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Fractal Flux AGI: A New Approach to Recursive Learning
- Artificial General Intelligence (AGI) aims to create dynamically adaptive systems that evolve recursively without relying on extensive training data or predefined architectures.
- The Fractal Flux AGI prototype explores recursive learning, fractal-driven decision-making, and time-spiral cognition to model intelligence as a constantly evolving structure.
- Five core principles of the Fractal Flux AGI model include recursive learning, fractal feedback loops, bootstrap adaptation, chaos regulation, and time-spiral evolution.
- A Python implementation of the Fractal Flux AGI model is provided to simulate knowledge evolution, fractal complexity, and memory retention over multiple time steps.
- The model's recursive learning loop updates itself based on past and predicted future states, promoting dynamic knowledge refinement without external datasets.
- Fractal flux function introduces self-similar complexity to ensure structured adaptation, chaos regulation for stability, and time-spiral evolution for continuous learning cycles.
- The prototype demonstrates self-improving intelligence, fractal-driven adaptation, long-term memory stability, and nonlinear knowledge evolution.
- Applications of the Fractal Flux AGI model include multi-agent learning, decision-making systems, cognitive modeling, and AI alignment.
- Future developments could integrate multi-agent interactions, refine chaos regulation mechanisms, and compare performance with traditional AI models.
- The Fractal Flux AGI model represents a step towards exploring self-referential intelligence structures that go beyond traditional training paradigms in the pursuit of AGI.
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