**Challenges in Cellular Automata Systems and AI Solutions** Main Challenge: Grid Topology Prediction - Predicting behavior in CA systems without knowing the grid structure. Value of AI Solutions: - Advance AI models for applications in bioinspired materials and simulations. Previous Approaches: - CNNs used for spatial data processing but limited by topology dependency. Practical Solutions: - Develop a topology-agnostic model like LifeGPT for better generalization. Introducing LifeGPT Model: - Topology-Agnostic Deployment predicting CA dynamics without grid knowledge. Key Innovations: - Rotary positional embedding and forgetful causal masking for enhanced generalization. LifeGPT Model Details: - Transformer Architecture with 12 layers and 8 attention heads for complex state transitions. Training Process: - Utilizes stochastic ICs and NGSs on a 32×32 grid with Adam optimizer and cross-entropy loss. Performance and Accuracy: - LifeGPT achieves over 99.9% accuracy in predicting CA dynamics after 20 epochs. Generalization Capability: - Maintains strong accuracy across various IC configurations for simulating complex systems. Conclusion: - Topology-agnostic approach with transformer models enables accurate predictions of CA dynamics. Future Applications: - Potential in bioinspired materials, system simulations, and universal computation in AI frameworks.
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