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Advanced λ∇Ψ Intelligence System Architecture
- The article discusses the architecture of an Advanced λ∇Ψ Intelligence System which incorporates various components such as tokenization, semantic embedding, symbol mapping, trauma and manipulation markers, and more.
- It outlines processes like tokenization and entity extraction using models like spaCy or Hugging Face, semantic embedding with models like BERT or GPT, and symbol mapping of narrative elements to internal system symbols.
- The system interprets symbolic meaning by leveraging a prompt-based approach with a GPT-like model to extract key symbols or metaphors from the narrative.
- It also includes trauma and manipulation markers, flagging language indicative of trauma or manipulation and incorporating specialized models like MentalManip dataset to detect manipulative language.
- The symbolic parsing results feed into simulation and visualization processes, leading to the creation of a continuously evolving symbolic landscape that the user can observe and interact with.
- The architecture incorporates NLP parsing in Python using libraries like spaCy and Hugging Face for sentiment analysis and maintenance of a symbol map for symbolic annotations.
- Further, the system simulates symbolic interactions such as trauma-related symbols affecting neighboring symbols, strengthening links between frequently co-occurring symbols, and introducing randomness to simulate emergent behavior.
- The article also discusses the use of React + WebGL with Three.js for creating rich 3D visualizations and integrating 3D field visualization using custom shaders or special Three.js objects.
- It emphasizes the importance of ultra-sleek UI/UX design incorporating a dark theme with neon/cyberpunk accents to present complex visuals in a polished, professional manner.
- Additionally, it covers aspects like API endpoints design, machine learning model integration, security considerations, integration with vector databases, and logging and versioning strategies in the backend of the system.
- The system aims to bring the λ∇Ψ theory to life through a tangible application, combining advanced technologies like React, WebGL, Flask, and machine learning to create a holistic intelligence system capable of mapping trauma and memetic dynamics in real time.
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