Re: KR for Cogai/gentle reminder

I would like to point out that KR is one of the central themes for the entire field commonly known as artificial intelligence.

What is a Knowledge Representation?
A perspective from the MIT AI Lab, MIT AI Lab and Symbolics, Inc. and MIT Lab for Computer Science
http://groups.csail.mit.edu/medg/people/psz/ftp/k-rep.html
So what we are doing in the AIKR W3 CG is basically a SUBSET of every other AI CG in the W3 Community Groups

Now a basic tenet of scientific dialogue is the possibility to disagree upon terminology, scope and findings, results and even theories.
The biggest problem in AI today is that we cannot even agree upon what actually is AI, what it should be and what are its main characteristics, and unfortunately this also applies to knowledge representation.
But because every field of scientific endeavor and engineering nowadays utilizes AI, and every field has its own knowledge that needs formal representation AIKR is at the core of all of this.
I sense that the CogAI focuses of the cognitive processes involved in the creation of knowledge and how to best capture this in formal representation, based upon their description of objectives.
So Paola is PARTIALLY right in trying to separate the work being done.
But let's not waste the possible synergies to be gained. We could TOGETHER produce deliverables (reports, articles) and the central role of KR in AI, and how this relates to cognitive processes that are also central to all AI.
Let's define this common ground and define the possible common objectives and potential deliverables. Because to quote the European Union, objectives for open, inclusive, explainable and ethical AI also presuppose open , inclusive, explainable and ethical knowledge and consequently cognitive processes and underlying architectures for such.
I have tasked myself with providing an overview of what is AI, using a timeline, with a concise summary of academic fields involved and how the EU objectives can be achieved.
Anyone willing to collaborate is welcome to contact me.
I have a vested personal interest to utilize AI for the common good  defined in sustainable development guidelines of the UN as well, because AI could be instrumental in tackling seemingly insurmountable problems like climate change, and other global issues plaguing our modern world.
Let's agree to be able to disagree, but not let it stand in our way to collaborate.

Milton Ponson
GSM: +297 747 8280
PO Box 1154, Oranjestad
Aruba, Dutch Caribbean
Project Paradigm: Bringing the ICT tools for sustainable development to all stakeholders worldwide through collaborative research on applied mathematics, advanced modeling, software and standards development 

    On Friday, October 28, 2022 at 11:28:23 PM AST, Adeel <aahmad1811@gmail.com> wrote:  
 
 Hello,
extract from the book:
"
Show that minimizing abnormality will work if we add the

assertion



All Québecois are abnormal Canadians,

but will not work if we only add







Québecois are typically abnormal Canadians.



"




That's harsh... LOL







On Sat, 29 Oct 2022 at 03:32, Adeel <aahmad1811@gmail.com> wrote:

Hello,
Perhaps, Paola is referring to the theory in this book -> Brachman and Levesque
Thanks,
Adeel
On Sat, 29 Oct 2022 at 03:06, Timothy Holborn <timothy.holborn@gmail.com> wrote:

Noted. 
https://en.wikipedia.org/wiki/Knowledge_representation_and_reasoning

In terms of knowledge representation, for humanity, my thoughts have been that it's about the ability for people to represent the evidence of a circumstance in a court of law.  If solutions fail to support the ability to be used in these circumstances, to successfully represent knowledge - which can be relied upon in a court of law; a circumstance that should never be wanted, but desirable to support peace.
Then, I guess, I'd be confused about the purposeful definion; or the useful purpose of any such tools being produced & it's relationship, by design, to concepts like natural justice. 
https://en.wikipedia.org/wiki/Natural_justice
Let me know if I am actually "off topic" per the intended design outcomes.
Regards,
Timothy Holborn.

On Sat, 29 Oct 2022, 11:55 am Paola Di Maio, <paoladimaio10@gmail.com> wrote:


Just as a reminder, this list is about sharing knowledge, research and practice in AI KR, The intersection with KR and CogAI may also be relevant here (and of interest to me)
If people want to discuss CogAI not in relation to KR, please use the CogAI CG list?What I mean is that: if KR is not of interest/relevance to a post, then why post here?

