Written by Nure Zannat
The 2024 Nobel Prize in Physics has been awarded to John J. Hopfield and Geoffrey E. Hinton for their groundbreaking work in artificial neural networks (RNA) and machine learning [1], a technology that has become synonymous with advances in artificial intelligence (AI) and the science of computation[2]. This may seem like a more appropriate award for computer science than physics, and that was the widely given critique for this choice. However, the decision to pay homage to his work in physics reflects the deep connection between his discoveries and fundamental concepts of the physical sciences. That is why their contribution is recognized as a breakthrough in physics.
To understand why this work is recognized in the field of physics. First, we need to delve into the nature of neural networks and their relationship to physical systems. This is because neural networks, according to Hopfield and Hinton’s model, are interconnected body systems. Similar to neurons in the brain, they can “learn” by adjusting the strength of their connections (weights), but the basic principles are deeply rooted in physics. [3] Theoretical physicist John J. Hopfield was one of the first to create a link between neural networks and physics, as his 1982 model of associative memory used ideas from statistical physics, especially the model of magnetic rotation, to explain how groups of neurons can gather and retrieve information together. The Hopfield model mimics how the magnetic spins of a material move based on their interactions with neighboring spins by using an existing configuration that minimizes system power In the same way in artificial neural networks. The system is developed for states that represent memory or learning styles. This comparison to a physical system is not coincidental. In the Hopfield model Network dynamics are controlled by energy minimization. [4] This is a concept that every physicist is familiar with. Its status in the network corresponds to its “value” in the energy landscape. It is a core concept in both thermodynamics and statistical mechanics. Using physical principles, Hopfield showed that neural networks can perform complex calculations such as error correction and pattern recognition. In this sense, neural networks are more than just computation. It is a physical system whose dynamics are similar to magnetic materials or other aggregate phenomena of nature.
Geoffrey Hinton’s involvement strengthened the relationship between neural networks and physics, especially through the development of the Boltzmann machine in the 1980s. Named in honor of physicist Ludwig Boltzmann, the model is a recurrent neural network inspired by the principles of thermodynamics. A Boltzmann machine works by determining the probability given to various configurations (or states) of the network according to the Boltzmann distribution, a basic concept of statistical mechanics that describes the distribution of energy states in a system at thermal equilibrium. Hinton’s work with Boltzmann machines provides a direct physical idea for the realm of computation. In Boltzmann machines, networks learn by adjusting weights in such a way that the probability distribution of states is consistent with the distribution of patterns that we received in all aspects of training. [5] This process reflects the distribution of gas particles or solid atoms between different energy states, according to interaction and temperature. By connecting these principles, Hinton was able to effectively bridge the gap between physics and machine learning, showing that computational processes in artificial neural networks can be described using the same probabilistic and thermodynamic concepts that govern physical systems.
The Nobel Prize committee recognized Hopfield’s and Hinton’s work as a contribution to physics because their discoveries do not only include the concept of algorithms or computational problem-solving, but also is rooted in the study of complex physical systems. and how to use these systems in calculations. Their work explores the deep connections between statistical mechanics, energy landscape, and the dynamics of the nervous system. It shows how these physical laws can guide smart systems projects. Their research has also shown that neural networks can be used to model and solve problems in the physical world. For example, RNA has been used successfully to simulate many quantum systems. part Predict the properties of matter and analyze data from high-energy physics experiments, such as at CERN, by training neural networks to estimate complex quantum states or to recognize particle signatures. Physicists will be able to make important advances in areas that would otherwise be computationally prohibitive without these models.
Although the theoretical underpinnings of Hopfield and Hinton’s work are firmly rooted in physics, the impact of their discovery extends far beyond the lab. Today, RNA and deep learning are ubiquitous in modern life. It has potential technologies such as image recognition, natural language processing, and automation. The 2024 Nobel Prize in Physics celebrates this special day. From exploring physical systems to creating powerful new ferments that are transforming industry, science and society, in awarding the Nobel Prize in Physics to John J. Hopfield and Geoffrey E. Hinton, the Nobel Committee recognized the profound and fundamental connection between physics and neural networks. His work is an example of how physical principles can lead to transformative advances in fields such as computers, biology, and artificial intelligence. Although his model is widely used in the field of computer science, the fundamental insights behind these models are rooted in our understanding of the physical world and also the continuous convergence of disciplines in pursuit of deeper understanding and practical innovation. With this awareness, we are reminded that the boundary between physics and computer science, and that biology is not fixed, but it is constantly being redesigned with new discoveries that crosses traditional research lines, when Hopfield and Hinton’s neural networks became necessary not just for calculations but also for understanding the complex systems that make up our universe.
References:
- Lakra, Sachin, et al. “The Future of Neural Networks.” arXiv.org, 20 Sept. 2012, arxiv.org/abs/1209.4855#.
- Advanced information. NobelPrize.org. Nobel Prize Outreach AB 2024. Thu. 31 Oct 2024. <https://www.nobelprize.org/prizes/physics/2024/advanced-information/>
- “Spining, M. T., et al. “Opening up the Black Box of Artificial Neural Networks.” Journal of Chemical Education, vol. 71, no. 5, May 1994, p. 406. https://doi.org/10.1021/ed071p406.
- Dmitrienko, V. D., and A. Yu. Zakovorotniy. “ARCHITECTURE AND ALGORITHMS OF NEURAL NETWORKS HAMMING AND HEBB, CAPABLE LEARN AND IDENTIFY NEW INFORMATION.” Radio Electronics Computer Science Control, vol. 0, no. 2, Oct. 2014, https://doi.org/10.15588/1607-3274-2014-2-15
- Sirignano, Justin, and Konstantinos Spiliopoulos. “Mean Field Analysis of Neural Networks: A Law of Large Numbers.” SIAM Journal on Applied Mathematics, vol. 80, no. 2, Jan. 2020, pp. 725–52. https://doi.org/10.1137/18m1192184
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