SciencePhys.orgFractal patterns reveal hidden limits to AI predictability
University of Sydney researchers have found a mathematical reason why AI systems trained on identical data can still behave differently, which could help make AI more reliable.
Researchers at the University of Sydney have identified a fundamental mathematical reason why artificial intelligence systems can learn different behaviors even when trained under almost identical conditions, a discovery that could help improve the reliability and trustworthiness of AI technologies.
University of Sydney researchers found that fractal patterns in the mathematics of neural network training can cause AI systems to learn different behaviours even under near-identical conditions.
The study, published in Nature Machine Intelligence, found that tiny differences in a model's starting parameters can lead to different outcomes even when training data and hardware are identical.
The findings could help engineers design more consistent and trustworthy AI, particularly in high-stakes fields like healthcare where unpredictable behaviour carries real risk.
The researchers say understanding this could guide the design of more dependable AI systems, including those used in healthcare like cancer detection tools.
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