The story of neural networks
Artificial neural networks, the technology that drives modern AI, mimic the way human brains process information. These networks consist of nodes, or “neurons,” that connect and influence one another, much like synapses in our own brains. This network architecture allows AI to learn and improve over time, making it capable of tasks like recognising speech, translating languages, and even generating human-like text.The Nobel laureates were instrumental in building this foundation, each contributing uniquely to the development of neural networks. John Hopfield, a professor at Princeton University, introduced the
On the other side of the Atlantic, Geoffrey Hinton, a professor at the University of Toronto, was working on his own innovations. Building on Hopfield’s ideas, Hinton developed the
Why physics?
It may seem surprising that theThe Hopfield network, for example, is based on the physics of atomic spin — a property that makes each atom a tiny magnet. Hopfields work utilised this concept to help his network “remember” patterns by aligning the connections between nodes to reduce energy, allowing it to retrieve images from memory in a manner similar to how a magnet aligns with a magnetic field.
Hinton’s Boltzmann machine, meanwhile, leverages statistical physics to enable machines to learn. The system trains by running repeated examples through the network, gradually improving its accuracy at recognising specific features — much like how we recognise familiar faces in a crowd.
Transforming the world of AI
Hopfield and Hinton’s discoveries laid the groundwork for many of today’s most powerful AI tools, from language models like ChatGPT to image-recognition software used in medical diagnostics and autonomous vehicles. Without these early innovations, the AI systems that have become so integral to modern life would not exist.Their discoveries have far-reaching applications, from scientific research to the everyday technology we often take for granted. As Ellen Moons, Chair of the Nobel Committee for Physics, remarked, “The laureates’ work has already been of the greatest benefit. In physics, we use artificial neural networks in a vast range of areas, such as developing new materials with specific properties.”
The recognition of Hopfield and Hinton’s work is not only a nod to the past but a beacon pointing toward the future. As AI continues to evolve, the foundation they built will remain crucial, inspiring new generations of scientists and researchers to push the boundaries of technology. It’s a testament to the enduring impact of their work that we are only beginning to scratch the surface of what’s possible with AI.