By Ayathandwa Tsili When artificial intelligence (AI) recognises a face, reads a medical scan or makes decisions, it often does so in ways that even its creators say they cannot fully explain. But for Rhodes University MSc Applied Mathematics graduate Georgina Fiorentinos, that wasn’t good enough – so she set out to find out for herself. And she wanted more than a mathematical answer. She wanted to understand how machines interpret the world around them and how reliable those interpretations really are. Her MSc research explored the inner workings of deep learning models, the type of AI behind technologies such as image recognition systems and automated decision-making tools. While these systems are widely used, the processes inside them are often difficult to interpret. “I’ve always been interested in what is happening inside these models,” Fiorentinos explains. “We usually see the input and the output, but the actual process in between can feel like a black box. I wanted to understand how the model decides what information is important and what it ignores.” Her interest began during her Honours research, where she studied how compression techniques could reduce noise in deep convolutional neural networks, models commonly used to analyse images. That work led her to investigate how information changes as it moves through a neural network and whether important details are preserved along the way. Working within the Rhodes University Artificial Intelligence Research Group, and supervised by Professor Atemkeng, she focused on the relationship between ‘signal’ and ‘noise’ inside these systems. In simple terms, signal refers to useful information a model needs to make accurate predictions, while noise refers to irrelevant details that can interfere with those decisions. “An image might seem obvious to us, but to a computer it’s just numbers,” she says. “The model has to learn which patterns