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.

Decentralized Driving AI (Open-Source Autonomous Vehicles)

Imagine a world where self-driving cars aren’t locked behind corporate walls but powered by a global, open-source movement. Picture torrents - yes, the same tech you might use to download a movie - sharing massive datasets of autonomous driving data. These datasets fuel AI models trained not in a single data center, but on computers scattered across the planet. That AI is then loaded into cars capable of steering themselves, creating an Autonomous Open Source Driver that improves with every mile driven. Buckle up - this is the future of autonomous driving, and it’s decentralized, bold, and unstoppable.

The Concept: Torrents Meet Autonomous Driving

At the core of this idea is the torrent - a peer-to-peer file-sharing system designed to handle big data. Torrents break files into bite-sized chunks, letting users download and upload simultaneously. Here, they’re repurposed to distribute the lifeblood of autonomous driving AI: datasets packed with video footage, sensor readings, and real-world driving scenarios.

Why torrents? Because they’re fast, efficient, and - most importantly - democratic. Anyone with a decent internet connection can grab the data and contribute to it. No tech giant or subscription required. This creates a massive, shared pool of driving data from roads worldwide, setting the stage for something truly groundbreaking.

How It Works

Step 1: Sharing the Data

The process starts with a torrent file loaded with autonomous driving data - think hours of dashcam footage, LIDAR scans, and GPS logs. Drivers, hobbyists, or even small companies upload their real-world driving experiences to the torrent. As more people join, the dataset grows richer and more diverse, capturing everything from Tokyo traffic jams to rural dirt roads.

Step 2: Decentralized Training

Next, the AI gets trained - but not in some sleek Silicon Valley server farm. Instead, it’s trained on a sprawling network of computers everywhere. Your gaming PC, a coder’s laptop, or even a university’s spare servers could pitch in. Think of it like Folding@home, where idle computing power tackles big problems, or a blockchain setup where contributors earn rewards for their efforts.

This decentralized approach taps into a global pool of computational muscle, training the AI on a scale no single company could match. The result? A model that’s tougher, smarter, and less prone to the biases of a single dataset.

Step 3: Loading the AI into Cars

Once trained, the AI model is packaged - possibly shared via torrents again - and loaded into cars equipped with the right hardware (cameras, sensors, etc.). These vehicles now have an Autonomous Open Source Driver capable of navigating roads, dodging obstacles, and making split-second calls. It’s not just a brain for the car - it’s a brain that’s free for anyone to use and improve.

Why It’s a Game-Changer

This isn’t just tech for tech’s sake - it’s a paradigm shift. Here’s why:

• Open Source Power: The AI’s code and data are public. Anyone can peek under the hood, tweak it, or fix it. This transparency beats the “trust us” black-box approach of proprietary systems, letting drivers know exactly how their car thinks.
• Global Diversity: Training on computers everywhere means the AI learns from every corner of the planet. It’s not stuck mastering California highways while flunking Mumbai monsoons. More data, more variety, better results.
• Constant Improvement: Every car running this AI can upload new experiences - say, a tricky intersection or a sudden deer crossing - back to the network. That data refines the model, making it better for everyone. It’s a self-improving system that gets sharper with every drive.

The Rough Road

Of course, this wild idea has its potholes:

• Data Quality: With anyone able to contribute, how do you keep the dataset clean? Junk data could confuse the AI. Community vetting or smart filters might be the fix.
• Security Risks: Sharing driving data via torrents could expose sensitive info or invite tampering. Anonymization and encryption will be key.
• Coordination Chaos: The users could vote when a version of the AI is ready. But how do you stop bad actors from screwing with the training? These are potentially messy problems that need clever solutions.

The Future: A Self-Driving World for All

Picture this: autonomous cars that aren’t just for the rich or the urban elite. A farmer in Nebraska could download the latest AI model, trained on similar backroads, and retrofit their old truck. A startup in Brazil could build cheap self-driving taxis tailored to local chaos. As more cars join the network, the Autonomous Open Source Driver evolves - learning from every near-miss, every storm, every curveball the road throws.

This is about a future where innovation comes from the crowd, not a company, and where self-driving tech serves everyone, not just subscription holders.

The Ride’s Just Beginning

Using torrents to share autonomous driving data, training AI on a decentralized network, and loading it into self-driving cars might sound like a fever dream. But it’s a dream with teeth - a radical rethink of how we build the future. The Autonomous Open Source Driver isn’t static; it’s alive, growing smarter with every contributor. In a world skeptical of centralized control, this could be the spark that lights up the roads. So, hop in - the journey’s just getting started.