Technology and Tools Selection for the ACM Project
Our ACM project centers on simulating an advanced model of consciousness. Below is an overview of the technologies and tools implemented, along with plans for future upgrades.
Language Model: Llama 3.3
Why Llama 3.3?
- Enhanced Capabilities: Trained on ~15 trillion tokens with a 128K context window, improving reasoning and quality in complex AI scenarios.
- Emergent Consciousness: Upgraded training data and performance align well with our goals of simulating emergent consciousness.
Multimodal Emotional Processing
Our Approach:
- Multimodal Integration: Combines textual and visual cues (inspired by EmoRoBERTa-like systems).
- Custom Fine-Tuning: Domain-specific data reflects the nuanced emotional responses needed for our simulation.
Memory Indexing and Vector Storage
Tool Choices:
- Pinecone: Managed service, scalable for dynamic simulation retrieval.
- FAISS / Milvus: Evaluated as potential alternatives offering greater control and lower latency.
World Modeling and Reinforcement Learning
Current Focus:
- DreamerV3: Balances modeling fidelity and efficiency, incorporating emotional context.
- Continuous Benchmarking: Ongoing tests against MuZero, PlaNet, and others for optimal performance.
Real-Time Video Processing & Unreal Engine Integration
Latency Optimization:
- VideoLlama3: Tailored for real-time video inference with GPU optimizations.
- Engine Tuning: Resolution adjustments and data pipeline optimization keep sub-100ms inferences.
Commitment to Innovation
Future Proofing:
- Modular Architecture: Allows rapid swaps or upgrades for emerging models and tools.
- Continuous Research: We monitor advances to keep the ACM’s simulation environment at the leading edge of artificial consciousness development.
For additional details or to contribute to our research, please send an email to info@theconsciousness.ai.
