Semantics and syntax are two different things. Syntax allows you to parse
sentences. Semantics is more about how concepts inter-relate to each other.
--  a network. A sentence tends to be a quasi-linearized walk through such
a network. For example, take a look at the "deep" and the "surface"
structures in meaning-text theory.  From there, one asks "what kind of
speech acts are there?"  and "why do people talk?" and this is would be the
"next level", beyond the homework exercise I mentioned in the previous
email.

--linas

On Wed, Apr 19, 2017 at 7:23 PM, Daniel Gross <[email protected]> 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 <[email protected]> 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 <[email protected]> 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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