卡内基·梅隆研究人员确定了计算生物学在AI可解释性方面的挑战,并建议采用各种方法。 Carnegie Mellon researchers identify challenges in AI interpretability for computational biology and suggest using diverse methods.
卡内基·梅隆大学研究人员已经查明了AI可解释性方面的挑战,这对于理解计算生物学中的模型行为至关重要。 Carnegie Mellon University researchers have identified challenges in AI interpretability, crucial for understanding model behavior in computational biology. 他们建议使用多种可解释的机器学习方法, 具有多种超参数, 并警告不要挑选结果. They suggest using multiple interpretable machine learning methods with diverse hyperparameters and warn against cherry-picking results. 这些准则旨在改进在计算生物学中使用可解释的机器学习方法,有可能促进更广泛地利用AI进行科学影响。 These guidelines aim to improve the use of interpretable machine learning methods in computational biology, potentially facilitating broader use of AI for scientific impact.