ASTRID, or "Analysis of Systemic Tagging Results in Intelligent Dynamics" is a Cognitive Architecture project of the dutch AGI company "Mind Construct". They express bold statements about their prototype. Learn more: https://mindconstruct.com/website/astrid
and the paper: https://mindconstruct.com/files/selflearningsymbolicaicritcalappraisal.pdf
I agree with the claim ot the ASTRID authors that "symbolic" systems can
learn. The problem of the systems which don't is that their "symbols"
are too narrow and lack the appropriate "connectedness"/scanning/exploring perspective, generality,
potential to map to the others, in short: too "flat" and simple.
P.S. However see the article: Neural Networks are Also Symbolic - Conceptual Confusions in ANN-Symbolic Terminology: https://artificial-mind.blogspot.com/2019/04/neural-networks-are-also-symbolic.html
I don't like the misnomer "symbolic". More appropriate terms for "symbolic" AI are logic-based, logical, conceptual (opposed to [purely] "numerical"); discrete? (or more discrete and more precisely addressable than the "subsymbolic"), "structured", "knowledge-based", "taxonomy-ontology-...", more explicit etc. (many other possible attributes).
On the other hand some methods which are called "subsymbolic" are more "numerical", but they eventually or at certain levels also become "symbolic" or logical and usually are mapped to some "symbolic" categorization (last layer).
Both do and should join and the "logical" and "discrete" at the lowest level and in its representation is also "numerical" and "analog" to some extent. It is some digital operation in a computer, when operating on visual, auditory etc. raw sensory data it is numeric etc.
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