Hi Linas, 

I think you "morphism" example is very interesting and just to emphasize a 
key insight -- context. 

In context of A one morphism may hold, in context B another -- and you 
indicated two kinds of contexts, ) domains (swimming, rowing) and 
human-introspective-valueladen interpretive context. 

thank you,

Daniel

On Friday, 21 April 2017 18:02:03 UTC+3, linas wrote:
>
> Ivan,
>  what I wanted to say is that meaning depends not only on the language, 
> but also on the person, and it changes over time. Most people agree on the 
> meanings of most words, most of the time, but not always.  Best example is 
> the slang of some subculture. If the subculture is a gang, there moght only 
> be 10 or 20 people in the world who understand and mostly agree on the 
> meanings of some of the words that the gang uses. And even then, they might 
> not agree, due to some confusion.
>
> Yes, meaning might be a morphism: (a morphism being an arrow between two 
> things, what you called "source" and "target")  Meaning might be more than 
> just a morphism between concdpts or words; it may be a morphism between 
> structures. In the context of "rowing", the word "catch" corresponds to a 
> specific set of physical movements.  In the context of "swimming", it means 
> something conceptually similar, but physically not the same.  In the 
> context of "baseball", it's completely different.
>
> On Thu, Apr 20, 2017 at 1:53 PM, Ivan Vodišek <ivan....@gmail.com 
> <javascript:>> wrote:
>
>> Yes Linas, thank you for response. That is why there is no exclusively 
>> definite interpretation of any expression. Expression "space" can be 
>> translated to numerous meanings, with each meaning having its own, slightly 
>> different interpretation in its own language. If we think about 
>> "Multiverse", notion "space" could look differently in each Universe 
>> sourcing from Multiverse (I hope my imagination doesn't spoil my 
>> arguments). If we tie semantics not only to starting expression, but also 
>> provide the second parameter (target), we have opportunity to define 
>> semantics as a function of two parameters: source Universe and target 
>> Universe. And even when we reach target Universe, there are options to 
>> define ambiguities of a single target expression, I agree.
>>
>> So the question we have to ask when we seek for *a* semantics of an 
>> expression should have the following form: What does expression X in 
>> language A means in language B, C or, maybe, D?
>>
>> Ivan
>>
>>
>> 2017-04-20 20:30 GMT+02:00 Linas Vepstas <linasv...@gmail.com 
>> <javascript:>>:
>>
>>> Ivan, I mostly agree (superficially) with most of what you are saying, 
>>> but: I notice you avoid or over-simplify the issues mentioned in the 
>>> wikipedia article "upper ontology".  The points are two fold: different 
>>> human beings have subtley different "upper ontologies", they tend to change 
>>> over time, they are often logically inconsistent, and they are strongly 
>>> tied to mood, alertness, voluability, life-experiences, culture, language.  
>>> The way that Russians, Americans and Chinese think about "outer space" is 
>>> different: not only is there no direct word-for-word translation for this 
>>> concept, but its worse: different people put different emphasis on what is 
>>> important about space, what its important defining characteristics are.  
>>> For some people, "space is infinite", for other people, "space is where 
>>> star trek happens", for others still "space is boring, inner space is what 
>>> we should explore".  So the "meaning" of the word "space" depends on the 
>>> individual, and on their identity, their "value system" (what they consider 
>>> to be important) and their *political* perspective.  Overtly political, 
>>> even: "space should be conquered, and the conqueror gets to put their 
>>> national flag on it, and claim all economic extractive rights". So what is 
>>> "space", really?
>>>
>>> --linas
>>>
>>> On Thu, Apr 20, 2017 at 12:25 PM, Ivan Vodišek <ivan....@gmail.com 
>>> <javascript:>> wrote:
>>>
>>>> Hi all :)
>>>>
>>>> May I say a few words about semantics? In my work on describing 
>>>> knowledge, I've concluded that a semantics (meaning) of an expression is 
>>>> merely an abstract concept of thought that relates the expression to its 
>>>> interpretation in another (or the same) language for which we already know 
>>>> its interpretation. Let's say we have unknown language A and already known 
>>>> language B in which the language A can be expressed. To know semantics of 
>>>> our language A (in the terms of B) is to know how to translate language A 
