Sydney researchers uncover a hidden limit to AI predictability

University of Sydney researchers Pulin Gong and Andrew Ly found in Nature Machine Intelligence that neural networks' training outcomes depend on fractal 'riddled basins' of starting conditions, so even deterministic training with identical data can yield different behaviours. In one larger image-recognition network, making starting settings 10 billion times more precise produced no measurable improvement in reproducibility, and a one-bit change sent training to a different configuration in about 48 percent of cases. The work, based on roughly 500,000 trained networks, also found a trade-off in which settings that learn most effectively make outcomes harder to reproduce.