• Modern AI, especially neural networks, has roots in the physics of spin glasses, complex materials studied in the mid-20th century.
• John Hopfield's adaptation of spin glass physics to neural networks in 1982 enabled the creation of learning and memory systems in machines.
• The principles of spin glasses are now being explored to design more understandable neural networks and even to create generative AI models.

The article unveils the unexpected origin story of modern artificial intelligence, tracing its roots back to the obscure realm of spin glass physics. In the mid-20th century, physicists grappled with the puzzling behaviors of spin glasses, metallic alloys with peculiar magnetic properties. It was John Hopfield, a condensed matter physicist, who recognized the potential of spin glass theories to model memory and learning in neural networks. This connection sparked a revolution in AI research, leading to the development of advanced neural networks and generative AI models we see today.

• What: The application of spin glass physics, particularly the Ising model and its energy landscape concept, to create neural networks capable of learning and recalling memories.
• Who: Key individuals include John Hopfield, Geoffrey Hinton, David Sherrington, Scott Kirkpatrick, Lenka Zdeborová, Dmitry Krotov, and Philip Anderson. Key organizations include Caltech, Bocconi University, Swiss Federal Institute of Technology Lausanne, IBM Research.
• When: The key period spans from the mid-20th century (spin glass research) to the 1980s (Hopfield's neural networks) to the present day (modern AI models and their connection to Hopfield networks).
• Where: The research and developments occurred across various academic and research institutions globally.

The article provides a compelling narrative of how an seemingly unrelated area of physics, spin glass research, played a crucial role in the advancement of artificial intelligence. The significance lies in the conceptual breakthrough of applying the energy landscape model from spin glasses to neural networks, enabling machines to 'remember' by navigating towards low-energy states. The development of Boltzmann machines and deep learning architectures further built upon these principles. The resurgence of Hopfield networks in modern AI models suggests that the connection between physics and AI remains relevant and potentially transformative.

Hopfield made the connection and said, ‘Look, if we can adapt, tune the exchange couplings in a spin glass, maybe we can shape the equilibrium points so that they can become memories,’

How mind emerges from brain is to me the deepest question posed by our humanity. Definitely a PROBLEM.

Mathematically, one can replace what were the spins or atoms. Other systems can be described using the same toolbox.

The journey from spin glass physics to modern AI highlights the power of interdisciplinary thinking and the unexpected connections between seemingly disparate fields. Hopfield's pioneering work laid the foundation for neural networks that learn and remember, and these principles continue to influence the development of advanced AI models. As researchers explore the potential of Hopfield networks to create and understand AI, the legacy of spin glass physics in shaping our intelligent machines is poised to endure.