MIT developed a way for a deep learning neural network to rapidly estimate confidence levels in their output. The advance could enhance safety and efficiency in AI-assisted decision making. Credits: Credit: iStock image edited by MIT News
Increasingly, artificial intelligence systems known as deep learning neural networks are used to inform decisions vital to human health and safety, such as in autonomous driving or medical diagnosis. These networks are good at recognizing patterns in large, complex datasets to aid in decision-making. But how do we know they’re correct? Alexander Amini and his colleagues at MIT and Harvard University wanted to find out.
