To clarify the “beyond Turing” claim without fluff—it’s not about hypercomputation magic, but introducing μ-bits as a constrained bit model that enforces physical realism (e.g., conservation laws via Noether’s theorem) in chaotic/emergent systems. This makes it “stricter” than TMs for certain real-world simulations, while still universal (proven in Coq, zero admits).
To clarify the “beyond Turing” claim without fluff—it’s not about hypercomputation magic, but introducing μ-bits as a constrained bit model that enforces physical realism (e.g., conservation laws via Noether’s theorem) in chaotic/emergent systems. This makes it “stricter” than TMs for certain real-world simulations, while still universal (proven in Coq, zero admits).
If you’re curious:
• Quick Python sim to play with: https://github.com/sethirus/The-Thiele-Machine/blob/main/sim... (try running a simple chaotic iteration).
• Hardware angle: Verilog for FPGA prototyping—anyone with ASIC experience want to collab on optimizing for low-power emergent logic?
• Thesis highlights: Ch. 7 on emergence in physics/AI, or Ch. 10 on why this could matter for verifiable ML training under constraints.
What breaks it for you? Proof holes, sim perf, or just the physics tie-in? Open to PRs or discussions!