Hello everyone,
I want to share a research concept and a proof of concept (PoC) for an experimental AGI architecture called “Emmanuelle”. This approach proposes a radical break from current methods to address two major challenges in AI: catastrophic forgetting and ethical alignment of AI.
The Core Concept
The central idea is to introduce a Module S (Symbolic/Spiritual layer) that organizes knowledge and memory not as a data stack, but around a hierarchy of values and symbolic relationships.
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Memory through Meaning: Each learning experience is linked to a value or a purpose, not just performance. This allows for a living, contextual, and naturally stable memory that doesn’t self-destruct.
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Semantic Graph: The architecture uses a dynamic directed graph (built with
networkx) to encode experiences into symbolic nodes. -
Ethically Guided Reinforcement Learning: The internal reward signal is aligned with virtues (wisdom, compassion, justice, etc.), encouraging actions that are both efficient and ethically consistent.
Why It’s Different
Unlike current replay or regularization approaches that struggle to adapt to open-ended environments, the Emmanuelle model is inspired by semantically organized human memory. Learnings that integrate into the global structure are reinforced; those that create chaos are pruned.
Proof of Concept and Code
I have successfully run an implementation of this model on a simple task (e.g., CartPole or LunarLander) and generated an image of the semantic graph in action.
I am sharing this work for the community to see, in the spirit of open science. I am not an academic researcher and cannot commit to a formal validation process, but I believe the concept is worth exploring and potentially improving upon by others.
Feel free to examine the code and the document.