Re: [ontolog-forum] New Postulate formulation on AI KR

Dear Paola,
You are right about some points in your postulate, but as you yourself indicated it must be refined to circumvent and/or avoid the fiery debate about whether symbolic (at the heart of mathematical description) and sub symbolic (closer to natural language and philosophical concepts and mental constructs) should be central in algorithms.
The ancients did not use separate symbols or semiotics, but loosely or tightly defined natural language or philosophical descriptions.
Epistemology deals with this.
Google "Epistemology in Artificial Intelligence" and you will find some ideas to refine your postulate.

Milton Ponson
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    On Tuesday, August 27, 2019, 12:06:02 AM ADT, Paola Di Maio <paola.dimaio@gmail.com> wrote:  
 
 Thanks, yes!
On Tue, Aug 27, 2019 at 8:58 AM joseph simpson <jjs0sbw@gmail.com> wrote:

All:
The statement:
"Interdisciplinarity is fundamental to problem solving in most domains, and applied science and engineering are concerned primarily with problem solving, rather than purely theoretical formulations"
Appears to relate directly to General Systems Theory.  See:
https://www.researchgate.net/publication/323387658_A_General_Systems_Context_for_Structural_Modeling
https://www.panarchy.org/boulding/systems.1956.html
The proper alignment of natural language and formal language may be addressed in a range of view points.
Some issues and ideas are outlined at:
https://www.researchgate.net/publication/324138184_System_Concepts_and_Theories
Take care, be good to yourself and have fun,
Joe






On Mon, Aug 26, 2019 at 1:42 AM Paola Di Maio <paola.dimaio@gmail.com> wrote:

Thank you Alex
variations/elaborations  welcome (Vx)
There is a problem with math KR tho - which has been discussed foreverI ll summarise it briefly hoping to put things to rest:
Interdisciplinarity is fundamental to problem solving in most domains, and applied science and engineering are concerned primarily with problem solving, rather than purely theoretical formulations
Interdisciplinarity means not everyone uses maths (  apparently even mathematicians sometimes use different types of maths) Most domain experts are not mathematicians
Therefore, maths, like ml should also be translatable/mappable to to plain natural language, even better to some diagrammatic notation  or logical language that reproduces the logical schema ideally, problems and solutions should be represented in a multiplicity of ways
This particular postulate - luckily I dont wake up early morning to postulate very often - has been prompted but the need to fill a gap between symbolic an subsymbolic KR as per the posts, and having a citable reference I am sure it can be refined furtherPDM


On Mon, Aug 26, 2019 at 4:29 PM Alex Shkotin <alex.shkotin@gmail.com> wrote:

Paola,

I keep in mind a variation of your postulate:
Any algorithm (including MLA) has math representation.

This is all that we have been talking about since Leibniz: math knowledge representation:-)
The problem is that computer programs and DBs are not mathematical structures but they work:-)

Alex  

пн, 26 авг. 2019 г. в 05:27, Paola Di Maio <paola.dimaio@gmail.com>:

Since posting  discussions about bridging the gap between symbolic and subsymbolic, (see related posts) I have formulated a simple postulate, and assigned a DOI so that anone following up on these discussions can cite the contribution accordingly. I ll be happy to review the postulate with comments and suggestions if any
To support AI explainability, learnability,verifiability and reproducibility, it is postulated thatfor each MLA *machine learning algorithm, there should correspond a natural language expression or other type of symbolic knowledge representation

https://doi.org/10.6084/m9.figshare.9730268.v2  
https://figshare.com/articles/A_New_Postulate_for_Knowledge_Representation_in_AI/9730268  



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