Mustafa Suleyman

TL;DR: A new methodology is defined that resolves this bottleneck and provides large scale supervised reading comprehension data that allows a class of attention based deep neural networks that learn to read real documents and answer complex questions with minimal prior knowledge of language structure to be developed.

TL;DR: The dataset is described, the statistics are described, how it was collected, and some baseline performance figures for neural network architectures trained and tested for human action classification on this dataset are given.

TL;DR: A novel deep learning architecture performs device-independent tissue segmentation of clinical 3D retinal images followed by separate diagnostic classification that meets or exceeds human expert clinical diagnoses of retinal disease.

TL;DR: A robust assessment of the AI system paves the way for clinical trials to improve the accuracy and efficiency of breast cancer screening and using a combination of AI and human inputs could help to improve screening efficiency.

TL;DR: The safe and timely translation of AI research into clinically validated and appropriately regulated systems that can benefit everyone is challenging, and robust clinical evaluation, using metrics that are intuitive to clinicians and ideally go beyond measures of technical accuracy, is essential.