>>>> to language B, under assumption that we already know semantics of B.
>>>>
>>>> If we think about it in a natural way, how do we explain to someone a 
>>>> meaning of some expression? What we do in this situation is that we 
>>>> actually translate the expression unknown to that person to a form that is 
>>>> known by that person. For example, how do we explain in known language 
>>>> what 
>>>> some word from another language means? We simply show how to translate it. 
>>>> As simple as that. 
>>>>
>>>> So, one might pose a question: "If semantics are all relative to the 
>>>> next member in the chain, what are semantics of the ending chain member?" 
>>>> On this thought, all I have right now are indices that the ending chain 
>>>> member is the Universe itself. If this is true, then every conceivable 
>>>> thought has its interpretation as an system inside Universe, with all of 
>>>> its static or dynamic states. Once we can picture out how to translate an 
>>>> expression to an Universe system, we can say that we know semantics of 
>>>> that 
>>>> expression. And the meaning of the Universe system itself? Sorry, don't 
>>>> ask 
>>>> me, I didn't create it, the thing is rolling on on its own :)
>>>>
>>>> Moving further with a train of thought, how do we stand with logic 
>>>> conclusions? We translate a set of logic formulas to another new sets of 
>>>> logic formulas, which could be interpreted as like we give meanings to 
>>>> starting logic formulas. Take a look at this sentence: If it rains, it 
>>>> means that streets are wet. We used the word "means". So, with a proper 
>>>> set 
>>>> of translation rules, we can give meanings to languages, we can draw 
>>>> logical conclusions, and, from what I've seen so far in my research, we 
>>>> can 
>>>> build whole imaginary systems that can emulate real Universe systems. 
>>>> Shortly, a set of translation rules can be seen as a knowledge base about 
>>>> some situation that is possible to exist inside the Universe.
>>>>
>>>> Not to stay just on words, I develop a programming language whose 
>>>> mandatory function is to make easier development at the field of 
>>>> artificial 
>>>> intelligence. As it is all about states in the Universe, and the real life 
>>>> situations are about those states, that language could be used for 
>>>> programming regular applications as well. The language is working 
>>>> embodiment of an universal rewrite system 
>>>> <https://en.wikipedia.org/wiki/Rewriting> and has some cute 
>>>> properties, complete enough for programming and concluding new knowledge, 
>>>> being system transformation, abduction, deduction or induction. I'll try 
>>>> to 
>>>> inform Opencog community about the progress of my work because I believe 
>>>> that AGI world could benefit from such an investigation in a field of 
>>>> representing knowledge. I hope, at least that the language would be an 
>>>> inspiration for a lucid AGI developer.
>>>>
>>>> - ivan -
>>>>
>>>>
>>>>
>>>> 2017-04-20 18:19 GMT+02:00 Daniel Gross <gros...@gmail.com 
>>>> <javascript:>>:
>>>>
>>>>> Hi Linas, 
>>>>>
>>>>> Thank you for your responses, and the pointer. 
>>>>>
>>>>> It seems to me that your example further pin-points my question:
>>>>>
>>>>> A quasi-linear walk through a semantic network is essentially a 
>>>>> constructed structure (or path) through the use of grammar, to get at a 
>>>>> possible reading of a sentence that would make sense to a person within a 
>>>>> "semantic space", without however capturing meaning per-se. A lexicon, 
>>>>> say, 
>>>>> "merely' captures the rules of constructions of particular given verbs 
>>>>> and 
>>>>> nouns *based* on their human interpreted meaning).
>>>>>
>>>>> Hence, grammar's purpose seems to really "only" to construct a 
>>>>> meanginful path rather than tell us what the meaning of the knowledge 
>>>>> embodied in that path is. The latter seems to require another "kind" of 
>>>>> semantics/meaning (and perhaps some might say that there are turtles all 
>>>>> the way down -- or at least until some grounding). 
>>>>>
>>>>> does my intuition make sense, 
>>>>>
>>>>> thank you,
>>>>>
>>>>> Daniel
>>>>>
>>>>>
>>>>> On Thursday, 20 April 2017 16:59:38 UTC+3, linas wrote:
>>>>>>
>>>>>> 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 <gros...@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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>>>>>>>>  
>>>>>>>>
>>>>>>>> >>> 
>>>>>>>> >>> For more options, visit https://groups.google.com/d/optout. 
>>>>>>>> >> 
>>>>>>>> >> 
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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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