Error Simulation

Error simulation research focuses on developing and applying methods to introduce and analyze errors in various computational models and systems, aiming to improve robustness and reliability. Current research emphasizes the use of Bayesian neural networks, large language models, and other deep learning architectures to generate and analyze both aleatoric and epistemic uncertainties, as well as to create synthetic errors mimicking human mistakes for training purposes. This work is crucial for enhancing the trustworthiness of AI systems across diverse applications, from medical image analysis and grammatical error correction to the reliable deployment of AI in safety-critical contexts. Improved error simulation techniques ultimately lead to more robust and reliable AI systems.