Why Artificial Intelligence Should Be Subclassed as a Part of Physics
Introduction
In recent years, artificial intelligence (AI) has rapidly evolved from a niche field within computer science to a transformative force impacting almost every aspect of human life. Its applications range from medical diagnostics to self-driving cars, from climate modeling to natural language processing. Despite its vast influence, AI is traditionally viewed as a subset of computer science, often disconnected from the core scientific disciplines like physics. However, a closer examination reveals that AI's conceptual foundations, practical advancements, and future potential are deeply intertwined with the fundamental principles of physics. This essay argues that artificial intelligence should be subclassed as a part of physics due to its reliance on physical systems, the parallels between physical and computational models, and the emerging role of AI in solving physics-related problems.
The Physical Nature of Computing
At its most fundamental level, all computation—including the processes that power AI—is a physical phenomenon. Computers, from the early days of mechanical calculators to today’s quantum processors, operate based on the manipulation of physical systems. The foundational building blocks of AI systems, such as transistors, logic gates, and electrical circuits, all depend on physical principles like electromagnetism, quantum mechanics, and thermodynamics.
The early development of computing, which paved the way for AI, was deeply rooted in the laws of physics. The invention of the transistor in the late 1940s, a direct product of quantum mechanics, marked the beginning of modern electronics and laid the groundwork for the rapid advances in computational power we see today. As transistors have continued to shrink, approaching the scale where quantum effects dominate, the field of computer science has had to contend more directly with the fundamental laws of physics. This convergence becomes even more pronounced when we consider the emerging field of quantum computing, where AI algorithms are being designed to run on machines that operate according to the principles of quantum mechanics.
Physics as the Inspiration for AI Models
AI has not only relied on physical systems for its computational power but has also drawn inspiration from physical models in its conceptual development. Many AI algorithms, particularly those used in machine learning, borrow heavily from techniques used in statistical mechanics—a branch of physics concerned with predicting the behavior of systems with many interacting components.
For example, neural networks, one of the most powerful and widely used AI models today, share conceptual similarities with models used in statistical physics to describe the behavior of large, interconnected systems. The famous Hopfield network, named after physicist John Hopfield (recently awarded the 2024 Nobel Prize in Physics alongside Geoffrey Hinton), is a prime example. This network was inspired by Ising models, which physicists use to study the alignment of spins in magnetic systems. Hopfield's work directly borrowed from statistical physics to design a model for associative memory, a foundational concept in neural networks.
Beyond this, techniques like simulated annealing, a method used in AI for optimization problems, are based on the physical process of annealing in metallurgy, where materials are slowly cooled to remove defects. This algorithm mimics the process of a metal cooling and settling into a state of minimum energy, highlighting how AI often finds inspiration in physical systems to solve complex computational problems.
The Role of AI in Solving Physics Problems
AI is not just borrowing ideas from physics; it is now being used to tackle some of the most complex problems in the field of physics itself. AI models have become invaluable tools in fields like particle physics, cosmology, and quantum mechanics, where traditional computational methods struggle to handle the complexity and scale of the data involved.
In particle physics, for example, AI is being used to analyze the enormous datasets generated by experiments like those at the Large Hadron Collider (LHC). These experiments produce vast amounts of data as they probe the fundamental particles that make up the universe, and AI algorithms are uniquely suited to identify patterns within this data, helping physicists detect new particles and phenomena that would be impossible to find manually.
AI is also being applied in quantum mechanics, where traditional methods for solving quantum systems often face insurmountable computational challenges. Quantum machine learning, a field that combines quantum computing and AI, holds the potential to solve problems in quantum chemistry, material science, and cryptography that are currently beyond the reach of classical computers. The algorithms used in quantum machine learning are rooted in both the principles of quantum mechanics and AI, further blurring the lines between the two fields.
In astrophysics, AI has been employed to sift through data from telescopes, helping to identify exoplanets, detect gravitational waves, and model the behavior of galaxies. These tasks are computationally intensive and rely on the physical properties of the universe to make predictions and classifications. Here again, AI serves as a tool that is fundamentally grounded in physics, not just in its operation but in the nature of the problems it is designed to solve.
AI as a Natural Extension of Physics
The relationship between physics and AI is not limited to shared models or the application of AI in physical research. At a more philosophical level, AI can be viewed as a natural extension of the goals of physics. Physics seeks to understand and describe the fundamental laws that govern the behavior of the universe. In a sense, AI attempts to do something similar: it seeks to model and predict complex systems, from natural phenomena to human behavior, using data and mathematical principles.
As AI becomes more integrated with physical systems, particularly with the advent of quantum computing, it is likely that we will see an even greater convergence between the two fields. Quantum computing itself is a perfect example of this fusion. By exploiting the principles of superposition and entanglement, quantum computers operate in a way that is fundamentally different from classical machines, and AI algorithms will need to be adapted to take full advantage of these capabilities.
Moreover, as AI systems become more sophisticated and autonomous, they begin to resemble complex adaptive systems, which are a focus of study in physics. These systems, like ecosystems or the climate, exhibit emergent behavior that cannot be predicted from the properties of individual components. In this sense, AI systems can be seen as new types of physical systems, whose behavior emerges from the interactions of their underlying parts—whether these parts are transistors, neurons, or qubits.
Conclusion
Artificial intelligence, while traditionally viewed as a subset of computer science, is deeply rooted in the principles of physics. From the physical nature of computation to the inspiration AI draws from statistical mechanics and optimization techniques, and the role AI now plays in solving complex physical problems, it is clear that AI and physics are inextricably linked. As AI continues to evolve, particularly with the development of quantum computing, this connection will only grow stronger.
Subclassing AI as a part of physics would not only reflect the reality of these deep connections but also foster greater interdisciplinary collaboration, encouraging advancements in both fields. AI has already proven itself to be a powerful tool in the physical sciences, and as we continue to push the boundaries of what is possible, the relationship between AI and physics will be critical to our understanding of both the universe and intelligence itself. By acknowledging AI as part of the broader field of physics, we honor the profound ways in which these two domains influence and enhance each other.