In my Hybrid AI post, I provided an overview of what Hybrid AI means (to me, at least). And I briefly discussed the Neuro-Symbolic (NS) synthesis – and how it acts as the “interfacing” part of the Ren system.
My focus on Hybrid AI and Neuro-Symbolic AI isn’t unique – both are gaining significant traction across the AI community. “Hybrid” gets a bit more traction in industry – it’s broad, friendly, and vague enough to market. “Neuro-Symbolic” gets a bit more traction in the research community – it’s precise, technical, and less marketable.
While Hybrid AI and Neuro-Symbolic AI are gaining traction, language and definitions continue to emerge. And as it turns out, the way I use Hybrid AI and Neuro-Symbolic are pretty different from – but not entirely inconsistent with – emerging usages.
A few things I hope you take away from this post:
- In the AI community, Hybrid AI is basically synonymous with Neuro-Symbolic AI (NSAI) – Hybrid AI means Neuro-Symbolic synthesis. In Ren’s world, the Neuro-Symbolic synthesis is one part of a larger hybridization. Ren uses Hybrid AI as a big-tent term (nine traditions); the community often uses Hybrid AI as a narrower, more focused term (two traditions).
- Ren DOES synthesize neural networks AI into the ‘interfacing’ layer because natural language generation and interpretation is where neural networks shine.
- Ren DOES NOT synthesize neural networks AI into the ‘thinking’ layer because this is where the weakness of neural networks (hallucinations, black box operation, etc.) are untenable in the caregiving context.
- This is different in focus than the large majority of the AI community that’s working on Neuro-Symbolic AI (NSAI): most everyone is focused on fusion reasoning – neural networks and symbolic systems woven together into a single reasoning engine. I’m focused on sequestering the neural networks and LLMs far away from Ren’s symbolic reasoning core.
For Ren, Hybrid AI is a Big Tent Term
These days, “Hybrid AI” and “Neuro-Symbolic AI” are often treated as synonymous. Most of the research, writing, and investment around Hybrid AI is about how to merge neural networks and symbolic systems – Neuro-Symbolic fusion. Gemini estimates that in the AI literature roughly 75-90% of what’s called Hybrid AI today is in fact Neuro-Symbolic AI.
For the AI community, Neuro-Symbolic AI is Hybrid AI.
But for Ren, the Neuro-Symbolic synthesis is one part of a much larger hybrid system: Ren synthesizes nine distinct AI traditions into four “pillars” that enable Ren’s capabilities.
With Ren, the Neuro-Symbolic synthesis is one part of Hybrid AI.
Neuro-Symbolic: Fusion Reasoning vs. Modular Interfacing
For the most part, Neuro-Symbolic fusion is specifically focused on reasoning. The research community has coalesced around Neuro-Symbolic fusion reasoning as its primary strategy for addressing the biggest challenges with both neural networks and symbolic systems in the reasoning process while capturing the best each tradition has to offer.
Neural networks are great at “fuzzy” pattern matching and working with unstructured or ambiguous information but suffer from hallucinations, lack of grounding, and black-box reasoning. Symbolic systems are great at transparent reasoning, grounding, and traceability but suffer from brittleness and an inability to operate on unstructured or ambiguous information.
The idea is that by embedding symbolic structure within neural systems, those systems can reason more transparently and maintain internal consistency. Conversely, by connecting symbolic systems to neural representations, those systems can gain flexibility and learn from unstructured data. Best of both worlds.
It’s an ambitious synthesis – an attempt to make neural models more explainable and symbolic models more adaptive.
Ren’s use of the Neuro-Symbolic synthesis is quite different from fusion reasoning and is focused rather on modular interfacing.
With Ren, neural networks are not merged with symbolic systems. They are built as modular services and agents that collaborate with one another but remain distinct. The purpose of the collaboration is to interface with the human world through natural language. The symbolic system operates like an automated prompt engineer in service of the rest of the Ren system.
When speaking with humans, the symbolic system identifies speaking intentions, establishes utterance parameters (tone, affect, reading level, etc.), maintains conversation and memory, provides utterance samples, and formulates LLM prompts. The neural network system converts the prompt into an utterance.
When listening to or reading human language, the symbolic system establishes interpretation parameters, provides template data structures, stipulates interpretation parameters (confidence intervals, safety stop phrases), and formulates LLM prompts. The neural network system uses the prompt to interpret spoken or written natural language.
While fusion reasoning and modular interfacing are very different applications of the Neuro-Symbolic synthesis, they share a common motivation – get the best of both traditions while compensating for the challenges of each.
Why Not Fusion Reasoning for Ren?
Simply put, the risk isn’t worth the reward.
- There is no strong consensus about whether LLMs are now able to – or will ever be able to – reason in a true and meaningful sense. What we do know is that LLMs make mistakes, hallucinate, and are opaque in their “reasoning” activities. And we know it takes a lot of time and effort to try to compensate for these problems (guardrails, prompt engineering).
- Fusion reasoning is still experimental and it’s unclear when usable patterns or frameworks will emerge from the labs.
- For the caregiving domain, symbolic reasoning is robust. And there are alternative ways (e.g., our use of agent ‘troupes’) to compensate for rigidity and brittleness.
- Caregiving is a high-stakes, safety-critical domain. You simply cannot build a reliable system on a reasoning engine that makes things up.
In my view, trustworthiness must be architected, not retrofitted.
Leave a Reply