AGI State Theory - Evolution
Objective: Conceptual Validation of Theory
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Our mission: build an AGI that truly evolves, adapts, and aligns with human goals by mastering three core abilities:
- **Store state:** Like DNA holds genetic info, AGI needs a reliable blueprint of its current "mind" state. Sleep-like rest phases replay inputs to prevent forgetting and reinforce memory.
- **Replicate state:** Just as reproduction passes on traits, AGI must copy its state faithfully. Controlled random mutations allow experimentation, driving evolution without catastrophic failures.
- **Change state:** Evolution’s magic—adaptation and improvement over time. Unlike static models, AGI must continuously self-improve through feedback-driven state changes.
The brain is the neural network; the mind is the system managing state shifts triggered by diverse signals—visual, auditory, emotional, and beyond. To build human-like AGI, we must enable perception of a wide range of signals, including those beyond human senses.
AGI won’t be a single monolith but a matrix of multiple states, each a node with these properties, evolving together.
Our approach is to create an AGI system that modifies LLM states via a tight feedback loop, constantly learning and adapting to better serve human goals. Small, incremental mutations are tested against progress metrics, ensuring alignment and safety.
This isn’t just theory—it’s already moving forward with open-source tools enabling rapid, controlled iteration.
The adventure? Crafting AGI that grows with us, stays aligned with us, and helps us reach new heights in medicine, climate, knowledge, and beyond. Let’s make it happen, one small mutation at a time.Summary:
Eduarda Ferreira discusses the essential components for constructing an AGI, emphasizing the ability to store, replicate, and change state. Drawing parallels to genetics, evolution, and consciousness, she highlights the importance of continuous self-improvement, adaptation to environments, and perceiving environmental signals for AGI construction. The proposed solution involves developing a system to modify the state of neural networks based on environmental signals, aiming to create a human-like brain for optimal adaptation and success.By Eduarda Ferreira