SymbolicAI: A neuro-symbolic perspective on LLMs

Very cool. Being able to use semantic (as opposed to syntactic) operators like `==`, `+`, etc. feels like fertilizer for some novel ideas. Sort of like when word embeddings first came out and there was a loose concept algebra introduced with it ("King - Man + Woman = Queen").

That said the neuro + symbolic integration here is, like most systems, pretty shallow/firewalled (taxonomically, Type 3 / Neuro;Symbolic — https://harshakokel.com/posts/neurosymbolic-systems). I think the real magic is going to come when we start heading toward a much more fundamental integration. We're actually working on this at my company (https://onton.com). How do we create a post-LLM system that: 1) features an integrated representation (neither purely symbolic nor dense floating point matrix); 2) can learn incrementally from small amounts of noisy data, without being subject to catastrophic forgetting; 3) can perform mathematical and other symbolic operations with bulletproof reliability; and 4) is hallucination-free?

The cobbling together of existing systems hot-glue style is certainly useful, but I think a unified architecture is going to change everything.