- The Nobel Prize for Physics has been awarded for advancements in AI
The 2024 Nobel Prize in Physics has been awarded to two scientists for developing the methods that are the foundations for today’s powerful AI.
John Hopfield and Geoffrey Hinton received the prestigious award for ‘foundational discoveries and inventions that enable machine learning with artificial neural networks.’
John Hopfield, of Princeton University, invented the methods which allow AI to save and recreate patterns.
Geoffrey Hinton, of the University of Toronto, gave AI the ability to find properties in data allowing it to complete tasks like recognising elements in specific pictures.
These discoveries allowed for the creation of the artificial neural networks which power modern chatbots such as ChatGPT.
The 2024 Nobel Prize in Physics has been awarded to John Hopfield and Geoffrey Hinton for developing the methods that are the foundations for today’s powerful AI.
Most modern artificial intelligences are based on a technology called artificial neural networks which mimic the connections between neurons in the brain.
In the AI, the neurons are represented by nodes which influence each other through connections which can be made weaker or stronger – allowing AIs to learn over time.
Without this technology, the powerful systems which run everything from ChatGPT to Apple Intelligence would not be possible.
This year’s Nobel laureates were both instrumental in laying the foundations for these important advances from the 1980s onward,
John Hopfield was responsible for inventing a system called the ‘Hopfield Network’ which allows AI to save and recreate patterns.
With his background in physics, Hopfield attempted to understand how the individual neurons of the brain work together to create interesting new abilities.
Today’s AI’s use a system called Artifical Neural Networks which would not be possible without the work of Hopfield and Hinton
He imagined that the brain worked together like a network of nodes which were joined by connections of different strengths.
Hopfield found that he could program these networks by feeding them an image and following a rule which adjusts the connections between the nodes to give the image ‘low energy’.
You can think about this like shaping a landscape of peaks and troughs – when the network is fed an image it creates a valley in a virtual landscape where the bottom of the valley has the lowest energy.
If you dropped a ball into this landscape it would keep rolling downwards towards lower energy until it was surrounded by uphills on all sides.
In just the same way, the Hopfield network is able to find the closest matching pattern to a distorted or even partially deleted input.
