Brief news summary

Researchers at MIT have developed a method, called IF-COMP, to enhance uncertainty estimates in machine-learning models, which can improve prediction accuracy. The scalable approach applies the minimum description length principle to provide more reliable confidence measures for AI decisions. IF-COMP can be used on large models, making it user-friendly and allowing non-experts to determine the trustworthiness of AI predictions. The findings have significant implications for high-stakes sectors such as healthcare, where precise predictions are crucial. The technique could result in better decision-making and improved real-world applications.