HORSES --
EXCEPT IN ITS SIMPLEST FORMS IT IS TOTALLY UNNATURAL." COMMON SENSE NOTIONS,
SUCH AS "THE EXCEPTION THAT PROVES THE RULE" INDICATES THAT REASONING WITH
BINARY TRUTH VALUES IS BRAIN-DEAD IN MANY DOMAINS.
BUT MANY FORMS OF LOGICAL REASONING ARE MUCH MORE FLEXIBLE. BAYESIAN
INFERENCING, FOR EXAMPLE, IS A TYPE OF LOGIC BECAUSE IT IS A TYPE OF REASONING
DESPITE ITS LIMITATIONS HAS SHOWN ITSELF TO BE EXTREMELY VALUABLE. IT IS USED
IN MANY SUCCESSFUL COMMERCIAL PRODUCTS. BAYESIAN CLASSIFIERS, FOR EXAMPLE,
HAVE BEEN USED TO MAKE NEW SCIENTIFIC DISCOVERIES FROM VAST AMOUNTS OF SENSOR
DATA. SO IN FACT, SOME TYPES OF LOGIC ARE EXTREMELY VALUABLE AND DO HELP
SCIENTISTS SOLVE PROBLEMS.
FURTHERMORE, IF YOU HAVE READ DOUG HOFSTADTER'S COPYCAT, WHICH MAKES CONTEXT
SPECIFIC ANALOGIES, YOU REALIZE IT USES A FLEXIBLE SIMILARITY SYSTEM, CALLED
SLIPNET, THAT CAUSES SIMILARITY MEASURES TO BE TIGHTENED OR LOOSENED IN A
CONTEXT-DEPENDENT WAY. THIS ALLOWS COPYCAT TO HANDLED THE DIS-SIMILARITES IN
THE CORRESPONDING THINGS THAT ARE BEING COMPARED TO MAKE AN ANALOGY.
NARS OR A NARS-LIKE SYSTEM COULD EASILY BE USED TO REPLACE HOFSTADTER'S
SLIPNET, AND COULD ARGUABLY HAVE SIGNIFICANT ADVANTAGES OVER SLIPNET, SUCH AS
MAKING COPYCAT'S ANALOGY DRAWING PROGRAM MORE GENERALIY APPLICABLE TO A WORLD
KNOWLEDGE BASE. SO "LOGIC" OF THE TYPE FOUND IN NARS COULD ACTUALLY BE USEFUL
IN THE VERY FIELD OF DRAWING ANALOGIES THAT THE ABOVE TEXT IMPLIES IT IS
USELESS FOR.
IN RECENT YEARS THERE HAS BEEN A LOT OF WORK IN DESIGNING SYSTEMS THAT
AUTOMATICALLY LEARN APPROPRIATE PROBABILISTIC LOGICS. ONE OF THESE IS
NOVAMENTE'S PROBABALISTIC LOGIC NETWORKS, OR PLN, WHICH BEN GOERTZEL REFERRED
TO IN HIS POST OF 10/10/2007 4:45 AM ON THIS LIST. I DON'T YET KNOW HOW WELL
ANY OF THESE SYSTEMS WORK, BUT THEY HOLD THE PROMISE OF ALLOWING LOGIC TO
DELIVER ALL OF THE VERY THINGS YOU SAY LOGIC CANNOT DELIVER IN LARGE
WORLD-KNOWLEDGE-COMPUTING AGI'S.
SO, PLEASE LET'S STOP KNOCKING "LOGIC."
New knowledge - especially new science - comes primarily from new
observation of the world, not from logically working through old knowledge.
Artificial general intelligence - the ability to develop new, unprogrammed
solutions to problems - depends on sensory models and observations.
Let me be brutally challenging here : the reason you guys are attached to
purely symbolic models of the world is not because you have any real
evidence of their being productive (for AGI), but because they're what you
know how to do. Hence Vlad's "why can't 3D world model be just described
abstractly.." He doesn't know - he just hopes - that it can. Logically.
What you need here is not logic but - ahem - evidence {sensory stuff].
BRUTAL CHALLENGE ACCEPTED.
(AGAIN, "PURELY SYMBOLIC" COVERS ANY DIGITAL SYSTEM, EVEN THE TYPE YOU SEEM
TO FAVOR.)
