RE: [xtm-wg] Re: Inquiry Into Inquiry

["Paul Stephen Prueitt" <bcngroup@erols.com>]

John Sowa states the precepts of strong AI very well. 

 

My questions, to him and to the participants, are questions of relevance and adequacy.

 

First question.  Is the separation of information into just two specific (non-overlapping) categories, declarative and procedural, adequate to the development of a computational paradigm that supports every day human individual knowledge management?

 

I think that the answer is no.  

 

My answer does not reflect JUST one person's opinion, but is something that can be grounded in empirical evidence and structured reasoning (human reasoning).  But this discussion is **not allowed** (read **not-funded**) simply by the dominance of the strong AI academic and scholarly disciplines.  This dominance is not based on results, I hold; but rather in denial of a clear and obvious truth. 

 

I also hold that the results of AI do not accrue to the understanding of how to build knowledge technologies for non-computer scientists. AI has become not relevant because it is not adequate to the biological and social processes involved in knowledge sharing.  Computers for data storage and communication is of value.  This is not the claim.  The claim is that AI is a false paradigm that has long ago lost it's legitimacy. 

 

John Sowa's note is read in my mind as " well yes there is no empirical grounding of AI in any real science.  We in the AI community have long ago decided that the empirical biological science means nothing to us." 

 

Is this really the position that is taken - or am I mistaken?

 

The **Value Proposition** is that a real knowledge technology is possible, but only after the strong AI paradigm is buried and a wooden stack put through it's heart.

 

A second **Value Proposition** exists for AI.  This value proosition is  IF AI is properly understood as part of the Science of the Artificial (see Herbert Simon's book), then its value to society will be enhanced.  So in computer security systems AI should have a high vlaue, higher than is recognized today.

 

AI science is defined by the AI scholars as being a inquiry into " What are the logical foundations of learning and reasoning."  This definition highlights the issue exactly. 

 

Second Question.  Where does the AI community obtain the right to ground its use of the terms "learning" and "reasoning" .  There is the terminology use AS IF one is to imagine that this is of the nature of human learning processes and human reasoning processes?    The meaning of the term "learn" is altered.

 

It is in a scientific literature that has been walked away from by the scholars of that discipline (cognitive neuroscience) and in a logic literature that the logicians should have walked away from since the time of Godel and Church.  The grounding is in the history of Western philosophy, and this history has long ago become mostly a mental exercise in determining how many Angels dance on the head of a pin.

 

We MUST move beyond.  The old foundation is gone.  A new foundation is possible.  (Why not?)

 

Will you help us?

 

 

 

 

-----Original Message-----
From: John F. Sowa [mailto:sowa@bestweb.net]
Sent: Thursday, July 26, 2001 6:13 AM
To: Paul Stephen Prueitt
Cc: OntologyStream; Jon Awbrey; W.M. Jaworski; Xtm-Wg; Steve Pepper; Steven R. Newcomb; Brian (Bo) Newman; Stand! Unfold! Ontology!; Arisbe; Robert Shaw; Ray Fergerson; 'Com-Prac
Subject: [xtm-wg] Re: Inquiry Into Inquiry

Paul Stephen Prueitt wrote:

> I ask John Sowa to make a comment about the fact that Tulving has declared
> his former views, regarding the distinction between semantic and episodic
> memory, as being a distinction that has been found to be lacking.  Are we
> simply to ignore this history?

I never regarded it as a crucial distinction upon which everything
else stands or falls.  In AI, the distinction between the definitional
networks and the assertional networks has been a useful way of dividing
up the task and organizing the various pieces of the puzzle.

And in fact, the approach that I have been developing in recent years
can be interpreted in different ways, some of which could be viewed
as supporting either position.

> If science cannot walk away from an establish paradigm, when evidence sets
> it aside, then why have a notion of falsification at all?

There were never any claims that could be or have been falsified.
The AI systems use both definitions and assertions.  Any particular
proposition that follows from the conjunction of both would still
be derived whether the two kinds of information were stored separately
or together.

There are several distinct fields:

1. Neuropsychology:  How do human and animal brains work?

2. AI science:  What are the logical foundations of learning
    and reasoning?

3. AI engineering:  How does one build intelligent machines that
    learn and reason effectively?

Each of these fields has had some influence on each of the others,
but no particular result from any one of them necessarily contradicts
any result of any of the others.  For example, you may have logical
theories that are inefficient in any neural or silicon implementation.
Or you could find others that are very good candidates for one, but not
the other.

John Sowa

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