The actual process of computation, sure, but machine learning was born from physics-based methods and applications to understand complexity and disorder.
Parisi won in 2021, not last year. His work was more about establishing spin glasses as a way to study complex systems. Hopfield definitely built on that, showing how those ideas could be applied to neural networks and info storage in state-space machines.
As for focusing on Hopfield networks and Boltzmann machines, I get where you're coming from. They’re just a couple of architectures among many, but they’re pretty foundational. They’re deeply rooted in statistical mechanics and have had a huge impact, finding applications across a range of fields beyond just machine learning.