The Nobel Prize in Physics 2024
The Nobel Prize in Physics 2024 was awarded to John J. Hopfield and Geoffrey E. Hinton "for foundational discoveries and inventions that enable machine learning with artificial neural networks"
They used physics to find patterns in information: Many people have experienced how computers can translate between languages, interpret images and even conduct reasonable conversations. What is perhaps less well known is that this type of technology has long been important for research, including the sorting and analysis of vast amounts of data. The development of machine learning has exploded over the past fifteen to twenty years and utilises a structure called an artificial neural network. Nowadays, when we talk about artificial intelligence, this is often the type of technology we mean.
Although computers cannot think, machines can now mimic functions such as memory and learning. This year’s laureates in physics have helped make this possible. Using fundamental concepts and methods from physics, they have developed technologies that use structures in networks to process information.
Machine learning differs from traditional software, which works like a type of recipe. The software receives data, which is processed according to a clear description and produces the results, much like when someone collects ingredients and processes them by following a recipe, producing a cake. Instead of this, in machine learning the computer learns by example, enabling it to tackle problems that are too vague and complicated to be managed by step by step instructions. One example is interpreting a picture to identify the objects in it.
Mimics the brain
An artificial neural network processes information using the entire network structure. The inspiration initially came from the desire to understand how the brain works. In the 1940s, researchers had started to reason around the mathematics that underlies the brain’s network of neurons and synapses. Another piece of the puzzle came from psychology, thanks to neuroscientist Donald Hebb’s hypothesis about how learning occurs because connections between neurons are reinforced when they work together.
© Johan Jarnestad/The Royal Swedish Academy of Sciences
The network saves images in a landscape
The network that Hopfield built has nodes that are all joined together via connections of different strengths. Each node can store an individual value – in Hopfield’s first work this could either be 0 or 1, like the pixels in a black and white picture.
© Johan Jarnestad/The Royal Swedish Academy of Sciences
Ref: https://www.nobelprize.org/prizes/physics/2024/popular-information/