The use of artificial intelligence (AI) is already becoming commonplace in physics, but could the field also help AI? The answer is yes, according to a recent survey by the Institute of Physics.
Particle physicists have long been aware of AI’s potential to accelerate data analysis and discovery. In the early 2000s, a neural network developed by Michael Feindt was used to identify particles in large datasets at the Collider Detector at Fermilab (CDF). This work laid the foundation for many future breakthroughs in particle physics.
Today, AI is being used across various fields of physics, from gravitational lensing to material science. For example, researchers have used machine learning algorithms to analyze large datasets and identify patterns that would be difficult or impossible to see by hand. AI has also been used to optimize complex experiments and simulate physical systems.
But AI’s role in physics goes beyond just data analysis. The field is also exploring ways to integrate AI into the scientific process itself, using techniques such as Bayesian neural networks and physics-informed neural networks. These approaches can help improve the accuracy and reliability of AI-driven results.
The Institute of Physics has recently published a “pathfinder” study on the relationship between physics and AI, which highlights the potential benefits and challenges of this emerging field. The study reveals that two-thirds of physicists have used AI to some degree, and most are familiar with machine learning approaches or generative AI.
However, caution is needed when working with AI in physics. Many respondents to the survey pointed out that relying solely on AI can lead to distorted outcomes due to biased training data or inadequate understanding of algorithmic complexities. To mitigate these risks, physicists are developing new methods to quantify uncertainty and improve trust in AI-driven results.
Ultimately, the relationship between physics and AI holds great promise for advancing our understanding of the universe and driving scientific innovation. By combining the rigor of physics with the power of machine learning, researchers can unlock new discoveries and push the boundaries of human knowledge.
Source: https://physicsworld.com/a/how-ai-can-help-and-hopefully-not-hinder-physics