Understanding the Machine Learning Black Box
From self-driving cars to facial recognition technologies, machine learning—an advanced form of artificial intelligence (AI)—is transforming modern life. Despite its widespread use, the inner workings of machine learning models often remain mysterious, earning them the nickname “black boxes.” But a physicist at the University of Utah is working to change that.
Zhengkang (Kevin) Zhang, an assistant professor in the Department of Physics & Astronomy, is applying the analytical tools of theoretical particle physics to better understand how machine learning algorithms operate. His recent study sheds light on the mechanisms behind these powerful models, potentially paving the way for more transparent and efficient AI systems.
From Physics to Artificial Intelligence
As a theoretical physicist, Zhang is accustomed to studying the universe’s smallest components to explain how matter behaves on a fundamental level. Now, he’s using that same methodology to decode the complex mathematical structures of machine learning models. “People used to say machine learning is a black box—you input a lot of data and at some point, it reasons and speaks and makes decisions like humans do,” said Zhang. “It feels like magic because we don’t really know how it works.”
Given the growing reliance on machine learning in healthcare, finance, and national security, Zhang emphasizes the need for a deeper understanding of these models. “We have to understand what our machine learning models are really doing—why something works or why something doesn’t,” he said.
Scaling Intelligence Efficiently
Traditional computer programming involves writing detailed, step-by-step instructions for every possible scenario. For instance, detecting irregularities in a CT scan would require lines of code for numerous cases. In contrast, machine learning relies on feeding large datasets into an algorithm, which then identifies patterns or predictions autonomously. Despite its efficiency, this process is energy-intensive and costly.
To mitigate these costs, many companies train machine learning models on smaller datasets before scaling up to real-world applications. Zhang is particularly interested in understanding how performance scales when the model or dataset size increases. “We want to be able to predict how much better the model will do at scale. If you double the size of the model or dataset, does the model become two times better? Four times better?” he asked.
Applying a Physicist’s Toolkit
A machine learning model can be simplified into a process: Input data → black box → output. The black box consists of a neural network, a web of basic computations that combine to form complex functions. Traditionally, optimizing these networks has involved a lot of trial and error, which increases time and costs.
“Being trained as a physicist, I would like to understand better what is really going on to avoid relying on trial and error,” said Zhang. “What are the properties of a machine learning model that give it the capability to learn to do things we wanted it to do?”
In his latest study, Zhang used Feynman diagrams—a tool invented by Nobel laureate Richard Feynman in the 1940s to simplify complex quantum calculations. These diagrams represent interactions visually, making it easier to understand and manage the vast number of terms involved in the equations.
Breaking New Ground in AI Research
Published in the journal Machine Learning: Science and Technology, Zhang’s paper explores scaling laws that describe how a model’s performance changes with size. While earlier researchers examined their model within a limited scope, Zhang extended the analysis beyond those boundaries. By applying Feynman diagrams, he was able to derive more accurate and comprehensive scaling laws.
“It’s so much easier for our brains to grasp, and also easier to keep track of what kind of terms enter your calculation,” Zhang explained. His work not only helps demystify the behavior of machine learning models but also offers a more cost-effective method for improving their performance.
The Human Dimension of AI
As artificial intelligence becomes more integrated into daily life, concerns about its societal impact continue to grow. Zhang believes that physicists, with their analytical rigor and problem-solving mindset, have a valuable role to play in ensuring AI technologies are used responsibly.
“We humans are building machines that are already controlling us—YouTube algorithms that recommend videos and influence our behavior,” Zhang noted. “That’s the danger of how AI is going to change humanity—it’s not about robots colonizing and enslaving humans. It’s that we humans build machines that we are struggling to understand, and our lives are already deeply influenced by these machines.”
By bringing the precision and critical thinking of physics into the realm of AI, Zhang is helping to illuminate the black box of machine learning—making it a little less mysterious and a lot more human-understandable.
This article is inspired by content from Original Source. It has been rephrased for originality. Images are credited to the original source.