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Re: [ontolog-forum] Foundations for Ontology

To: ontolog-forum@xxxxxxxxxxxxxxxx
From: "John F. Sowa" <sowa@xxxxxxxxxxx>
Date: Fri, 07 Oct 2011 10:21:53 -0400
Message-id: <4E8F0B01.7020604@xxxxxxxxxxx>
On 10/7/2011 12:52 AM, Rob Freeman wrote:
> On the subject of foundational issues. Here's a website I found just
> this week (via a random search for "vector language models" on Google+
> of all things):
> http://artificial-intuition.com/
> I submit it for the broader information of the Ontolog community, as
> a separate development along similar themes to my own (without the
> "intuition" label please, but hey, labels have their place.)    (01)

Actually, what I like most about that web site is the word 'intuition'.
Another word I like even better is 'imagination' -- because it clearly
emphasizes the word 'image'.  As I said in the ontofound.pdf slides,
images and mental models are the foundation for human language.    (02)

I certainly agree that something like intuition or imagination is
essential for truly human-like intelligence.  And my quotations from
Paul Halmos and Albert Einstein emphasize the importance of intuition
for *discovery* and *innovation* in mathematics, science, and *life*.    (03)

I hadn't heard of Monica Anderson before, but her CV shows that she has
a solid programming background with a lot of experience in using and
developing AI software (including writing an interface to Cyc):    (04)

    http://syntience.com/andersonresume.pdf    (05)

I agree with the following points:    (06)

> Several disciplines such as Chaos Theory, Complexity Theory, and the new
> discipline of Systems Biology concern themselves with complex systems
> of all kinds. No matter which direction you approach from, you will soon
> discover a set of underlying problem types that make these fields resist
> analysis and modeling efforts using the established methods of conventional
> Science...
> Intuition is Theory-free. It does not require a high-level logical model.
> This neatly solves a bootstrapping problem of Artificial Intelligence.
> You cannot create high-level models until you already have Intelligence.
> Intuition based skills improve with practice, whereas Logic based
> skills  do not...
> Intuition also has severe limitations; some of these mirror the
> advantages of Logic based systems...    (07)

I also appreciate her candor about the results:    (08)

> I am currently exploring Artificial Intuition based methods. The performance
> I can achieve with the hardware and the algorithms I have is unspectacular.
> All my results to date can be rather easily duplicated using conventional
> programming methods. I like to say I have results but no demonstration,
> and the difference is that a result is impressive to someone who understands
> how it was achieved, whereas a demo is impressive to anyone whether or not
> they understand the technology.    (09)

This quotation is from http://artificial-intuition.com/possible.html
That same page has various diagrams, which I don't disagree with.  But
I prefer Minsky's, which shows similar kinds of tradeoffs *and* relates
them to the kinds of software methods.  See slide 25 of    (010)

    http://www.jfsowa.com/talks/ca4cs.pdf    (011)

Note that Minsky shows all the currently popular methods, and he shows
their strengths and limitations.  I believe that *all* of the methods
in his diagram are valuable for many purposes.  But I also agree that
none of them, by itself, can support everything.  And I agree that
"we must find a better representation!"    (012)

I have been saying that in many different ways in many different talks,
publications, and emails.    (013)

John    (014)

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