Deep learning neural networks are artificial intelligence systems that are being used for increasingly important decisions. Deep learning neural networks are used for tasks as varied as autonomous driving to diagnosing medical conditions. This type of network excels at recognizing patterns in large and complex datasets to help with decision-making.
One big challenge is determining if the neural network is correct. Researchers at MIT and Harvard University have developed a quick way for a neural network to churn through data and provide a prediction along with the neural network’s confidence level in its answer. Researchers on the project believe that their system could save lives since deep learning is already deployed in the real world.
Currently, uncertainty estimation for neural networks tends to be computationally expensive and too slow for split-second decisions. The approach devised by the researchers is called “deep evidential regression” and speeds the process up, potentially leading to safer outcomes. Researchers on the project say we need the ability to have high-performance models and understand when results from the models can’t be trusted.
Continue reading… “MIT neural network knows when it can be trusted”