ACTUALLY, EVER SINCE I DID MY READING LIST UNDER MINSKY IN 1969-70, MY
GUIDING PHILOSOPHY HAS BEEN THE GIST OF K-LINE THEORY - I.E., THAT ONE REASONS
ABOUT NEW SITUATIONS BY EVOKING MEMORIES OF PAST SIMILAR SITUATIONS. SO I HAVE
BEEN IN FAVOR OF "EXPERIENTIAL REASONING" FOR OVER 37 YEARS. AND I HAVE NEVER
BEEN A BIG FAN OF FORMAL LOGIC FOR THE REASONS STATED ABOVE.
BUT I SEEK TO AVOID BEING NARROW MINDED. I THINK THERE ARE MANY DIFFERENT
POSSIBLE TYPES AND DEGREES OF EXPERIENCE, THERE ARE MANY DIFFERENT WAYS IT CAN
BE REPRESENTED, ALTHOUGH SOME REPRESENTATIONS ARE MUCH MORE CAPABLE AND
EFFICIENT THAN OTHERS. THERE ARE MANY DIFFERENT DEGREES AND TYPES OF
INTELLIGENCE. NOT ALL AGI'S NEED VISUAL MODELS, OR EVEN SENSORY MODELS OF
PHYSICAL REALITY. AGI'S USED FOR SOME LIMITED DOMAINS MAY NOT EVEN NEED
MODEL'S OF 3-DIMENSIONAL PHYSICAL SPACE -- SUCH AS THE HYPOTHETICAL
PROGRAM-LEARNING AGI IN MY EARLIER POST OF TODAY. (ALTHOUGH IT WOULD ALMOST
CERTAINLY DEVELOP OR START WITH A GENERAL MODEL OF N-DIMENSIONAL SPACES.)
I BELIEVE THE CONCEPT OF TURING EQUIVALENCE SHOULD OPEN OUR MINDS TO THE FACT
THAT MOST THINGS IN COMPUTATION CAN BE DONE MANY DIFFERENT WAYS. ALTHOUGH SOME
WAYS ARE MUCH LESS EFFICIENT THAN OTHERS AS TO BE PRACTICALLY USELESS, AND
ALTHOUGH SOME WAYS MAY LACK ESSENTIAL CHARACTERISTICS THAT LIMIT EVEN THEIR
THEORETICAL CAPABILITIES.
AS MUCH AS YOU MAY KNOCK OLD FASHIONED AI SYSTEMS, THEY ACCOMPLISHED A HELL
OF A LOT WITH FLY-BRAIN LEVEL HARDWARE. THUS, RATHER THAN DISMISS THE TYPES OF
REPRESENTATIONS AND REASONING THEY USED AS USELESS, I WOULD SEEK TO UNDERSTAND
BOTH THEIR STRENGTHS AND WEAKNESSES. BEN GOERTZEL'S NOVAMENTE EMBRACES USING
THE EFFICIENCY OF SOME MORE NARROW FORMS OF AI IN DOMAINS OR TASKS WHERE THEY
ARE MORE EFFICIENT (SUCH AS LOW LEVEL VISION, OR FOR DIFFERENT TYPES OF MENTAL
FUNCTIONS), BUT HE SEEKS TO HAVE SUCH DIFFERENT AI'S RELATIVELY TIGHTLY
INTEGRATED, SUCH AS BY HAVING THE SYSTEM HAVE SELF AWARENESS OF THEIR
INDIVIDUAL CHARACTERISTICS. WITH SUCH SELF AWARENESS AN INTELLIGENT AGI MIGHT
WELL OPTIMIZE REPRESENTATIONS FOR DIFFERENT DOMAINS OR DIFFERENT LEVELS OF
ACCESS.
LIKE NOVAMENTE, I HAVE FAVORED A FORM OF REPRESENTATION WHICH IS MORE LIKE A
SEMANTIC NET. BUT ONE CAN REPRESENT A SET OF LOGICAL STATEMENTS IN SEMANTIC
NET FORM. I THINK WITH ENOUGH LOGICAL STATEMENTS IN A GENERAL, FLEXIBLE,
PROBABILISTIC LOGIC ONE SHOULD BE ABLE TO THEORETICALLY REPRESENT MOST FORMS OF
EXPERIENCE THAT ARE RELEVANT TO AN AGI -- INCLUDING THE VERY TYPE OF VISUAL
SENSORY MODELING YOU SEEM TO BE ADVOCATING.
