Here is another spiffy quote from PG:
PG: Conceptual spaces can also
provide a better way of representing learning in general and concept formation
in particular than what can be achieved on the symbolic level.
For Gaerdenfor, the phrase "conceptual spaces"
refers to "intrinsic" senses scattered across an array of (cortical
columns)", if I may so freely interpret his intent against his terminology.
He cites examples like the Cochlea's scattering by frequency of harmonics,
visual color limits to RGB cones and rods, muscular extension, and so forth, each
representing a scattering dimension. He uses this finiteness
of scattering along the neocortex (again I assisted his interpretation) to
conclude that induction over domains of infinities is biologically illegitimate:
Many of the problems of
induction that are created by the symbolic approach dissolve into thin air when
analysed on the conceptual level. Similarly, the problem of how transducers
work becomes a non-problem since no transducers are needed for the information
represented in conceptual spaces.
RC: He seems to say that specialized processing in neural
compartments (noncortical brain areas) is very limited, so the dimensions are
scattered across dimensions of each of the cortices, and the brain does the
rest. I suppose that is partly the connectionist image.
But it also shows that the designation of any symbol used
by the patient is localized by name or description in the appropriate cortical
column(s) so it can be referenced linguistically.
PG: The theory of conceptual
spaces may also indicate a direction where a solution to the frame problem can
be ferreted out. The starting point is to separate the information to be represented
into domains. The combinatorial explosion of symbolic representations of a changing
world is a result of not keeping symbolic information about different domains separated.
RC: There is some truth to that, but also lots of work left
to do being stated. Basically, database models use the primitive domains
(integer, real, Boolean, char, string, ...) and don't build more object-oriented
domains that might be more intuitively understandable by the salient
crowd.
But to do so leaves you with only a snapshot of the data
model at that point in time, while the actual data model varies with new or
updated requirements changes.
Since the need for those more detailed views of the
database are merely conceptual, it doesn't help the bankers of the system justify
funding such work. So that level of detail is usually not
completed.
So, if we (Tonto) can map the fruit fly sensor qualities
and their neurons onto their cortical locations (assuming they have cortices
big enough to do so), perhaps we can even relate that to some primitive functionality
in the brain which corresponds to humans. If so, then we (Tonto) have
that fly model of related phenomenon to work on.
Sincerely,
Rich
Cooper,
Rich Cooper,
Chief Technology Officer,
MetaSemantics Corporation
MetaSemantics AT EnglishLogicKernel DOT com
( 9 4 9 ) 5 2 5-5 7 1 2
http://www.EnglishLogicKernel.com
-----Original Message-----
From: ontolog-forum-bounces@xxxxxxxxxxxxxxxx
[mailto:ontolog-forum-bounces@xxxxxxxxxxxxxxxx] On Behalf Of John F Sowa
Sent: Monday, May 25, 2015 1:30 PM
To: ontolog-forum@xxxxxxxxxxxxxxxx
Subject: Re: [ontolog-forum] Fruit fly emotions mimic human emotions - ontology
discovery possible?
Tom,
I am well aware of those debates and of the intensity on
all sides:
> This is the “vs.” I am referring to, and
in spite of your “should”,
> the facts on the current ground is that there is
this debate. Indeed,
> the article by Fodor and Lepore and the reply by
David Chalmers, both
> of which you recently provided links to, make it
quite clear how
> intense the “vs.” remains.
The most cutthroat debates are among philosophers and
theologians -- primarily because they're searching for certainty, and they have
no way of knowing when they're wrong.
That was the point of my talk at the Mexican AI
conference in November:
http://www.jfsowa.com/talks/micai.pdf
Why has AI failed? And how can
it succeed?
That wrangling led to single-paradigm systems, which are
very strong on one type of problem and useless for anything else.
> Here's Gardenfors, on my "vs.":
> “Within cognitive science, there are currently
two dominating
> approaches to the problem of modeling
representations.” From the point
> of view of the symbolic approach (which I and others
call the “mental
> representation” approach), “cognition is
seen as essentially being
> computation, involving symbol manipulation.”
I presented a guest lecture at Lund at PG's invitation,
so I won't be too harsh on him. Peter did good work on belief revision,
which I strongly recommend. He's the G of the AGM axioms. But that
quotation is an extremely oversimplified and misleading summary of AI and
cognitive science.
Marvin Minsky's _Society of Mind_ is a good antidote to
that kind of partisanship. See the reference in Slide 13 of micai.pdf:
http://web.media.mit.edu/~push/CognitiveDiversity.pdf
That was a strong influence on my "Flexible Modular
System" (FMF):
http://www.jfsowa.com/pubs/arch.pdf
A quotation from arch.pdf
> The lack of progress in building general-purpose
intelligent systems
> could be explained by several different hypotheses:
>
> * Simulating human intelligence on a digital
computer is impossible.
>
> * The ideal architecture for true AI has not
yet been found.
>
> * Human intelligence is so flexible that no
fixed architecture can do
> more than simulate a single aspect
of what is humanly possible.
>
> Many people have presented strong, but not
completely convincing
> arguments for the first hypothesis. In the
search for an ideal
> architecture, others have implemented a variety of
at best partially
> successful designs. The purpose of this paper is to
explore the third
> hypothesis: propose a flexible modular
framework that can be tailored
> to an open-ended variety of architectures for
different kinds of
> applications.
For examples that show how the FMF works, see "Two
paradigms are better than one, and multiple paradigms are even better":
http://www.jfsowa.com/pubs/paradigm.pdf
Fundamental principle: Neuroscientists are the
first to emphasize that
*nobody* really knows how the brain works. For
philosophers to engage in endless wrangling about the virtues of one half-baked
theory or another is fundamentally misguided.
Recommendation in micai.pdf: Implement various
theories. Test them alone and in different combinations. See what
works. Collaborate!
John
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