2024 Nobel Prizes and the A3D3 Institute
This year’s Nobel Prizes in Physics and Chemistry recognize outstanding work in the context of artificial intelligence (AI). We would like to take this opportunity to describe our (and AI’s) view on how these Nobel Prizes are connected to the work we do at the A3D3 Institute.
What were the Nobel Prizes in Physics and Chemistry 2024 awarded for?
The 2024 Physics Nobel Prize was awarded to John J. Hopfield and Geoffrey E. Hinton for their foundational contributions to artificial neural networks, which drew inspiration from physics. In the network developed by Hopfield, each neuron is connected to all other neurons, and the system was optimized by minimizing its energy analogously to atomic spin models. Hinton developed the Boltzmann machine and, borrowing a concept from statistical physics, optimized the network until it reached a thermal equilibrium. In Chemistry, the AI-related part of the Nobel Prize went to Demis Hassabis and John M. Jumper for their work on AlphaFold2, a model that predicts protein structures.
How is the A3D3 research related to the work awarded with the Nobel Prizes? What about the future?
As stated by artificial intelligence in the form of a large language model, “the [A3D3] institute’s work can be seen as both building upon the foundations laid by the Nobel laureates and pushing the boundaries of AI applications in science even further.” In A3D3, we aim to harness the AI-driven data revolution, which has been driven, among others, by the 2024 Nobel Prize laureates, to accelerate discoveries in fundamental research.
Both Nobel Prizes show the vital interplay between fundamental science and artificial intelligence: not only are artificial neural networks strongly inspired by concepts from physics and chemistry, but they are also used as tools to advance scientific knowledge. Physics concepts are still (and will likely be in the future) an inspiration for designing even better artificial neural networks. A recent example is models that learn the probability distribution utilizing normalizing flow techniques, with the concepts being developed in strong interplay with statistical physics. Within the A3D3 Institute, for example, we develop normalizing flow-based models to estimate localization and source parameters of gravitational wave events.
While Hopfield’s and Hinton’s work of connecting neurons in analogy to structures in the brain is strongly influenced by neuroscience, research in the A3D3 Institute is closing this circle now by using AI to help gain an understanding of the brain. To achieve this, we work on developing and testing AI algorithms for neural and behavioral data.
In the paper accompanying the 2024 Physics Award, the Nobel Committee outlines the role of artificial neural networks as tools for scientific discovery, explicitly mentioning many research areas to which the A3D3 Institute contributes. Examples include the discovery of the Higgs boson, a milestone in particle physics, and the use of AI to analyze data recorded with the IceCube detector, which led to a neutrino image of the Milky Way.
In A3D3, we further improve artificial neural networks for various scientific use cases, such as particle tracking or identification. Transformer models, like those used in AlphaFold2 to predict protein structures, are often the backbone of algorithms we construct, for example, for identifying if a jet produced in a particle collision originates from a heavy resonance.
Instantaneous reconstruction and data classification are multidisciplinary challenges, spanning from quick alerts for multi-messenger astronomy to triggers to filter particle collision data to be recorded at the LHC. As Michael Coughlin, A3D3 co-PI and coordinator for multi-messenger astronomy, states: “Machine learning in astrophysics is opening an era of time-domain astronomy where time is truly of the essence, as it is critical to discover and characterize sources that will otherwise disappear unstudied if not observed in real-time.” Therefore, research within the A3D3 Institute focuses on extending current AI methods to make real-time applications possible. With the ability to run reconstruction, detection, and data analysis in real-time, we aim to build a platform utilizing advances in machine learning and artificial intelligence for a new era of discovery and precision measurements.
By: Christina Reissel
October 24th, 2024
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