Re: KR for Cogai/gentle reminder

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
> <https://www.cin.ufpe.br/~mtcfa/files/in1122/Knowledge%20Representation%20and%20Reasoning.pdf>
>
> 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/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>
>>>>>>
>>>>>>
>>>>>>
>>>>>>