Edward W. Porter
Porter & Associates
24 String Bridge S12
Exeter, NH 03833
(617) 494-1722
Fax (617) 494-1822
[EMAIL PROTECTED]
-----Original Message-----
From: Mike Tintner [mailto:[EMAIL PROTECTED]
Sent: Thursday, October 11, 2007 7:53 PM
To: [email protected]
Subject: Re: [agi] Do the inference rules.. P.S.
Vladimir: ..and also why can't 3D world model be just described abstractly,
by
> presenting the intelligent agent with bunch of objects with attached
> properties and relations between them that preserve certain
> invariants? Spacial part of world model doesn't seem to be more
> complex than general problem of knowledge arrangement, when you have
> to keep track of all kinds of properties that should (and shouldn't)
> be derived for given scene.
>
Vladimir and Edward,
I didn't really address this idea essentially common to you both, properly.
The idea is that a network or framework of symbols/ symbolic concepts can
somehow be used to reason usefully and derive new knowledge about the
world - a network of classes and subclasses and relations between them, all
expressed symbolically. Cyc and Nars are examples.
OK let's try and set up a rough test of how fruitful such networks/ models
can be.
Take your Cyc or similar symbolic model, which presumably will have
something like "animal - mammals - humans - primates - cats etc " and
various relations to "move - jump - sit - stand " and then "jump -
on - objects" etc etc. A vast hierarchy and network of symbolic concepts,
which among other things tell us something about various animals and the
kinds of movements they can make.
Now ask that model in effect: "OK you know that the cat can sit and jump on
a mat. Now tell me what other items in a domestic room a cat can sit and
jump on. And create a scenario of a cat moving around a room."
I suspect that you will find that any purely symbolic system like Cyc will
be extremely limited in its capacity to deduce further knowledge about cats
or other animals and their movements with relation to a domestic room - and
may well have no power at all to create scenarios.
But you or I, with a visual/ sensory model of that cat and that room, will
be able to infer with reasonable success whether it can or can't jump, sit
and stand on every single object in that room - sofa, chair, bottle, radio,
cupboard etc etc. And we will also be able to make very complex assessments
about which parts of the objects it can or can't jump or stand on - which
parts of the sofa, for example - and assessments about which states of
objects, (well it couldn't jump or stand on a large Coke bottle if erect,
but maybe if the bottle were on its side, and almost certainly if it were a
jeroboam on its side). And I think you'll find that our capacity to draw
inferences - from our visual and sensory model - about cats and their
movements is virtually infinite.
And we will also be able to create a virtually infinite set of scenarios of
a cat moving in various ways from point to point around the room.
Reality check: what you guys are essentially advocating is logical systems
and logical reasoning for AGI's - now how many kinds of problems in the real
human world is logic actually used to solve? Not that many. Oh it's an
important part of much problemsolving but only a part. How much scientific
problemsolving depends seriously on logic? Is logic going to help you
understand and have ideas about genetics or how cells work, or the brain
works, or how and why wars start? Is it going to be much use for design
problems? Does it help in telling stories? .. keep on going through the vast
range of human and animal problemsolving (all of which remember are the ONLY
forms of [A]GI that actually work).
That's why I asked you: give me some examples of useful new knowledge or
analogies [especially analogies] that have been derived from logical systems
or logic, period (except about logic itself).
New knowledge - especially new science - comes primarily from new
observation of the world, not from logically working through old knowledge.
Artificial general intelligence - the ability to develop new, unprogrammed
solutions to problems - depends on sensory models and observations.
Let me be brutally challenging here : the reason you guys are attached to
purely symbolic models of the world is not because you have any real
evidence of their being productive (for AGI), but because they're what you
know how to do. Hence Vlad's "why can't 3D world model be just described
abstractly.." He doesn't know - he just hopes - that it can. Logically.
What you need here is not logic but - ahem - evidence {sensory stuff].
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