The 2024 Nobel Prize in Physics was awarded to U.S. scientist John J. Hopfield and Canadian scientist Geoffrey E. Hinton for their pioneering research in the field of artificial neural networks and machine learning.
The Revelation of Artificial Neural Networks: Calculations that Mimic how the Brain Works
Artificial Neural Networks (ANNs) originated from the study of the human brain and attempt to mimic the way neurons interact with each other. This technology is designed as a learning tool that can be trained on data to perform complex tasks, such as image recognition and language translation. One of the early cornerstones of the field was the “Hopfield Network” proposed by Hopfield in the 1980s. What makes this model unique is that it can reconstruct the original image or pattern when the input data is incomplete, providing a new way to solve the uncertainty in reality.
Hinton is known as the “Godfather of AI” and his research focuses on deep learning. He and his colleagues developed the Boltzmann Machine, a stochastic regeneration neural network able to learn complex probability distributions and automatically extracts features during unsupervised learning. In addition, Hinton has promoted backpropagation, a core method for training multi-layer neural networks, which greatly improves the performance of deep learning models.
Physics and AI Intertwined
Although Hopfield and Hinton’s research focuses primarily on the field of AI, its core concepts and methods are grounded in physics. Hopfield networks utilize concepts from statistical mechanics, such as energy functions and state transitions, to describe the dynamic behavior of neurons. Hinton’s Boltzmann machine, on the other hand, is named directly after the physicist Ludwig Boltzmann. The model uses the Boltzmann distribution to describe the probabilistic state of the system. These studies show the powerful role of physical methods in understanding and modeling complex systems.
Modern Applications of Artificial Intelligence
These groundbreaking studies not only deepen our understanding of the human brain, but also drive the widespread application of AI technologies in real life. For example, image recognition technology based on deep learning is being in medical diagnosis to help doctors detect diseases faster. Speech recognition and translation tools improve cross-language communication and make human-machine interaction more convenient.
Following the rapid development of AI technology, Hopfield and Hinton’s findings will continue to influence future technological advancement. In the future, as deep learning and AI technologies continue to be developed, we can expect to see more innovative applications based on these theories to further improve human life.
Source:
Radio France Internationale (October 8, 2024), 2024 Nobel Prize in Physics Reveals Mystery at the Heart of Artificial Intelligence, retrieved from https://rfi.my/B1Xu
The Storm Media (October 8, 2024), The Nobel Prize in Physics Pays Tribute to Artificial Intelligence: Hopfield and Hinton’s Pioneering Research, retrieved from https://www.storm.mg/article/5251154
The News Lens (October 8, 2024) The Evolution of Artificial Intelligence: Revelations of the Nobel Prize in Physics, retrieved from https://www.thenewslens.com/article/242935
United Daily News (October 8, 2024) The Nobel Prize in Physics is Announced: Hopfield and Hinton Recognized for Research on Artificial Intelligence, retrieved from https://udn.com/news/story/123769/8281042
The Nobel Prize in Physics 2024 (October 8, 2024) Official website of the Nobel Prize in Physics. https://www.nobelprize.org/prizes/physics/2024/press-release/
Science Media Center Taiwan (October 8, 2024), Translation of the full text of the official press release on the 2024 Nobel Prize in Physics. Science Media Center Taiwan. https://smctw.tw/17682/
