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.

The study by Professor Pulin Gong from the School of Physics and his Ph.D. student Andrew Ly has been published in Nature Machine Intelligence.

As AI systems become increasingly embedded in health care, business, research and everyday life, ensuring they behave consistently and predictably has become a major challenge. The new research suggests that some forms of AI unpredictability are not due to engineering flaws or inconsistencies in computer hardware, but arise from the underlying mathematics of how neural networks learn.