Re: KR for Cogai/gentle reminder

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/question
> KR 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 Architectures
> I 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 vast
> to 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
>> <http://aurellem.org/society-of-mind/index.html>' and 'soar' as point of
>> extension.
>>
>> 40 years of cognitive architecture
>> <https://link.springer.com/content/pdf/10.1007/s10462-018-9646-y.pdf>
>>
>> Recently, Project Debater
>> <https://research.ibm.com/interactive/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 sense
>>> it fetches some images and mixes them without any understanding of what
>>> they are
>>> and 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 work
>>> It 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 supposed
>>> to 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>
>>>>
>>>>
>>>>
>>>>