Re: The role of symbolic AI at the dawn of AGI

FYI ...

On Wed, 11 Oct 2023, 11:13 pm Dave Raggett, <dsr@w3.org> wrote:

> I gave an invited lecture yesterday to the ART-AI group at the University
> of Bath, UK. See: UKRI CDT in Accountable, Responsible and Transparent AI.
> Website: https://cdt-art-ai.ac.uk
>
> Title: *The role of symbolic knowledge at the dawn of AGI*
>
> Abstract:
>
> Large language models and generative AI have shown amazing capabilities.
> We tend to see them as much more intelligent than they actually are. It is
> time to embrace the many research challenges ahead before we can truly
> realise AGI. Work in the cognitive sciences can help us to better mimic
> human cognition, and to understand how to address generative AI failures
> such as  factual errors, logical errors, inconsistencies, limited
> reasoning, toxicity, and fluent hallucinations. How can we architect
> systems that continuously learn from limited data like we do, combining
> observations and direct experience along with autonomous, algorithmic and
> reflective cognition?
>
> If machine learning is so effective for neural networks, where does that
> leave symbolic AI?  My conjecture is that symbolic AI has a strong future
> as the basis for semantic interoperability between systems, along with
> knowledge graphs as an evolutionary replacement for today's relational
> databases. We, do however, need to recognise that human interactions and
> our understanding of the world is replete with uncertainty, imprecision,
> incompleteness and inconsistentency.  Logicians have largely turned a blind
> eye to the challenges of imperfect knowledge.
>
> This is despite a long tradition of work on argumentation, stretching all
> the way back to Ancient Greece. This tradition underpins courtroom
> proceedings, ethical guidelines, political discussion and everyday
> arguments. I will introduce the plausible knowledge notation as a way to
> address plausible inference of properties and relationships, fuzzy scalars
> and quantifiers, along with analogical reasoning. Work on symbolic AI can
> help guide research on neural networks, and vice versa, neural networks can
> assist human researchers, speeding the development of new insights.
>
>
>
> The slides are available at: http://www.w3.org/2023/10/10-Raggett-AI.pdf
>
> Dave Raggett <dsr@w3.org>
>
>
>
>