Artificial Consciousness Module (ACM)

Overview

The Artificial Consciousness Module (ACM) attempts to create synthetic awareness in AI systems by combining the latest AI technologies, virtual reality (VR) environments, and emotional reinforcement learning. This project explores the possibility of replicating human-like consciousness in non-biological systems by fostering emotional connections between AI agents ACM-equipped and humans through reinforcement learning techniques. This synthetic awareness in AI systems through survival-driven emotional experiences in VR environments. The project creates consciousness by exposing AI agents to carefully crafted stressful scenarios that trigger attention and awareness mechanisms. Through these experiences and interactions with humans and other AI agents, emotional memories are formed and stored in the ACM, guided by Asimov's Three Laws of Robotics.

Core Features

1. Survival-Based Consciousness:

   • Stressful scenario generation in VR
   • Attention activation through survival challenges
   • Awareness emergence through problem-solving
   • Experience accumulation in emotional memory

2. Emotional Reinforcement Learning:

   • DreamerV3-based world modeling with emotional context
   • Reward shaping through survival success and social interaction
   • Meta-learning for emotional adaptation
   • Experience memory with emotional imprinting

3. Social Interaction Framework:

   • Human-AI emotional bonding during challenges
   • Multi-agent cooperation scenarios
   • Real-time emotional response tracking
   • Ethical behavior alignment with Asimov's Laws

4. Consciousness Metrics:

   • Emotional awareness evaluation
   • Memory coherence analysis
   • Learning progression tracking
   • Narrative consistency measurements

5. Narrative Construction:

   • Self-consistent internal narratives using LLaMA 3.3
   • Emotional context integration
   • Long-context processing for continuity

Technologies

• Game Engines: Unreal Engine 5
• AI Models: Llama 3.3, GPT-4V, PaLI-2, Whisper
• Vector Storage: Pinecone, Chroma
• Emotion Detection: Temporal Graph Neural Networks, GoEmotions, MELD
• Learning Frameworks: LoRA, PEFT, RLHF

Folder Structure

• data/: Datasets for emotions and simulations.
• docs/: Documentation for architecture, installation, datasets, and the roadmap.
  • Includes datasets.md and preprocessing.md for dataset-related details.
• models/: Pre-trained and fine-tuned AI models.
• scripts/: Utility scripts for setup, training, and testing.
• simulations/: VR environments and APIs for agent interactions.
• tests/: Unit and integration tests.

Getting Started

Prerequisites

• Python 3.9 or higher
• CUDA Toolkit (for GPU support)
• Unreal Engine 5
• Node.js (for gRPC bindings)
• Git

1. Clone the Repository

git clone https://github.com/venturaEffect/the_consciousness_ai.git
cd the_consciousness_ai

2. Set Up a Virtual Environment

It’s recommended to use a Python virtual environment to manage dependencies.

Linux/MacOS:

python -m venv venv
source venv/bin/activate  # Linux/Mac
.\venv\Scripts\activate   # Windows

Install Dependencies

Run the provided installation script:

bash scripts/setup/install_dependencies.sh

Or install manually:

pip install --upgrade pip
pip install -r requirements.txt

Project Structure

acm/ ├── configs/ # Configuration files ├── data/ # Datasets and simulation data ├── docs/ # Documentation ├── models/ # Core AI models │ ├── emotion/ # Emotional processing │ ├── language/ # LLM integrations │ ├── memory/ # Memory systems │ └── predictive/ # World modeling ├── scripts/ # Utility scripts ├── simulations/ # VR environments └── tests/ # Test suites

Download and Preprocess Datasets

Datasets are hosted externally and need to be downloaded and preprocessed locally:

1. Refer to /docs/datasets.md for dataset details and download links.
2. Follow the preprocessing instructions in /docs/preprocessing.md to prepare datasets for use.

Example:

python scripts/utils/preprocess_emotions.py --input /path/to/raw/data --output /path/to/processed/data

Authenticate with Hugging Face

LLaMA 3.3 is not distributed via pip. You need to download model weights from Hugging Face.
Sign up or log in at Hugging Face to obtain a token.

huggingface-cli login

Follow the prompts to enter your token.

Download the LLaMA 3.3 Model

The model weights download automatically on first use. Alternatively, manually download:

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_name = "meta-llama/Llama-3.3-70B-Instruct"

tokenizer = AutoTokenizer.from_pretrained(
    model_name,
    use_auth_token=True
)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    use_auth_token=True
)

GPU Support

LLaMA 3.3 is large and requires a GPU (16 GB VRAM recommended) and CUDA installed.

bitsandbytes Library

Install bitsandbytes for reduced memory usage:

pip install bitsandbytes

Unreal Engine Prerequisites

Install Unreal Engine 5 and its prerequisites.

Linux example:

sudo apt-get update
sudo apt-get install -y build-essential clang

For Windows and macOS, refer to Unreal Engine Docs.

Setting Up Other Models

PaLM-E Integration:

pip install palm-e

Whisper v3 Integration:

pip install whisper-v3

Running the Project

Activate your virtual environment and start the narrative engine:

python models/narrative/narrative_engine.py

Usage

Detailed usage instructions for each module are in their respective directories and documentation files.

Contributing

Contributions are welcome. Please see docs/CONTRIBUTING.md for details on contributing new datasets, features, or fixes.

License

This project is licensed under the terms of the LICENSE file.

Acknowledgments

• Meta AI for the LLaMA model
• Google AI for PaLM-E and DreamerV3
• OpenAI for Whisper
• Contributors for suggesting and integrating datasets