Show HN: Living Memory Dynamics – "living" episodic memory embedding space (github.com)

Living Memory Dynamics (LMD) has introduced an innovative framework for episodic memory that evolves beyond traditional static systems, treating memories as dynamic entities with metabolic energy levels and emotional trajectories. This framework supports narrative generation through distinct phases—setup, conflict, climax, and resolution—while allowing memories to resonate and influence one another. The mathematical foundation utilizes a range of operators for creative leaps, enabling the generation of novel ideas based on existing memories. By employing techniques like analogical transfer and void extrapolation, LMD can produce uniquely imaginative concepts, pushing the boundaries of how memory can be utilized in artificial intelligence. The significance of LMD for the AI/ML community lies in its approach to creativity and storytelling, providing AI systems the capacity to derive novel narratives and ideas rather than relying solely on pre-defined data. With GPU acceleration support and optional language grounding for human-readable output, LMD can function efficiently and flexibly in various applications. Furthermore, the emphasis on biological inspiration presents a potentially transformative perspective on memory and creativity in machine learning, setting the stage for future research and development in AI storytelling, ideation, and conceptual blending.