Re: definitions, problem spaces, methods

Hello,

But, none of those models have explainability.
So, cannot explain precisely how they are reaching those conclusions and
decisions because they are essentially working in a black-box?

Thanks,

Adeel

On Mon, 7 Nov 2022 at 12:58, Dave Raggett <dsr@w3.org> wrote:

> GPT3, BLOOM as examples of large language models
>
> DALLE-E, Stable Diffusion as examples of text to image
>
> AlphaFold for predicting 3D protein structures
>
> These all embed knowledge obtained from deep learning  against large
> corpora. The models combine the networks and their trained connection
> parameters, e.g. BLOOM has 176 billion parameters and DALL-E 2 has around
> 3.5 billion. This approach discovers its own (distributed) knowledge
> representation and scales much better than hand-authored KR. However like
>  hand-authored KR, it is still brittle when it comes to generalising beyond
> its training data, something that humans are inherently better at.  Deep
> learning suffers from a lack of transparency, and there has been quite a
> bit of work trying to improve on that, e.g. showing which parts of an image
> were most important when it came to recognising an object. One big
> potential advantage is in handling imprecise context dependent knowledge,
> where hand authored KR (e.g. RDF) has a hard time. There is a lot of
> current effort on graph embeddings as a synthesis of neural networks and
> symbolic graphs. However, these are still far from being able to model
> human reasoning with chains of plausible inferences and metacognition
> (reasoning about reasoning).
>
> On 7 Nov 2022, at 10:59, Paola Di Maio <paola.dimaio@gmail.com> wrote:
>
> Dave perhaps you could post a few examples of non symbolic KR so that we
> can get our heads around
> such a thing-
> Please note that my postulate shared on this list
> https://lists.w3.org/Archives/Public/public-aikr/2019Aug/0045.html
> states that
>
> To support AI explainability, learnability,verifiability and
> reproducibility, it is postulated that
> for each MLA *machine learning algorithm,
> there should correspond a natural language expression or other type of
> symbolic knowledge representation
>
>
> https://figshare.com/articles/poster/A_New_Postulate_for_Knowledge_Representation_in_AI/9730268/2
>
> was also slightly reworded in different presentations
>
> On Mon, Nov 7, 2022 at 5:45 PM Dave Raggett <dsr@w3.org> wrote:
>
>> The statement *“We can only pursue artificial intelligence via symbolic
>> means” *is false, since artificial neural networks eschew symbols, and
>> have been at the forefront of recent advances in AI.  I therefore prefer
>> the Wikipedia definition of KR which is less restrictive:
>>
>> “Knowledge representation and reasoning (KRR, KR&R, KR) is the field of
>> artificial intelligence (AI) dedicated to representing information about
>> the world in a form that a computer system can use to solve complex tasks”
>>
>>
>> See: https://en.wikipedia.org/wiki/Knowledge_representation_and_reasoning
>>
>> On 7 Nov 2022, at 03:03, Mike Bergman <mike@mkbergman.com> wrote:
>>
>> Hi All,
>>
>> It is always useful to have a shared understanding within a community for
>> what defines its interests and why they have shared interests as a
>> community. I applaud putting these questions out there. Like all W3C
>> community groups, we have both committed students and occasional grazers.
>> One can generally gauge usefulness of a given topic in a given group by the
>> range of respondents to a given topic. Persistence seems to be more a
>> function of specific interlocuters not letting go rather than usefulness.
>>
>> After researching what became a book to consider the matter, I came to
>> the opinion that AI is a subset of KR [1]. The conclusion of that
>> investigation was:
>>
>> "However, when considered, mainly using prescission, it becomes clear
>> that KR
>> can exist without artificial intelligence, but AI requires knowledge
>> representation.
>> * We can only pursue artificial intelligence via symbolic means*, and KR
>> is the transla -
>> tion of information into a symbolic form to instruct a computer. Even if
>> the com-
>> puter learns on its own, we represent that information in symbolic KR
>> form. This
>> changed premise for the role of KR now enables us to think, perhaps, in
>> broader
>> terms, such as including the ideas of instinct and kinesthetics in the
>> concept. This
>> kind of re-consideration alters the speculative grammar we have for both
>> KR and AI,
>> helpful as we move the fields forward." (p 357)
>>
>> That also caused me to pen a general commentary on one aspect of the KR
>> challenge, how to consider classes (types) versus individuals (tokens) [2].
>> I would also argue these are now practically informed topics, among many,
>> that augment or question older bibles like Brachman and Levesque.
>>
>> Best, Mike
>> [1] https://www.mkbergman.com/pubs/akrp/chapter-17.pdf
>> [2]
>> https://www.mkbergman.com/2286/knowledge-representation-is-a-tricky-business/
>>
>> --
>> __________________________________________
>>
>> Michael K. Bergman
>> 319.621.5225http://mkbergman.comhttp://www.linkedin.com/in/mkbergman
>> __________________________________________
>>
>>
>> Dave Raggett <dsr@w3.org>
>>
>>
>>
>>
> Dave Raggett <dsr@w3.org>
>
>
>
>