The repo includes a 13-chapter thesis (PDF and sources), proofs, and tools for exploration. It’s aimed at formal methods enthusiasts, AI researchers, and hardware devs interested in verifiable, adaptive reasoning beyond traditional limits. Feedback welcome on the proofs, emergence chapter, or hardware impl, let’s collaborate!
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!
hayley-patton•3w ago
No, I don't.
nwthiele•3w ago