Re: Knowledge Representation (KR) must be the core

Dave

Thanks for sharing the perspective where knowledge engineering becomes a
collaborative effort between humans and machines and the lecture. I have no
critique for all that you reference.

Indeed, as an unpacking task, I propose that  this group is well positioned
to address some of the  Fear, Uncertainty,  Doubt (FUD) concerns promoted
by the  Zero-Trust-in-AI / Don Quixote pundits.  Specifically,  I do
consider it best, that the total effort required for Knowledge
Representation (KR) utilization should be explained when seeking
stakeholder agreement long-term AI strategy;  and that this community
expands on the StratML-centric and ML oriented reasoning document we
produced during 2020 e.g. add PKN reasoning.

cheers

Carl Mattocks

It was a pleasure to clarify


On Sun, Oct 15, 2023 at 11:14 AM Dave Raggett <dsr@w3.org> wrote:

> Hi Carl,
>
> A slightly different perspective is where knowledge engineering becomes a
> collaborative effort between humans and machines.  The human partner is
> concerned with use cases, curation and scalability. The machine partner
> deals with the knowledge representation, versioning and ensuring that
> rulesets are updated to match changes to the ontologies, as a basis for
> satisfying inevitable demands for ongoing support for both new and old
> applications.
>
> This changes the focus to the representations used for the human-machine
> collaboration, freeing the internal machine representation of knowledge to
> better suit advances in AI.  LLMs have demonstrated that self-guided
> machine learning is orders of magnitude better than hand crafting
> knowledge. This is only just the beginning and we can look forward to major
> improvements in neural network architectures and training techniques.  You
> can get a feeling for what I am talking about in my recent invited lecture
> for the University of Bath’s AI Group, see:
>
> https://www.w3.org/2023/10/10-Raggett-AI.pdf
>
> Collaborative knowledge engineering will be very permissive in respect to
> representations, e.g. natural language, mathematical expressions, diagrams,
> pictures, tables, spreadsheets, databases and so forth. AI systems will
> understand these in a very similar way to how we do.  This suggests that
> the premise that "Knowledge Representation (KR) must be the core of future
> AI systems” is flawed and needs unpacking.  What kinds of AI systems are we
> talking about?  How would this be effected by the emergence of AGI?
>
> Best regards,
>      Dave
>
> On 15 Oct 2023, at 15:22, carl mattocks <carlmattocks@gmail.com> wrote:
>
>
> Given that there more types of AIKR I believe our members could help
> people identify the differences and provide some simple rules on usage
> e.g. Reasoning supported
>
> As an explainer this article is focused on " why Knowledge Representation
> (KR) must be the core of any cost-effective long-term AI strategy  "  and
> suggests that they need to be "models advise us on what actions to take"
> https://dmccreary.medium.com/the-jellyfish-and-the-flatworm-bdad78e6f68b
>
> Given this group has already created a document focused on key elements
> of  AI Strategy .. I would be happy to schedule a series of meeting to
> expand it towards "
> identify the Knowledge Representation differences and provide some simple
> rules on usage
> enjoy
>
> Carl Mattocks
>
> It was a pleasure to clarify
>
>
>>
> Dave Raggett <dsr@w3.org>
>
>
>
>