Re: definitions, problem spaces, methods

Hi Dave,

Words and quantifiers are symbols. When we take huge text corpora and 
turn the words into text-prediction vectors, we create an index derived 
from those symbols. By this choice of knowledge re-presentation, we can 
only infer correlations, not assertions, propositions, intended 
relations, or meaning. Still, with huge numbers, in bounded domains for 
specific purposes, we can obtain remarkable language results. But we 
have no explainability, unknown boundaries of applicability, and hence a 
brittleness to their use in general settings. It is my belief, based on 
my understanding of Peirce, that we will never improve on this until we 
symbolically capture the intended assertions in the initial message. 
through some structure such as a knowledge graph. In any case, even when 
only using vector index values, we still translate back to symbolic 
language through the one-to-one mapping of word token and its index.

Peirce placed representation and semiosis (study of signs) in his 
mediate universal category of Thirdness. I find it tremendously helpful 
to use this lens when evaluating any aspect of knowledge representation 
. When we do AI using something like GPT-3 we are making an active 
choice of how we will represent our knowledge to the computer. For GPT-3 
and all massive data statistical models, that choice limits us to indexes.

Thanks, Mike

On 11/7/2022 3:44 AM, Dave Raggett 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
>> 319.621.5225
>> http://mkbergman.com
>> http://www.linkedin.com/in/mkbergman
>> __________________________________________
>
> Dave Raggett <dsr@w3.org>
>
>
>
-- 
__________________________________________

Michael K. Bergman
319.621.5225
http://mkbergman.com
http://www.linkedin.com/in/mkbergman
__________________________________________