Summary of AI and Physics Connection

 **Summary of AI and Physics Connection:**

Yann LeCun and Derrick Hodge highlight the deep interconnections between AI and physics. LeCun notes that the derivation of backpropagation from classical mechanics and optimal control was detailed in his 1987 PhD thesis and 1988 paper. Hodge elaborates on how various AI methods correspond to physical principles:

1. **Variational Bayesian Inference** relates to thermodynamics.

2. **Bayesian Ensembling** is analogous to path integrals.

3. **Backpropagation** can be derived via the Lagrangian saddle point method.

4. **Convergence in non-convex objectives** mirrors spontaneous symmetry breaking.

5. **ConvNet pooling** parallels renormalization group theory and MERA tensor networks.

6. **Lorentz-equivariant ConvNets**.

These connections suggest that AI and physics share foundational computational mechanisms, leading to unified theories, new research frontiers, and deeper understanding of both fields.