MICHAELCARLTON

Dr. Michael Carlton
Physics-Inspired AI Architect | Neuromorphic Computing Pioneer | Natural Law Neural Designer

Professional Mission

As an innovator at the nexus of physics and artificial intelligence, I engineer nature-constrained neural architectures that transform conventional deep learning into physically-grounded intelligent systems—where every activation function mirrors thermodynamic laws, each network topology emerges from spacetime symmetries, and all learning dynamics obey energy-conservation principles. My work bridges theoretical physics, computational neuroscience, and next-generation AI hardware to redefine machine intelligence through the lens of universal physical laws.

Foundational Contributions (April 2, 2025 | Wednesday | 14:36 | Year of the Wood Snake | 5th Day, 3rd Lunar Month)

1. Physics-Constrained Architectures

Developed "PhysNet" framework featuring:

• Hamiltonian-inspired neural dynamics conserving computational energy

• Gauge-equivariant attention mechanisms respecting physical symmetries

• Entropy-regularized learning aligning with thermodynamic limits

2. Neuromorphic Hardware Co-Design

Created "NeuroCrystal" technology enabling:

• Spintronic neural components with 99% energy efficiency

• Topological quantum learning circuits

• Biophysical plausible spike propagation models

3. Theoretical Unification

Pioneered "The Noether Learning Theorem" proving:

• Every differentiable symmetry corresponds to a conserved learning invariant

• Bounds on information density in physical neural systems

• Quantum-classical hybrid learning convergence guarantees

Field Advancements

• Achieved 47x energy reduction in physics simulation AI

• Validated first thermodynamics-compliant LLM at Stanford Linear Lab

• Authored The Physics of Intelligence (Princeton Computational Physics Press)

Philosophy: True machine intelligence shouldn't just solve problems—it should resonate with the universe's deepest laws.

Proof of Concept

• For CERN: "Designed symmetry-preserving networks for particle collision analysis"

• For Quantum Startups: "Developed noise-utilizing neural architectures for NISQ devices"

• Provocation: "If your neural network violates conservation laws, you're not doing AI—you're doing mathematical alchemy"

On this fifth day of the third lunar month—when tradition honors natural harmony—we redefine computation as an extension of physical reality.