As I see it, the meaning of a word can be understood as the fuzzy set
of patterns in which that word is involved...

Some of these will be purely language-internal patterns (as
highlighted by Saussure and other structuralist linguists way back
when), others will be patterns associating the word with nonlinguistic
data (as in symbol groundings)

One way of thinking about the relationships between these different
aspects of meaning is given here:

https://arxiv.org/abs/1703.04368

-- Ben

On Thu, Apr 20, 2017 at 2:23 AM, Daniel Gross <gross...@gmail.com> wrote:
> Hi Ben,
>
> Thank you for your response. I started reading the paper and was wondering
> if you could help me clarify a confusion i apparently have when it comes to
> the meaning of meaning:
>
> How is linguistic meaning connected to human embodied meaning that we would
> call human (or AGI) understanding.
>
> Linguistic meaning seems to be about the linguistic meta-language that shows
> how a human would parse a sentence unambiguously, so that a human can, in
> principle, understand the meaning of a sentence, although, what is, say,
> instructed by a sentence, as understood by a human seems not captured, but
> would require more machinery.
>
> In this sense, linguistic machinery seems to embody (as a theory of mind)
> how humans understand (in a cognitive economical manner), rather than what
> humans understand --at least this is what confuses me ...
>
> any thought would be much appreciated ...
>
> thank you,
>
> Daniel
>
>
>
>
>
> On Wednesday, 19 April 2017 09:16:42 UTC+3, Ben Goertzel wrote:
>>
>> We have a probabilistic logic engine (PLN) which works on (optionally
>> probabilistically labeled) logic expressions....   This logic engine
>> can also help with extracting semantic information from natural
>> language or perceptual observations.  However, it's best used together
>> with other methods that carry out "lower levels" of processing in
>> feedback and cooperation with it...
>>
>> In the case of vision, Ralf Mayet is leading an effort to use a
>> modified InfoGAN deep NN to extract semantic information from
>> images/videos/sounds to pass into PLN, the Pattern Miner, and so forth
>>
>> In the case of textual language, Linas is leading an effort to extract
>> a first pass of semantic and syntactic information from unannotated
>> text corpora via this general approach
>>
>> https://arxiv.org/abs/1401.3372
>>
>> The same approach should work when non-textual groundings are included
>> in the corpus, or when the learning is real-time experiential rather
>> than batch-based.... but there's plenty of nitty-gritty work here...
>>
>> ben goertzel
>>
>> On Wed, Apr 19, 2017 at 7:23 AM, Daniel Gross <gros...@gmail.com> wrote:
>> > Hi Linas,
>> >
>> > How do you propose to learn an ontology from the data -- also, what
>> > purpose
>> > would, in your opinion, the learned ontology serve. Or stated
>> > differently,
>> > in what way are you thinking to engender higher-level cognitive
>> > capabilities
>> > via machine learned bundled neuron (and implicit ontologies, perhaps).
>> >
>> > thank you,
>> >
>> > Daniel
>> >
>> >
>> > On Wednesday, 19 April 2017 03:40:47 UTC+3, linas wrote:
>> >>
>> >>
>> >>
>> >> On Tue, Apr 18, 2017 at 3:22 PM, Alex <alexand...@gmail.com> wrote:
>> >>>
>> >>> Maybe we can solve the problem about modelling classes (and using OO
>> >>> and
>> >>> UML notions for knowledge representation) with the following
>> >>> (pseudo)code
>> >>>
>> >>> - We can define ConceptNode "Object", that consists from the set or
>> >>> properties and functions
>> >>>
>> >>> - We can require that any class e.g. Invoice is the inherited from the
>> >>> Object:
>> >>>   IntensionalInheritanceLink
>> >>>     Invoice
>> >>>     Object
>> >>>
>> >>> - We can require that any more specifica class, e.g. VATInvoice is the
>> >>> inherited from the more general class:
>> >>>   IntensionalInheritanceLink
>> >>>     VATInvoice
>> >>>     Invoice
>> >>>
>> >>> - We can require that any instance is inherited from the concrete
>> >>> class:
>> >>>   ExtensionalInheritanceLinks
>> >>>     invoice_no_2314
>> >>>     VATInvoice
>> >>
>> >>
>> >> If you wish, you can do stuff like that. opencog per se is agnostic
>> >> about
>> >> how you do this, you can do it however you want. The proper way to do
>> >> this
>> >> is discussed in many places; for example here:
>> >> https://en.wikipedia.org/wiki/Upper_ontology
>> >>
>> >> I'm not particularly excited about building ontologies by hand, its
>> >> much
>> >> more interesting (to me) to understand how they can be learned
>> >> automatically, from raw data.
>> >>>
>> >>>
>> >>> But I don't know yet what can and what can not be the parent for
>> >>> extensional and intensional inheritance. Can an entity be
>> >>> extensionally
>> >>> inherited from the more complex object or it can be extensionally
>> >>> inherited
>> >>> from empty set-placeholder only. When we introduce notion of set, then
>> >>> the
>> >>> futher question always arise - does OpenCog make distinction between
>> >>> sets
>> >>> and proper classes?
>> >>
>> >>
>> >> Why? This "distinction" only matters if you want to implement set
>> >> theory.
>> >> My pre-emptive strike to halt this train of thought is this: Why would
>> >> you
>> >> want to implement set theory, instead of, say, model theory or
>> >> universal
>> >> algebra, or category theory, or topos theory?  why the heck would
>> >> distinguishing a set-theoretical-set from a
>> >> set-theoretical-proper-class
>> >> matter? (which oh by the way is similar but not the same thing as a
>> >> category-theoretic-proper-class...)
>> >>
>> >> You've got multiple ideas going here, at once: the best way to
>> >> hand-craft
>> >> some ontology; the best theoretical framework to do it in; the
>> >> philosophy of
>> >> knowledge representation in general... and, my personal favorite: how
>> >> do I
>> >> get the machine to do this automatically, without manual intervention?
>> >>
>> >>>
>> >>>
>> >>> There is second problem as well - there is only one - mixed
>> >>> InheritanceLink. One can use SubsetLink for the extensional
>> >>> inheritance
>> >>> (still it feels strange), but there is certainly necessary syntactic
>> >>> sugar
>> >>> for intensional inheritance, because it is hard to write and read
>> >>> SubsetLink
>> >>> of property sets again and again
>> >>> (http://wiki.opencog.org/w/InheritanceLink).
>> >>
>> >>
>> >> If the machine has learned an ontology with a million subset links in
>> >> it,
>> >> no human being is ever going to read or want to read that network.
>> >> It'll be
>> >> like looking at a bundle of neurons: the best you can do is say "oh
>> >> wow, a
>> >> bundle of neurons!"
>> >>
>> >> --linas
>> >>>
>> >>>
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>>
>> --
>> Ben Goertzel, PhD
>> http://goertzel.org
>>
>> "I am God! I am nothing, I'm play, I am freedom, I am life. I am the
>> boundary, I am the peak." -- Alexander Scriabin
>
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-- 
Ben Goertzel, PhD
http://goertzel.org

"I am God! I am nothing, I'm play, I am freedom, I am life. I am the
boundary, I am the peak." -- Alexander Scriabin

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