What is KR, its relevance and limitations is a vast topic, written about in many scholarly books, but also these books are not adequately covering the topic, In that sense, the topic of KR itself, without further qualification, is too vast to be discussed without narrowing it down to a specific problem/questionKR in relation to CogAI has been the subject of study for many of us for many years, and it is difficult to discuss/comprehend/relate to for those who do not share the background. I do not think this list can fill the huge gap left by academia, however there are great books freely available online that give some introduction .When it comes to the application of KR to new prototypes, we need to understand what these prototypes are doing, why and how. Unfortunately NN fall short of general intelligence and intellegibility for humans.

Adeel, thank you for sharing the paper 40 years of Cognitive ArchitecturesI am not sure you were on the list back then, but I distributed the resource as a working reference for this list and anyone interested in February 2021, and have used the resource as the basis for my research on the intersection AI KR/CogAI since
https://lists.w3.org/Archives/Public/public-aikr/2021Feb/0017.html
Dave: the topics KR, AI, CogAI and consciousness, replicability, reliability, and all the issues brought up in the many posts in this thread and other thread are too vastto be discussed meaningfully in a single thread

May I encourage the breaking down of topics/issues making sure the 
perspective and focus of KR (including its limitations) are not lost in the long threads
Thank you(Chair hat on)

On Fri, Oct 28, 2022 at 6:23 PM Adeel <aahmad1811@gmail.com> wrote:

Hello,
To start with might be useful to explore 'society of mind' and 'soar' as point of extension.
40 years of cognitive architecture
Recently, Project Debater also came into the scene. Although, not quite as rigorous in Cog or KR.
Thanks,
Adeel
On Fri, 28 Oct 2022 at 02:05, Paola Di Maio <paoladimaio10@gmail.com> wrote:

Thank you all for contributing to the discussion
the topic is too vast - Dave I am not worried if we aree or not agree, the universe is big enough 

To start with I am concerned whether we are talking about the same thing altogether. The expression human level intelligence is often used to describe tneural networks, but that is quite ridiculous comparison. If the neural network is supposed to mimic human level intelligence, then we should be able to ask; how many fingers do humans have?But this machine is not designed to answer questions, nor to have this level of knowledge about the human anatomy. A neural network is not AI in that senseit fetches some images and mixes them without any understanding of what they areand the process of what images it has used, why and what rationale was followed for the mixing is not even described, its probabilistic. go figure.
Hay, I am not trying to diminish the greatness of the creative neural network, it is great work and it is great fun. But a) it si not an artist. it does not create something from scratch b) it is not intelligent really, honestly,. try to have a conversation with a nn 
This is what KR does: it helps us to understand what things are and how they workIt also helps us to understand if something is passed for what it is not *(evaluation)This is is why even neural network require KR, because without it, we don know what it is supposedto do, why and how and whether it does what it is supposed to do
they still have a role to play in some computation



DR Knowledge representation in neural networks is not transparent, 
PDM I d say that either is lacking or is completely random

DR Neural networks definitely capture knowledge as is evidenced by their capabilities, so I would disagree with you there.


PDM  capturing knowledge is not knowledge representation, in AI, 
capturing knowledge is only one step, the categorization of knowledge is necessary to the reasoning



 

We are used to assessing human knowledge via examinations, and I don’t see why we can’t adapt this to assessing artificial minds because assessments is very expensive, with varying degrees of effectiveness, require skills and a process -  may not be feasible when AI is embedded to test it/evaluate it

We will develop the assessment framework as we evolve and depend upon AI systems. For instance, we would want to test a vision system to see if it can robustly perceive its target environment in a wide variety of conditions. We aren’t there yet for the vision systems in self-driving cars!
Where I think we agree is that a level of transparency of reasoning is needed for systems that make decisions that we want to rely on.  Cognitive agents should be able to explain themselves in ways that make sense to their users, for instance, a self-driving car braked suddenly when it perceived a child to run out from behind a parked car.  We are less interested in the pixel processing involved, and more interested in whether the perception is robust, i.e. the car can reliably distinguish a real child from a piece of newspaper blowing across the road where the newspaper is showing a picture of a child.
It would be a huge mistake to deploy AI when the assessment framework isn’t sufficiently mature.
Best regards,
Dave Raggett <dsr@w3.org>