- The 2024 Nobel Prize in Physics was awarded to John J. Hopfield and Geoffrey E. Hinton for their foundational contributions to artificial neural networks (ANNs) and machine learning, technologies that have revolutionised various fields of science and technology.
- Artificial Neural Networks (ANNs): ANNs are networks of interconnected neurons (or processing units) designed to mimic the brain’s function. Each neuron processes information and passes it along to others, forming a network capable of learning and performing complex tasks.
- Hopfield Network:
- Introduced by John J. Hopfield in 1982, a recurrent neural network allows information to flow bidirectionally between neurons.
- Based on Hebbian learning, the Hopfield network can solve problems like image denoising by minimising system energy, analogous to reducing magnetic energy in physical systems.
- Boltzmann Machines:
- Geoffrey Hinton expanded on Hopfield’s work with the Boltzmann machine, which models a network where neurons minimise energy in a manner similar to spin glasses in physics.
- These systems can classify data and generate new patterns by reducing the value of an energy function.
- Hebbian Learning: A concept from neuropsychology, Hebbian learning underpins the functioning of ANNs by strengthening connections between neurons that frequently activate together.
Dig Deeper: Read about John Hopfield’s Associative Memory.
