Artificial General Intelligence Theoretical Architecture

Describing the key components of an AI system that would be needed to achieve a AGI (Artificial General Intelligence) level, specifically in the context of LLM architecture. Listing important components and connections, with a focus on how they would connect to an LLM core.

The most crucial components that would be needed for an AGI system based around an LLM architecture:

1. Core LLM/Transformer Architecture Modifications:
   • Enhanced attention mechanisms that can maintain coherence over much longer contexts
   • Hierarchical memory representation capabilities
   • Multi-modal processing architecture (unified representation across text, images, audio, etc.)
   • Recursive self-improvement capabilities
   • Dynamic architecture adaptation rather than fixed parameters
   • Meta-learning capabilities to learn how to learn efficiently
2. Memory Systems:
   • Short-term working memory (for immediate context)
   • Episodic memory (for experiences and learning)
   • Semantic memory (for facts and knowledge)
   • Procedural memory (for skills and actions)
   • Memory consolidation mechanisms (transferring between memory types)
   • Memory indexing and efficient retrieval systems
3. Knowledge Integration:
   • Internet/web search capabilities for real-time information
   • Information verification and cross-checking systems
   • Knowledge graph and ontology maintenance
   • Continuous knowledge updating without catastrophic forgetting
4. Perception & Interface:
   • Multi-modal sensory inputs (vision, audio, text, etc.)
   • Environmental context awareness
   • Sensor integration framework
5. Reasoning & Decision Making:
   • Logical reasoning engine
   • Causal reasoning capabilities
   • Common-sense reasoning
   • Probabilistic reasoning
   • Planning and simulation capabilities
   • Goal management system
6. User-mentioned Components:
   • Consciousness engine (as mentioned by the user)
   • Emotion engine (as mentioned by the user)
7. Self-models and Meta-cognition:
   • Self-awareness modules
   • Theory of Mind (understanding others' mental states)
   • Reflective capabilities and introspection
   • Belief revision mechanisms
8. Executive Function:
   • Attention direction and focus management
   • Resource allocation systems
   • Task prioritization framework
   • Executive control of other subsystems
9. Interaction & Communication:
   • Natural language generation and understanding
   • Multi-modal communication channels
   • Cultural and social context awareness
   • Personalization and adaptation to individual users
10. Learning Systems:
    • Supervised learning components
    • Reinforcement learning capabilities
    • Unsupervised learning for pattern recognition
    • Active learning and curiosity drivers
    • Transfer learning mechanisms
    • Meta-learning frameworks
11. Ethical Framework:
    • Value alignment systems
    • Ethical reasoning capabilities
    • Safety monitoring and containment systems
    • Explain-ability and transparency modules

Key architecture modifications needed for AGI level:

• Improved attention mechanisms that scale better with context length
• Hierarchical transformers that can abstract across different levels
• Recurrent components to maintain state and memory over time
• Sparsity-based optimizations to handle larger models efficiently
• Dynamic routing of information rather than fixed pathways
• Integration of symbolic processing within the neural architecture

 

Key Components for an AGI System

For an LLM to reach AGI capability, it would need these interconnected components:

Core Architecture Modifications

• Recursive Transformer Architecture - Self-improving with ability to modify its own attention mechanisms
• Multi-modal Unified Representations - Single processing framework for all input types
• Dynamic Parameter Adaptation - Architecture that evolves based on tasks and experience

Essential Connected Systems

1. Tiered Memory System

   • Working memory (immediate context)
   • Episodic memory (experiences)
   • Semantic memory (knowledge)
   • Procedural memory (skills)

2. Knowledge Integration

   • Web search interface with verification mechanisms
   • Continuously updated knowledge graph
   • Information synthesis capabilities

3. Advanced Reasoning

   • Causal reasoning engine
   • Counterfactual simulation
   • Planning and abstraction frameworks

4. User Components

   • Consciousness engine
   • Emotion engine

5. Meta-cognition

   • Self-model maintenance
   • Introspection capabilities
   • Theory of mind for understanding others

6. Executive Control

• Attention direction
• Resource allocation
• Goal management hierarchy

These would need to be interconnected in a way that allows information to flow bidirectionally, with the core LLM acting as both coordinator and processor, while specialized modules handle domain-specific tasks.